The moisture trapped in the soil affects a lot more than the health of crops and trees. Look at natural--color satellite images and it becomes clear that most of the water on Earth (about 97 percent) is stored in the oceans. Next you might notice some on the land: liquid water fills lakes and rivers, while frozen water blankets the poles and mountaintops. In the atmosphere, water is visible in the countless tiny droplets that compose the clouds, though there is plenty of moisture even in cloud-free skies. Soil moisture has many expressions and influences in Earth's climate, from evaporation to freezing and thawing ice to droughts and floods. (Photos used under a Creative Commons license, courtesy of Guido Appenzeller (top left), TREEAID (bottom left), and Mike Rosenberg (top right). NASA Earth Observatory image (bottom right) by Joshua Stevens, using Landsat data from the U.S. Geological Survey.) Not immediately visible, however, is the water residing in the soil. This water does not appear brilliantly blue or white, like the oceans or ice. In fact, it is hard to spot in natural-color satellite images. Compared to the amount of water stored elsewhere on the planet, the amount in the soil is minuscule. But that small volume has great significance. It can affect when, where, and what a farmer will plant. It can influence the weather. And at high northern latitudes, soil moisture has serious implications for global climate. For all of these reasons, researchers have developed satellite instruments to measure the water hidden between soil particles. The instruments are either active or passive. Active radar sensors transmit microwave radiation toward the ground and measure the echoes. Depending on the moisture content, the reflected signal will "look" different---information scientists then use to derive the amount of soil moisture. (One such sensor flies on the Metop satellites operated by the European Organization for the Exploitation of Meteorological Satellites.) The radar approach allows scientists to measure moisture in very specific areas (high resolution), but it is less accurate than other approaches. "Passive radiometers detect microwave wavelengths of light that are naturally emitted by the soil. Because the signal varies with wetness, scientists can use the information to estimate the amount of moisture in the top few inches of soil. These measurements give better estimates of the amount of water, but over a broader area (coarse spatial resolution) than active radar. The European Space Agency has flown a passive radiometer on the Soil Moisture and Ocean Salinity (SMOS) mission since 2009, and NASA put them on the Aqua (2002) and Aquarius (2011) satellites. NASA's Soil Moisture Active Passive (SMAP) satellite was launched in January 2015 and carries both a radiometer and radar. However, the radar stopped transmitting data a few months after launch. The Soil Moisture Active Passive (SMAP) satellite can observe global soil moisture daily at a 36-kilometer resolution. (NASA Earth Observatory map by Joshua Stevens, using data courtesy of JPL and the SMAP science team.) All of these platforms, combined with insights from ground-based sensors, contribute to a growing record of global soil moisture. The goal is to establish a standardized set of measurements for the entire planet so that everyone from meteorologists to climate modelers can track the movement of this small but vital reservoir of water. The most obvious users of soil moisture data are farmers and ranchers. There's more to it, however, than the simple fact that plants need water to grow. Knowing something about the moisture in the soil is important before, during, and after the growing season. For example, will mud prevent a tractor from safely driving across the fields? How much water will fruits, nuts, and vegetables have available at each stage of growth, from germination through harvest? What is the forecast for crop yields around the world? How will the amount of moisture and agricultural output affect trade policy and food aid? Ground-based sensors can monitor soil moisture over small areas, typically less than one square meter. To find out what is happening over larger areas, researchers in several U.S. states have patched together a network of sensors. In Oklahoma, for example, a network to monitor weather and climate parameters (including soil moisture) was conceived after a disastrous flood struck Tulsa in 1984. Interest in this type of network for agricultural purposes also arose in Stillwater. The result was an environmental monitoring network called Mesonet, which in 1996 started to include soil moisture sensors. Today, more than 100 stations across Oklahoma are making measurements at various soil depths down to 60 centimeters (24 inches). Sensors record the temperature during and after a imparting a pulse of heat; the amount of water in the soil can then be inferred from the temperature change. (Other ground-based methods involve neutron scattering or soil coring.) Data from these sensors, updated every 30 minutes, can help farmers quickly figure out where there is inadequate moisture in their fields. Mesonet is just one of 31 networks and 1,479 stations in North America. But, in situ networks do not cover all areas of the United States, and certainly not the planet. Nearly 1,500 stations track soil moisture in locations across the United States. As an example, the plot shows data from the Coastal Sage UCI station (California). As the map shows, the network is relatively sparse for the size of the nation. (NASA Earth Observatory map and chart by Joshua Stevens, using data from the TAMU North American Soil Moisture Database.) To fill in the gaps, some scientists estimate global soil moisture by running computer models loaded with precipitation, temperature, and humidity data. Gathering all of the data to run such models can take as long as two to three months, which makes real-time applications impossible. "What we really want is soil moisture information that can be used to understand how plants are growing and what's going on in the atmosphere right now," said Susan Moran, a hydrologist with USDA's Agricultural Research Service and chair of the SMAP Applications Working Group. "We have to get soil moisture information to the agriculture community, and the only way to do that is from satellites." With the recent satellite missions, Moran and colleagues have been learning more about how soil moisture affects plant growth and agricultural productivity, especially during conditions of water shortage and drought. For instance, she notes there have been drier and longer droughts than the one currently parching the western U.S., but none that have been so hot. The combination of heat and the lack of water is driving soil moisture to unprecedented deficits. "Data from SMAP will make a giant difference for my work," she said. "We have already looked at five years of data from SMOS; add SMAP onto that and we begin to get a good time series of global soil moisture to help us figure out where vegetation has a high risk of mortality." Soil moisture has an obvious, visible effect on the landscape. The high-profile examples are droughts and floods. But the water in the soil has a more subtle, yet equally important role in day-to-day weather. Soil moisture forms a vast, thin, and mostly out-of-sight reservoir of water that accumulates in the root zone of plants. The water is released to the atmosphere through evaporation and plant transpiration. Averaged globally, this evapotranspiration contributes to more than 60 percent of the precipitation that falls over land each year. Today, satellites can measure soil moisture globally and quickly. Saturated soils in the map above---measured by SMAP on October 5, 2015---were the result of intense rains that caused flooding in the southeastern United States. (NASA Earth Observatory map by Joshua Stevens, using soil moisture data courtesy of JPL and the SMAP science team.) "The first time we were struck by the importance of soil moisture for weather forecasts was in July 1993," said Patricia de Rosnay, a researcher at the European Centre for Medium-Range Weather Forecasts (ECMWF). During the first six months of that year, extreme amounts of rain and snow fell on the central United States. Yet the existing weather models were not accounting for the storage and evaporation of all of that water. They could not see how the water on the land was feeding back into the weather patterns to make the deluge more extreme. By July 1993, the Upper Mississippi River faced its worst flooding on record. July 1993 also happened to be the same month that ECMWF scientists began testing a new weather forecast model. Their model accounted for soil moisture in the root zone, allowing researchers to see how the soil sustained a high level of evaporation and fed the extreme rainfall event. The new model had produced a closer representation of reality. The strength of the connection between soil moisture and the weather is not the same everywhere. According to NASA scientist Randy Koster, there are hot spots---about 10 percent of Earth's surface where the amount of soil moisture plays a more critical role in the weather. Water that evaporates from Earth's surface is linked to the formation of clouds and rainfall. In dry areas, variations in the amount of evaporation are too small to have much of an effect on the atmosphere. In humid regions, particularly the tropics, changes in soil moisture do not matter much for evaporation because it is limited by the amount of water that the atmosphere can hold. Landsat 8 acquired this image N'Djamena, Chad, on October 20, 2015. The city sits along the Logone River and within the African Sahel. As pictured here after the rainy season, the river's saturated banks are surrounded by a dry, sandy landscape. (NASA Earth Observatory image by Joshua Stevens, using Landsat data from the U.S. Geological Survey.) The soil moisture hot spots are areas that are neither too dry nor too wet. They are located in the transition zones between dry and wet areas---places that have suitably high evaporation that is more dependent on moisture on the ground than in the atmosphere. The Midwestern United States is one of those hot spots. So, too, are northern India and the African Sahel. Better estimates of soil moisture in weather models will not necessarily make for perfect long-range forecasts. Randomness in the variables that cause weather will always hinder the accuracy beyond a few days. But with better information on the thin reservoir in the soil, forecasters can tip the scales further in favor of getting weather prediction right. In the planet's highest northern latitudes, even the water in the soil is locked away as ice, making it mostly inaccessible to plants. But just a short distance to the south, in the boreal areas of Alaska, Canada, Siberia, and Scandinavia, the landscape comes alive each year after the spring thaw. The transition is relatively rapid, occurring over just a few weeks, and coincides with increasing sunlight and spring snowmelt. Rapid warming releases liquid water. As liquid water becomes more readily available, plant and animal activity are energized. The land greens up, and animals return to graze. "I'm always impressed by how rapidly northern landscapes transition from frozen and dormant conditions in the winter to a rapid burst of life and activity in the spring," said John Kimball, a scientist at the University of Montana. The transition between frozen and thawed land is something researchers have observed for more than 30 years with satellites. The Nimbus-7 Pathfinder, the Defense Meteorological Satellite Program (DMSP) satellites, and Aqua all have carried passive microwave radiometers. They detect the microwave energy coming from the Earth's surface, which will have different characteristics depending on whether the soil is frozen or thawed. When surface water and soil moisture is locked away as ice, this frozen landscape looks like a desert to a microwave sensor. Thawed landscapes look comparatively wet, so this large contrast is the basis for something called a freeze-thaw measurement. Across a year, the ice and frozen lands advance and retreat in the high northern and southern latitudes. (NASA Earth Observatory image by Joshua Stevens, using NASA's Blue Marble data.) Kimball and colleagues have mined 30 years of freeze-thaw patterns from the satellite record. In a paper published in 2012, the researchers showed that soils in the Northern Hemisphere thawed for as many as 7.5 days more in 2008 than they did in 1979. The change was primarily driven by an earlier start to the spring thaw and coincided with measureable warming in the region. "This was a real eye-opener to me," Kimball said. "We found that the earlier spring-thaw was driving widespread increases in northern growing seasons." The start and the length of the growing season have implications for how much carbon is exchanged between the land and atmosphere. Each year, half of all global carbon emissions are removed from the atmosphere by natural processes on the surface. It is sequestered somewhere on land, and a large amount of that carbon is stored at high latitudes. According to scientists at the Woods Hole Research Center, the boreal region covers about 15 percent of the global land surface, yet holds more than 30 percent of all carbon contained on land. A longer growing season in the north could make vegetation a more important "sink," removing carbon dioxide from the atmosphere and storing it in forest biomass, dead organic matter, and the soil. But those boreal lands also could become a carbon source though burning, decay, and thawing soil. Currently, the region is thought by many to be a net sink, absorbing more carbon than it releases. But how might thawing soils affect that balance? The answer is complicated by the fact that the timing of the thaw can vary dramatically over a small area. Sunlight sweeps over the landscape at a low angle, so areas with even the slightest rolling topography can be cast in either shade or sunlight. South-facing slopes thaw first. And just a few extra weeks of thawing time can have a huge impact on plant growth. Another complication in the carbon equation is permafrost. Even where the top layer of soil has thawed, there is often a long-term frozen layer below. This frozen layer locks up carbon so that it cannot decompose. But as seasonal thaws reach greater soil depths, more organic matter can decompose and get flushed out into the atmosphere by streams or degassing into the atmosphere. "There is debate as to how stable that soil will be with continued global warming," Kimball says. But progress is being made. Freeze-thaw monitoring, according to Kimball, has made a major advance thanks to the development of well-calibrated, long-term satellite soil moisture records. As those observations continue, and as they encompass more of the planet, it stands to reason that our understanding of the entire water cycle will improve. Soil moisture has an obvious, visible effect on the landscape. The high-profile examples are droughts and floods. But the water in the soil has a more subtle, yet equally important role in day-to-day weather. Soil moisture forms a vast, thin, and mostly out-of-sight reservoir of water that accumulates in the root zone of plants. The water is released to the atmosphere through evaporation and plant transpiration. Averaged globally, this evapotranspiration contributes to more than 60 percent of the precipitation that falls over land each year. "The first time we were struck by the importance of soil moisture for weather forecasts was in July 1993," said Patricia de Rosnay, a researcher at the European Centre for Medium-Range Weather Forecasts (ECMWF). During the first six months of that year, extreme amounts of rain and snow fell on the central United States. Yet the existing weather models were not accounting for the storage and evaporation of all of that water. They could not see how the water on the land was feeding back into the weather patterns to make the deluge more extreme. By July 1993, the Upper Mississippi River faced its worst flooding on record. July 1993 also happened to be the same month that ECMWF scientists began testing a new weather forecast model. Their model accounted for soil moisture in the root zone, allowing researchers to see how the soil sustained a high level of evaporation and fed the extreme rainfall event. The new model had produced a closer representation of reality. The strength of the connection between soil moisture and the weather is not the same everywhere. According to NASA scientist Randy Koster, there are hot spots---about 10 percent of Earth's surface where the amount of soil moisture plays a more critical role in the weather. Water that evaporates from Earth's surface is linked to the formation of clouds and rainfall. In dry areas, variations in the amount of evaporation are too small to have much of an effect on the atmosphere. In humid regions, particularly the tropics, changes in soil moisture do not matter much for evaporation because it is limited by the amount of water that the atmosphere can hold. The soil moisture hot spots are areas that are neither too dry nor too wet. They are located in the transition zones between dry and wet areas---places that have suitably high evaporation that is more dependent on moisture on the ground than in the atmosphere. The Midwestern United States is one of those hot spots. So, too, are northern India and the African Sahel. Better estimates of soil moisture in weather models will not necessarily make for perfect long-range forecasts. Randomness in the variables that cause weather will always hinder the accuracy beyond a few days. But with better information on the thin reservoir in the soil, forecasters can tip the scales further in favor of getting weather prediction right. Remotely sensed biomass carbon density maps are widely used for myriad scientific and policy applications, but all remain limited in scope. They often only represent a single vegetation type and rarely account for carbon stocks in belowground biomass. To date, no global product integrates these disparate estimates into an all-encompassing map at a scale appropriate for many modelling or decision-making applications. We developed an approach for harmonizing vegetation-specific maps of both above and belowground biomass into a single, comprehensive representation of each. We overlaid input maps and allocated their estimates in proportion to the relative spatial extent of each vegetation type using ancillary maps of percent tree cover and landcover, and a rule-based decision schema. The resulting maps consistently and seamlessly report biomass carbon density estimates across a wide range of vegetation types in 2010 with quantified uncertainty. They do so for the globe at an unprecedented 300-meter spatial resolution and can be used to more holistically account for diverse vegetation carbon stocks in global analyses and greenhouse gas inventories. Background & Summary Terrestrial ecosystems store vast quantities of carbon (C) in aboveground and belowground biomass1. At any point in time, these stocks represent a dynamic balance between the C gains of growth and C losses from death, decay and combustion. Maps of biomass are routinely used for benchmarking biophysical models2,3,4, estimating C cycle effects of disturbance5,6,7, and assessing biogeographical patterns and ecosystem services8,9,10,11. They are also critical for assessing climate change drivers, impacts, and solutions, and factor prominently in policies like Reducing Emissions from Deforestation and Forest Degradation (REDD+) and C offset schemes12,13,14. Numerous methods have been used to map biomass C stocks but their derivatives often remain limited in either scope or extent12,15. There thus remains a critical need for a globally harmonized, integrative map that comprehensively reports biomass C across a wide range of vegetation types. Most existing maps of aboveground biomass (AGB) and the carbon it contains (AGBC) are produced from statistical or data-driven methods relating field-measured or field-estimated biomass densities and spaceborne optical and/or radar imagery12,15,16. They largely focus on the AGB of trees, particularly those in tropical landscapes where forests store the majority of the region's biotic C in aboveground plant matter. Land cover maps are often used to isolate forests from other landcover types where the predictive model may not be appropriate such that forest AGB maps intentionally omit AGB stocks in non-forest vegetation like shrublands, grasslands, and croplands, as well as the AGB of trees located within the mapped extent of these excluded landcovers17. Non-forest AGB has also been mapped to some extent using similar approaches but these maps are also routinely masked to the geographic extent of their focal landcover18,19,20,21. To date, there has been no rigorous attempt to harmonize and integrate these landcover-specific, remotely sensed products into a single comprehensive and temporally consistent map of C in all living biomass. Maps of belowground biomass (BGB) and carbon density (BGBC) are far less common than those of AGB because BGB cannot be readily observed from space or airborne sensors. Consequently, BGB is often inferred from taxa-, region-, and/or climate-specific "root-to-shoot" ratios that relate the quantity of BGB to that of AGB22,23,24. These ratios can be used to map BGB by spatially applying them to AGB estimates using maps of their respective strata5. In recent years, more sophisticated regression-based methods have been developed to predict root-to-shoot ratios of some landcover types based on covariance with other biophysical and/or ecological factors25,26. When applied spatially, these methods can allow for more continuous estimates of local BGB5,27. Like AGBC, though, few attempts have been made to comprehensively map BGBC for the globe. Despite the myriad of emerging mapping methods and products, to date, the Intergovernmental Panel on Climate Change (IPCC) Tier-1 maps by Ruesch and Gibbs28 remains the primary source of global AGBC and BGBC estimates that transcend individual landcover types. These maps, which represents the year 2000, were produced prior to the relatively recent explosion of satellite-based AGB maps and they therefore rely on an alternative mapping technique called "stratify and multiply"15, which assigns landcover-specific biomass estimates or "defaults" (often derived from field measurements or literature reviews) to the corresponding classified grid cells of a chosen landcover map12. While this approach yields a comprehensive wall-to-wall product, it can fail to capture finer-scale spatial patterns often evident in the field and in many satellite-based products12,15. The accuracy of these maps is also tightly coupled to the quality and availability of field measurements29 and the thematic accuracy and discontinuity of the chosen landcover map. Given the wealth of landcover-specific satellite based AGB maps, a new harmonization method akin to "stratify and multiply" is needed to merge the validated spatial detail of landcover-specific remotely sensed biomass maps into a single, globally harmonized product. We developed such an approach by which we (i) overlay distinct satellite-based biomass maps and (ii) proportionately allocate their estimates to each grid cell ("overlay and allocate"). Specifically, we overlay continental-to-global scale remotely sensed maps of landcover-specific biomass C density and then allocate fractional contributions of each to a given grid cell using additional maps of percent tree cover, thematic landcover and a rule-based decision tree. We implement the new approach here using temporally consistent maps of AGBC as well as matching derived maps of BGBC to generate separate harmonized maps of AGBC and BGBC densities. In addition, we generate associated uncertainty layers by propagating the prediction error of each input dataset. The resulting global maps consistently represent biomass C and associated uncertainty across a broad range of vegetation in the year 2010 at an unprecedented 300 meter (m) spatial resolution. Our harmonization approach (Fig. 1) relies on independent, landcover-specific biomass maps and ancillary layers, which we compiled from the published literature (Table 1). When published maps did not represent our epoch of interest (i.e. grasslands and croplands) or did not completely cover the necessary spatial extent (i.e. tundra vegetation), we used the predictive model reported with the respective map to generate an updated version that met our spatial and temporal requirements. We then used landcover specific root-to-shoot relationships to generate matching BGBC maps for each of the input AGBC maps before implementing the harmonization procedure. Below we describe, in detail, the methodologies used for mapping AGBC and BGBC of each landcover type and the procedure used to integrate them. Woody tree biomass Since the first remotely sensed woody AGB maps were published in the early 1990s, the number of available products has grown at an extraordinary pace16 and it can thus be challenging to determine which product is best suited for a given application. For our purposes, we relied on the GlobBiomass AGB density map30 as our primary source of woody AGB estimates due to its precision, timestamp, spatial resolution, and error quantification. It was produced using a combination of spaceborne optical and synthetic aperture radar (SAR) imagery and represents the year 2010 at a 100 m spatial resolution -- making it the most contemporary global woody AGB currently available and the only such map available for that year. Moreover, GlobBiomass aims to minimize prediction uncertainty to less than 30% and a recent study suggests that it has high fidelity for fine-scale applications31. The GlobBiomass product was produced by first mapping the growing stock volume (GSV; i.e. stem volume) of living trees, defined following Food and Agriculture Organization (FAO) guidelines32 as those having a diameter at breast height (DBH) greater than 10 centimeters (cm). AGB density was then determined from GSV by applying spatialized biomass expansion factors (BEFs) and wood density estimates. These factors were mapped using machine learning methods trained from a suite of plant morphological databases that compile thousands of field measurements from around the globe33. The resulting AGB estimates represent biomass in the living structures (stems, branches, bark, twigs) of trees with a DBH greater than 10 cm. This definition may thereby overlook AGB of smaller trees and/or shrubs common to many global regions. Unlike other maps, though, the GlobBiomass product employs a subpixel masking procedure that retains AGB estimates in 100 m grid cells in which any amount of tree cover was detected in finer resolution (30 m) imagery34. This unique procedure retains AGB estimates in tree-sparse regions like savannahs, grasslands, croplands, and agroforestry systems where AGB is often overlooked17, as well as in forest plantations. The GlobBiomass product is the only global map that also includes a dedicated uncertainty layer reporting the standard error of prediction. We used this layer to propagate uncertainty when converting AGB to AGBC density, modelling BGBC, and integrating with C density estimates of other vegetation types. Bouvet et al.35 -- some of whom were also participants of the GlobBiomass project -- independently produced a separate AGB density map for African savannahs, shrublands and dry woodlands circa 2010 at 25 m spatial resolution35 (hereafter "Bouvet map"), which we included in our harmonized product to begin to address the GlobBiomass map's potential omission of small trees and shrubs that do not meet the FAO definition of woody AGB. This continental map of Africa is based on a predictive model that directly relates spaceborne L-band SAR imagery -- an indirect measure of vegetation structure that is sensitive to low biomass densities36 -- with region-specific, field-measured AGB. Field measurements (n = 144 sites) were compiled from 7 different sampling campaigns -- each specifically seeking training data for biomass remote sensing -- that encompassed 8 different countries35. The resulting map is not constrained by the FAO tree definition and is masked to exclude grid cells in which predicted AGB exceeds 85 megagrams dry mater per hectare (Mg ha−1) -- the threshold at which the SAR-biomass relationship saturates. To avoid extraneous prediction, it further excludes areas identified as "broadleaved evergreen closed-to-open forest", "flooded forests", "urban areas" and "water bodies" by the European Space Agency's Climate Change Initiative (CCI) Landcover 2010 map37 and as "bare areas" in the Global Land Cover (GLC) 2000 map38. While the Bouvet map is not natively accompanied by an uncertainty layer, its authors provided us with an analytic expression of its uncertainty (SE; standard error of prediction) as a function of estimated AGB (Eq. 1) which we used to generate an uncertainty layer for subsequent error propagation. We combined the GlobBiomass and Bouvet products to generate a single woody biomass map by first upscaling each map separately to a matching 300 m spatial resolution using an area-weighted average to aggregate grid cells, and then assigning the Bouvet estimate to all overlapping grid cells, except those identified by the CCI Landcover 2010 map as closed or flooded forest types (Online-only Table 1) which were not within the dryland domain of the Bouvet map. While more complex harmonization procedures based on various averaging techniques have been used by others39,40, their fidelity remains unclear since they fail to explicitly identify and reconcile the underlying source of the inputs' discrepancies41. We thus opted to use a more transparent ruled-based approach when combining these two woody biomass maps, which allows users to easily identify the source of a grid cell's woody biomass estimate. Given the local specificity of the training data used to produce the Bouvet map, we chose to prioritize its predictions over those of the GlobBiomass product when within its domain. In areas of overlap, the Bouvet map values tend to be lower in moist regions and higher in dryer regions (Fig. 2), though, where used, these differences rarely exceed ±25 megagrams C per hectare (MgC ha−1). We then converted all woody AGB estimates to AGBC by mapping climate and phylogeny-specific biomass C concentrations from Martin et al.42. Climate zones were delineated by aggregating classes of the Köppen-Gieger classification43 (Table 2) to match those of Martin et al.42. Phylogenetic classes (angiosperm, gymnosperm and mixed/ambiguous) were subsequently delineated within each of these zones using aggregated classes of the CCI Landcover 2010 map (Online-only Table 1). Martin et al.42 only report values for angiosperms and gymnosperms so grid cells with a mixed or ambiguous phylogeny were assigned the average of the angiosperm and gymnosperm values and the standard error of this value was calculated from their pooled variance. Due to residual classification error in the aggregated phylogenetic classes, we weighted the phylogeny-specific C concentration within each climate zone by the binary probability of correctly mapping that phylogeny where, within each climate zone, μc is the mean probability-weighted C concentration of the most probable phylogeny, μm is the mean C concentration of that phylogeny from Martin et al.42, pm is the user's accuracy of that phylogeny's classification (Table 3), and μn and μo are the mean C concentrations of the remain phylogenetic classes from Martin et al.42. Standard error estimates for these C concentrations were similarly weighted using summation in quadrature where is the probability-weighted standard error of the most probable phylogeny's C concentration and and are the standard errors of the respective phylogeny-specific C concentrations from Martin et al.42. Probability-weighted C concentrations used are reported in Table. Mapped, probability-weighted C estimates were then arithmetically applied to AGB estimates. Uncertainty associated with this correction was propagated using summation in quadrature of the general form (Eq. 4) is the uncertainty of μf, and , are the respective uncertainty estimates of the dependent parameters (standard error unless otherwise noted). Here, μf, is the estimated AGBC of a given grid cell, and is the product of its woody AGB estimate, and its corresponding C concentration. Tundra vegetation biomass The tundra and portions of the boreal biome are characterized by sparse trees and dwarf woody shrubs as well as herbaceous cover that are not included in the GlobBiomass definition of biomass. AGB density of these classes has been collectively mapped by Berner et al.18,45 for the North Slope of Alaska from annual Landsat imagery composites of the normalized difference vegetation index (NDVI) and a non-linear regression-based model trained from field measurements of peak AGB that were collected from the published literature (n = 28 sites). Berner et al.18 note that while these field measurements did not constitute a random or systematic sample, they did encompass a broad range of tundra plant communities. In the absence of a global map and due the sparsity of high quality Landsat imagery at high latitudes, we extended this model to the pan-Arctic and circumboreal regions using NDVI composites created from daily 250 m MODIS Aqua and Terra surface reflectance images46,47 that were cloud masked and numerically calibrated to Landsat ETM reflectance -- upon which the tundra model is based -- using globally derived conversion coefficients48. We generated six separate 80th percentile NDVI composites circa 2010 -- one for each of the MODIS missions (Aqua and Terra) in 2009, 2010 and 2011 -- following Berner et al.18. We chose to use three years of imagery (circa 2010) rather than just one (2010) to account for the potential influence that cloud masking may exert upon estimates of the 80th NDVI percentile in a single year. We then applied the tundra AGB model to each composite, converted AGB estimates to AGBC by assuming a biomass C fraction of 49.2% (SE = 0.8%)42 and generated error layers for each composite from the reported errors of the AGB regression coefficients and the biomass C conversion factor using summation in quadrature as generally described above (Eq. 4). A single composite of tundra AGBC circa 2010 was then created as the pixelwise mean of all six composites. We also generated a complementary uncertainty layer representing the cumulative standard error of prediction, calculated as the pixelwise root mean of the squared error images in accordance with summation in quadrature. Both maps were upscaled from their native 250 m spatial resolution to a 300 m spatial resolution using an area weighted aggregation procedure, whereby pixels of the 300 m biomass layer was calculated as the area weighted average of contained 250 m grid cells, and the uncertainty layer was calculated -- using summation in quadrature -- as the root area-weighted average of the contained grid cells squared. Grassland biomass Grassland AGBC density was modelled directly from maximum annual NDVI composites using a non-linear regression-based model developed by Xia et al.19 for mapping at the global scale. This model was trained by relating maximum annual NDVI as measured by the spaceborne Advanced Very High-Resolution Radiometer (AVHRR) sensor to globally distributed field measurements of grassland AGBC that were compiled from the published literature (81 sites for a total of 158 site-years). Like the tundra biomass training data, these samples did not constitute a random or systematic sample but do encompass a comprehensive range of global grassland communities. Given the inevitable co-occurrence of trees in the AVHRR sensor's 8 km resolution pixels upon which the model is trained, it's predictions of grassland AGBC are relatively insensitive to the effects of co-occurring tree cover. We thereby assume that its predictions for grid cells containing partial tree cover represent the expected herbaceous AGBC density in the absence of those trees. Maximum model predicted AGBC (NDVI = 1) is 2.3 MgC ha−1 which is comparable to the upper quartile of herbaceous AGBC estimates from global grasslands49 and suggests that our assumption will not lead to an exaggerated estimation. For partially wooded grid cells, we used modelled grassland AGBC density to represent that associated with the herbaceous fraction of the grid cell in a manner similar to Zomer et al.17 as described below (See "Harmonizing Biomass Carbon Maps"). We applied the grassland AGBC model to all grid cells of maximum annual NDVI composites produced from finer resolution 16-day (250 m) MODIS NDVI imagery composites circa 201050,51. Here again, three years of imagery were used to account for potential idiosyncrasies in a single year's NDVI composites resulting from annual data availability and quality. As with AGB of tundra vegetation, annual composites (2009--2011) were constructed separately from cloud-masked imagery collected by both MODIS missions (Aqua and Terra; n = 6) and then numerically calibrated to AVHRR reflectance using globally derived conversion coefficients specific to areas of herbaceous cover52. We then applied the AGBC model to each of these composites and estimated error for each composite from both the AVHRR calibration (standard deviation approximated from the 95% confidence interval of the calibration scalar) and the AGBC model (relative RMSE) using summation in quadrature. A single map of grassland AGBC circa 2010 was then created as the pixelwise mean of all six composites and an associated error layer was created as the pixelwise root mean of the squared error images. Both maps were aggregated from their original 250 m resolution to 300 m to facilitate harmonization using the area-weighted procedure described previously for woody and tundra vegetation (see section 1.2). Cropland biomass Prior to harvest, cropland biomass can also represent a sizable terrestrial C stock. In annually harvested cropping systems, the maximum standing biomass of these crops can be inferred from annual net primary productivity (ANPP). While spaceborne ANPP products exist, they generally perform poorly in croplands53,54. Instead, cropland ANPP is more commonly derived from crop yields20,21,53. We used globally gridded, crop-specific yields of 70 annually harvested herbaceous commodity crops circa 2000 by Monfreda et al.20 -- the only year in which these data were available. These maps were produced by spatially disaggregating crop-yield statistics for thousands of globally distributed administrative units throughout the full extent of a satellite-based cropland map20. These maps were combined with crop-specific parameters (Online-only Table 2) to globally map AGBC as aboveground ANPP for each crop following the method of Wolf et al.21. This method can be simplified as (Eq. 5) where y is the crop's yield (Mg ha−1), ω is the dry matter fraction of its harvested biomass, h is its harvest index (fraction of total AGB collected at harvest) and c is the carbon content fraction of its harvested dry mass. This simplification assumes, following Wolf et al.21, that 2.5% of all harvested biomass is lost between the field and farmgate and that unharvested residue and root mass is 44% C. Total cropland AGBC density was then calculated as the harvested-area-weighted average of all crop-specific AGBC estimates within a given grid cell. Since multiple harvests in a single year can confound inference of maximum AGBC from ANPP, we further determined the harvest frequency (f) of each grid cell by dividing a cell's total harvested area (sum of the harvested area of each crop reported within a given grid cell) by its absolute cropland extent as reported in a complementary map by Ramankutty et al.55. If f was greater than one, multiple harvests were assumed to have occurred and AGBC was divided by f to ensure that AGBC estimates did not exceed the maximum standing biomass density. Since the yields of many crops and, by association, their biomass have changed considerably since 200056,57, we calibrated our circa 2000 AGBC estimates to the year 2010 using local rates of annual ANPP change (MgC ha−1 yr−1) derived as the Theil-Sen slope estimator -- a non-parametric estimator that is relatively insensitive to outliers -- of the full MODIS Terra ANPP timeseries (2000--2015)58. Total ANPP change between 2000 and 2010 for each grid cell was calculated as ten times this annual rate of change. Since MODIS ANPP represents C gains in both AGB and BGB, we proportionately allocated aboveground ANPP to AGBC using the total root-to-shoot ratio derived from the circa 2000 total crop AGBC and BGBC maps (described below). Since error estimates were not available for the yield maps or the crop-specific parameters used to generate the circa 2000 AGBC map, estimated error of the circa 2010 crop AGBC map was exclusively based on that of the 2000--2010 correction. The error of this correction was calculated as the pixel-wise standard deviation of bootstrapped simulations (n = 1000) in which a random subset of years was omitted from the slope estimator in each iteration. The 8 km resolution circa 2000 AGBC map and error layer were resampled to 1 km to match the resolution of MODIS ANPP using the bilinear method prior to ANPP correction and then further resampled to 300 m to facilitate harmonization. Woody crops like fruit, nut, and palm oil plantations were not captured using the procedure just described and their biomass was instead assumed to be captured by the previously described woody biomass products which retained biomass estimates in all pixels where any amount of tree cover was detected at the sub-pixel level (see section 1.1). Belowground biomass carbon maps Matching maps of BGBC and associated uncertainty were subsequently produced for each of the landcover-specific AGBC maps using published empirical relationships. With the exception of savannah and shrubland areas, woody BGBC was modelled from AGBC using a multiple regression model by Reich et al.25 that considers the phylogeny, mean annual temperature (MAT), and regenerative origin of each wooded grid cell and that was applied spatially using maps of each covariate in a fashion similar to other studies5,27. Tree phylogeny (angiosperm or gymnosperm) was determined from aggregated classes of the CCI Landcover 2010 map37 (Online-only Table 1) with phylogenetically mixed or ambiguous classes assumed to be composed of 50% of each. MAT was taken from version 2 of the WorldClim bioclimatic variables dataset (1970--2000) at 1 km resolution59 and resampled to 300 m using the bilinear method. Since there is not a single global data product mapping forest management, we determined tree origin -- whether naturally propagated or planted -- by combining multiple data sources. These data included (i) a global map of "Intact Forest Landscapes" (IFL) in the year 201360 (a conservative proxy of primary, naturally regenerating forests defined as large contiguous areas with minimal human impact), (ii) a Spatial Database of Planted Trees (SDPT) with partial global coverage61, (iii) national statistics reported by the FAO Global Forest Resources Assessment (FRA) on the extent of both naturally regenerating and planted forests and woodlands within each country in the year 201062, total area of natural and planted trees was equal to the corresponding FRA estimates. If the FAOSTAT-reported area of tree crops exceeded FRA-reported planted area, the difference was added to FRA planted total. All areas mapped as IFL were assumed to be of natural origin and BGB was modelled as such. Likewise, besides the exceptions noted below, all tree plantations mapped by the SDPT were assumed to be of planted origin. In countries where the extent of the IFL or SDPT maps fell short of the FRA/FAOSTAT reported areas of natural or planted forests, respectively, we estimated BGBC in the remaining, unknown-origin forest grid cells of that country (BGBCu), as the probability-weighted average of the planted and natural origin estimates using Eq. 6 and are the respective BGBC estimates for a grid cell assuming entirely planted and natural origin, respectively, and and are the respective differences between (i) the FRA/FAOSTAT and (ii) mapped extent of planted and natural forest within the given grid cell's country. While the mapped extent of IFL forests within a given country never exceeded that country's FRA reported natural forest extent, there were infrequent cases (n = 22 of 257) in which the mapped extent of tree plantations exceeded the corresponding FRA/FAOSTAT estimate of planted forest area. In these cases, we down-weighted the BGB estimates of SDPT forests in a similar fashion such that the weight of their planted estimate ( ) was equal to the quotient of (i) the FRA/FAOSTAT planted area and (ii) the SDPT extent within the country, and the weight of the natural origin estimate applied to the SDPT extent ( ) was equal to A BGBC error layer was then produced using summation in quadrature from the standard error estimates of the model coefficients, the AGBC error layer, the relative RMSE of MAT (27%), and the derived global uncertainty of the phylogeny layer. Phylogeny error was calculated as the Bernoulli standard deviation (δ) of the binary probability (p) of correct classification (i.e. "area weighted user's accuracy"44; Table 3) using Eq. 7. Since savannahs and shrublands are underrepresented in the regression-based model25, their BGBC was instead estimated using static root-to-shoot ratios reported by Mokany et al.22, which are somewhat conservative in comparison to the IPCC Tier-1 defaults23,24 put favoured for consistency with methods used for grasslands (see below). Error was subsequently mapped from that of the AGBC estimates and the root-to-shoot ratios applied BGBC of tundra vegetation was mapped from AGBC using a univariate regression model derived by Wang et al.26 that predicts root-to-shoot ratio as a function of MAT. We applied the model using the WorldClim version 2 MAT map59 and propagated error from the AGBC estimates, the relative RMSE of MAT and the standard error of regression coefficients. Where tundra AGB exceeded 25 Mg ha−1 -- the maximum field-measured shrub biomass reported by Berner et al.18 -- vegetation was considered to include trees and the Reich et al.25 method described earlier for woody vegetation was used instead. In the absence of a continuous predictor of grassland root-to-shoot ratios, we applied climate specific root-to-shoot ratios from Mokany et al.22 to the corresponding climate regions of the Köppen-Gieger classification43 (Table 2). Here, again, these ratios vary slightly from the IPCC Tier-1 defaults23,24 but were chosen for their greater sample size and specificity. Grassland BGBC error was mapped from the error of the AGBC estimates and the respective root-to-shoot ratios. Cropland BGBC was again estimated from crop-specific yields and morphological parameters (Online-only Table 2) following Wolf et al.21 and Eq. 8 where y is the crop's yield (Mg ha−1), r is the root-to-shoot ratio of the crop, and h is its harvest index. Here again we assume that 2.5% of all harvested biomass is lost between the field and farmgate and that root biomass is 44% C, following Wolf et al.21. BGBC error was mapped from the error of the 2000-to-2010 ANPP correction for BGBC allocation as described above for cropland AGBC. Harmonizing biomass carbon maps The AGBC and BGBC maps were harmonized separately following the same general schema (Fig. 3). Given that our harmonized woody biomass map contains biomass estimates for grid cells in which any amount of tree cover was detected at the subpixel level (see section 1.1), we conserved its estimates regardless of the landcover reported by the 2010 CCI map in order to more fully account for woody biomass in non-forested areas17. We then used the MODIS continuous vegetation fields percent tree cover map for 201063 to allocate additional biomass density associated with the most probable herbaceous cover (grass or crop) to each grid cell in quantities complementary to that of the grid cell's fractional tree cover estimate (Eq. 9) where μT is the total biomass estimate of a grid cell, μw is the woody biomass estimate for the grid cell, μh is its herbaceous biomass estimate, and q is the MODIS fractional tree cover of the grid cell. Since MODIS tree cover estimates saturate at around 80%64, we linearly stretched values such that 80% was treated as complete tree cover (100%). Moreover, we acknowledge that percent cover can realistically exceed 100% when understory cover is considered but we were unable to reasonably determine the extent of underlying cover from satellite imagery. As such, our approach may underestimate the contribution of herbaceous C stocks in densely forested grid cells. The most likely herbaceous cover type was determined from the CCI Landcover 2010 map, which we aggregated into two "likely herbaceous cover" classes -- grass or crop -- based on the assumed likelihood of cropland in each CCI class (Online-only Table 1). However, due to inherent classification error in the native CCI Landcover map, when determining the herbaceous biomass contribution we weighted the relative allocation of crop and grass biomass to a given grid cell based on the probability of correct classification by the CCI map (i.e. "user's accuracy", Table 6) of the most probable herbaceous class ( ) such that μh can be further expressed as (Eq. 10) where μi is the predicted biomass of the most probable herbaceous class, and μj is that of the less probable class. The uncertainty of a grid cell's total AGBC or BGBC estimate ( ) was determined and mapped from that of its components ( ) by summation in quadrature which can be simplified as (Eq. 11) is the error of the grid cell's estimated μw, is the error of its estimated μh, and is the error of its q. Here, can be further decomposed and expressed as Eq. 12 to account for the accuracy weighted allocation procedure expressed previously (Eq. 10) is the error of the estimated biomass density of the most probable herbaceous class, is the estimated standard deviation of that class's Bernoulli probability (p; Eq. 7), and is the error of the estimated biomass density of the less probable herbaceous subclass. Exceptions to the above schema were made in the tundra and boreal biomes -- as delineated by the RESOLVE Ecoregions 2017 biome polygons65 -- where thematic overlap was likely between the woody and tundra plant biomass maps. A separate set of decision rules (Fig. 3) was used to determine whether grid cells in these biomes were to be exclusively allocated the estimate of the tundra plant map or that of the fractional allocation procedure described above. In general, any land in these biomes identified as sparse landcover by the CCI landcover map (Online-only Table 1) was assigned the tundra vegetation estimate. In addition, lands north of 60° latitude with less than 10% tree cover or where the tundra AGBC estimate exceeded that of the woody AGBC estimate were also exclusively assigned the tundra vegetation estimate. Lands north of 60° latitude not meeting these criteria were assigned the woody value with the additional contribution of grass. Subtle numerical artefacts emerged from the divergent methodologies employed north and south of 60°N latitude. These were eliminated by distance weighting grid cells within 1° of 60°N based on their linear proximity to 60°N and then averaging estimates such that values at or north of 61°N were exclusively based on the northern methodology, those at 60°N were the arithmetic average of the two methodologies and those at or south of 59°N were exclusively based on the southern methodology. This produced a seamless, globally harmonized product that integrates the best remotely sensed estimates of landcover-specific C density. Water bodies identified as class "210" of the CCI 2010 landcover map were then masked from our final products. Data Records Data layers (n = 4, Table 7) for the maps of AGBC and BGBC density (Fig. 4) as well as their associated uncertainty maps which represent the combined standard error of prediction (Fig. 5) are available as individual 16-bit integer rasters in GeoTiff format. All layers are natively in a WGS84 Mercator projection with a spatial resolution of approximately 300 m at the equator and match that of the ESA CCI Landcover Maps37. Raster values are in units megagrams C per hectare (MgC ha−1) and have been scaled by a factor of ten to reduce file size. These data are accessible through the Oak Ridge National Laboratory (ORNL) DAAC data repository. In addition, updated and/or derived vegetation-specific layers that were used to create our harmonized 2010 maps are available as supplemental data on figshare67. Technical Validation Our harmonized products rely almost exclusively upon maps and models that have been rigorously validated by their original producers and were often accompanied by constrained uncertainty estimates. Throughout our harmonization procedure, we strived to conserve the validity of each of these products by minimizing the introduction of additional error and by tracking any introductions, as described above, such that the final error layers represent the cumulative uncertainty of the inputs used. Ground truth AGB and BGB data are almost always collected for individual landcover types. Consequently, we are unable to directly assess the validity of our integrated estimates beyond their relationships to individual landcover-specific estimates and the extents to which they were modified from their original, previously-validated form prior to and during our harmonization procedure. Modifications to independent biomass layers Temporal and spatial updates made to existing landcover-specific maps of non-tree AGB resulted in relatively small changes to their predictions. For example, we used numerically calibrated MODIS imagery to extend the Landsat-based tundra plant AGB model beyond its native extent (the North Slope of Alaska) to the pan-Arctic region since neither a comparable model nor a consistent Landsat time series were available for this extent. We assessed the effects of these assumptions by comparing our predictions for the North Slope with those of the original map18 (Fig. 6a). Both positive and negative discrepancies exist between ours and the original, though these rarely exceed ±2 MgC ha−1 and no discernibly systematic bias was evident. Fig. 6 figure 6 Differences between landcover-specific AGBC estimates from the original published maps and the modified versions used as inputs to create the 2010 harmonized global maps. Tundra vegetation AGBC (a) is compared to the Landsat-based map of Berner et al.45 for the north slope of Alaska after converting it to units MgC ha−1. Here, the comparison map was subsequently aggregated to a 1 km resolution and reprojected for visualization. Grassland AGBC (b) is compared to the AVHRR-based map of Xia et al.19 which represents the average estimate between 1982--2006. For visualization, the map was aggregated to a 5 km resolution and subsequently reprojected after being masked to MODIS IGBP grasslands in the year 200685 following Xia et al.19. As such, this map does not necessarily represent the spatial distribution of grid cells in which grassland estimates were used. Cropland AGBC (c) is compared to the original circa 2000 estimates to assess the effects of the 2000-to-2010 correction. The map is masked to the native extent of the combined yield maps and aggregated to a 5 km resolution for visualization. For all maps, negative values indicate that our circa 2010 estimates are lower than those of the earlier maps while positive values indicate higher estimates. Full size image Our updated map of grassland biomass carbon in the year 2010 was similarly made by applying the original AVHRR-based model to calibrated MODIS imagery. This too resulted in only subtle changes to the original biomass map (Fig. 6b) that were rarely in excess of 0.5 MgC ha−1. In most areas, our estimates were higher than those of Xia et al.19 who mapped the mean AGBC density between 1986 and 2006. Most of these elevated estimates corresponded with areas in which significant NDVI increases ("greening") have been reported while notably lower estimates in the Argentine Monte and Patagonian steppe biomes of southern South America, likewise, correspond with areas of reported "browning"68,69. Both greening and browning trends are well documented phenomena and have been linked to climatic changes70. Moreover, we further compared AGBC estimates from both the original Xia et al.19 map and our 2010 update to AGBC field measurements coordinated by the Nutrient Network that were collected from 48 sites around the world between 2007 and 200949. The RMSE (0.68 MgC ha−1) of our updated map was 10% less that of the Xia et al. map for sites with less than 40% tree cover. Likewise, our 2010 estimates were virtually unbiased (bias = −0.01 MgC ha−1) in comparison to the Xia map (bias = 0.25 MgC ha−1). While still noisy, these results suggest that our temporal update improved the overall accuracy of estimated grassland AGBC. Finally, cropland biomass carbon maps were also updated from their native epoch (2000) to 2010 using pixel-wise rates of MODIS ANPP change over a ten-year period. While MODIS ANPP may be a poor snapshot of crop biomass in a single year, we assumed that its relative change over time reflects real physiological shifts affecting the cropland C cycle. This correction also resulted in only small differences that rarely exceeded ±2 MgC ha−1 and that, spatially, correspond well with observed declines in the yields of select crops that have been linked to climate change71,72 (Fig. 6c). Nonetheless, updated global yield maps comparable to those available for 2000 would greatly improve our understanding of the interactions between climate change, crop yields, and C dynamics. Belowground biomass estimates Belowground biomass is notoriously difficult to measure, model, and also to validate. We accounted for the reported uncertainty of nearly every variable considered when estimating belowground biomass and pixel-level uncertainty, but we were unable to perform an independent validation of our harmonized estimates at the pixel level due to a paucity of globally consistent field data. To complete such a task, a globally orchestrated effort to collect more BGB samples data across all vegetation types is needed. Given this lack of data, we instead compared the estimated uncertainty of our BGBC maps to that of our AGBC estimates to infer the sources of any divergence (Fig. 5). As expected, our cumulative BGBC uncertainty layer generally reveals greater overall uncertainty than our AGBC estimates, with BGBC uncertainty roughly twice that of AGBC throughout most of the globe. The highest absolute uncertainty was found in biomass rich forests. Arid woodlands, especially those of the Sahel and eastern Namibia, generally had the greatest relative BGBC uncertainty, though their absolute uncertainty was quite small (generally less than 3 MgC ha−1). Here, biomass estimates of sparse woody vegetation were primarily responsible for heightened relative uncertainty. High relative and absolute BGBC uncertainty were also associated with predictions in select mountainous forests (e.g. east central Chile) as well as forested areas in and around cities. These patterns were largely driven by AGB uncertainty in the GlobBiomass product. Biomass harmonization The GlobBiomass global woody AGB map produced by Santoro et al.30 comprises the backbone of our integrated products and, with few exceptions, remains largely unchanged in our final AGBC map. The native version of the GlobBiomass map is accompanied by an error layer describing the uncertainty of each pixel's biomass estimate and this too forms the core of our integrated uncertainty layers. In areas with tree cover, the global average error of GlobBiomass estimates is 39 Mg ha−1 or 50% with greater relative uncertainty in densely forested areas, along the margins of forested expanses like farm fields and cities, and in similar areas with sparse tree cover. Adding additional grass or crop biomass in complementary proportion to a grid cell's tree cover often did not exceed the estimated error of the original GlobBiomass map (Fig. 7). Grid cells exceeding GlobBiomass's native uncertainty comprise less than 40% of its total extent. Exceptions were primarily found in grassland and cropland dominated regions where tree cover was generally sparse, and, consequently, the herbaceous biomass contribution was relatively high. Even so, the absolute magnitude of these additions remains somewhat small (less than 2.3 MgC ha−1 for grassland and 15 MgC ha−1 for cropland). Fig. 7 figure 7 Differences between the final harmonized AGBC map and GlobBiomass AGBC. GlobBiomass AGB was aggregated to a 300 m spatial resolution and converted to C density prior to comparison. Negative values indicate areas where the new map reports lower values than GlobBiomass while positive value denote higher estimates. Full size image Larger deviations from GlobBiomass were also present in areas of both dryland Africa and the Arctic tundra biome, where we used independent layers to estimate woody biomass. In African drylands, GlobBiomass likely underestimates woody biomass by adopting the conservative FAO definition (DBH \> 10 cm), which implicitly omits the relatively small trees and shrubs that are common to the region. The Bouvet map of Africa that we used to supplement these estimates is not bound by this constraint, was developed from region-specific data, and predicts substantially higher AGB density throughout much of its extent with comparatively high accuracy (RMSE = 17.1 Mg ha−1)35. GlobBiomass also included sporadic biomass estimates throughout much of the Arctic tundra biome. Trees are generally scarce throughout this biome, which is instead dominated by dwarf shrubs and herbaceous forbs and graminoids, so given GlobBiomass's adherence to FAO guidelines, its predictions here may be spurious. We thus prioritized the estimates of the independent model developed specifically to collectively predict biomass of both woody and herbaceous tundra vegetation. These estimates were generally higher than GlobBiomass but agreed well with independent validation data from North America (RMSE = 2.9 Mg ha−1)18. Comparison with the IPCC Tier-1 global biomass carbon map While far from a perfect comparison, the only other map to comprehensively report global biomass carbon density for all landcover types is the IPCC Tier-1 map for the year 2000 by Ruesch and Gibbs28. As previously described, this map was produced using an entirely different method ("stratify and multiply") and distinct data sources23 and represents an earlier epoch. However, the map is widely used for myriad applications, and it may thus be informative to assess notable differences between it and our new products. Ruesch and Gibbs28 report total living C stocks of 345 petagrams (PgC) in AGBC and 133 PgC in BGBC for a total of 478 PgC, globally. Our estimates are lower at 287 PgC and 122 PgC in global AGBC and BGBC, respectively, for a total of 409 PgC in living global vegetation biomass. Herbaceous biomass in our maps comprised 9.1 and 28.3 PgC of total AGBC and BGBC, respectively. Half of all herbaceous AGBC (4.5 PgC) and roughly 6% of all herbaceous BGBC (1.7 PgC) was found in croplands. Moreover, we mapped 22.3 and 6.1 PgC, respectively, in the AGB and BGB of trees located within the cropland extent. These trees constituted roughly 7% of all global biomass C and are likely overlooked by both the Ruesch and Gibbs map28 and by remotely sensed forest C maps that are masked to forested areas. Zomer et al.17 first highlighted this potential discrepancy in the Ruesch and Gibbs map28 when they produced a remarkably similar estimate of 34.2 Pg of overlooked C in cropland trees using Tier-1 defaults. However, their estimates were assumed to be in addition to the 474 PgC originally mapped by Ruesch and Gibbs28. Here, we suggest that the 28.4 PgC we mapped in cropland trees is already factored into our 409 PgC total. Our AGBC product predicts substantially less biomass C than Ruesch and Gibbs28 throughout most of the pantropical region and, to a lesser extent, southern temperate forests (Fig. 8a). This pattern has been noted by others comparing the Ruesch and Gibbs map28 to other satellite-based biomass maps73 and may suggest that the IPCC default values used to create it23 are spatially biased. In addition, well-defined areas of high disagreement emerge in Africa that directly correspond with the FAO boundaries of the "tropical moist deciduous forest" ecofloristic zone and suggest that this area, in particular, may merit critical review. Moreover, the opposite pattern is observed in this same ecofloristic zone throughout South America. Our map also predicts greater AGBC throughout much of the boreal forest as well as in African shrublands and the steppes of South America. We observed similar, though less pronounced discrepancies, when comparing BGBC maps (Fig. 8b). Notably, our map predicts substantially more BGBC throughout the tundra biome -- a previously underappreciated C stock that has recently risen to prominance74 -- the boreal forest, African shrublands and most of South America and Australia. However, we predict less BGBC in nearly all rainforests (Temperate and Tropical). These differences and their distinct spatial patterns correspond with the vegetation strata used to make the IPCC Tier-1 map28 and suggest that the accuracy of the "stratify and multiply" method depends heavily upon the quality of the referenced and spatial data considered. Inaccuracies in these data may, in turn, lead to false geographies. Integrating, continuous spatial estimates that better capture local and regional variation, as we have done, may thus greatly improve our understanding of global carbon geographies and their role in the earth system. Congruence with IPCC Tier-2 and Tier-3 nationally reported woody carbon stocks The error and variance between our woody biomass estimates -- when aggregated to the country level -- and comparable totals reported in the FRA were less for comparisons made against FRA estimates generated using higher tier IPCC methodologies than for those based on Tier-1 approaches (Fig. 9). Across the board for AGBC, BGBC, and total C comparisons, the relative RMSE (RMSECV) of our estimates, when compared to estimates generated using high tier methods, was roughly half of that obtained from comparisons with Tier-1 estimates (Table 8). Likewise, the coefficient of determination (R2) was greatest for comparisons with Tier-3 estimates. For each pool-specific comparison (AGBC, BGBC, and total C), the slopes of the relationships between Tier-1, 2, and 3 estimates were neither significantly different from a 1:1 relationship nor from one another (p \> 0.05; ANCOVA). Combined, these results suggest that our maps lead to C stock estimates congruent with those attained from independent, higher-tier reporting methodologies. Fig. 9 figure 9 Comparison of woody biomass density estimates to corresponding estimates of the FAO's FRA and the USFS's FIA. National woody AGBC totals derived from the woody components of our harmonized maps are compared to national totals reported in the 2015 FRA62 (a) in relation to the IPCC inventory methodology used by each country. Likewise, we derived woody AGBC totals for US states and compared them to the corresponding totals reported by the 2014 FIA75 (b), a Tier-3 inventory. We also show the additional effect of considering non-woody C -- as is reported in our harmonized maps -- in light green. Similar comparisons were made between our woody BGBC estimates and the corresponding estimates of both the FRA (c) and FIA (d). We further summed our woody AGBC and BGBC estimates and compared them to the total woody C stocks reported by both the FRA (e) and FIA (f). Full size image Table 8 Statistical comparison of woody biomass carbon totals derived from the 2010 harmonized maps and those reported by the FRA in relation to the IPCC inventory methodology used. Full size table To explore this association at a finer regional scale, we also compared our woody C estimates to the United States Forest Service's Forest Inventory Analysis75 (FIA) and found similarly strong congruence for AGBC and Total C stocks but subtle overestimates for BGBC (Fig. 9). The FIA is a Tier-3 inventory of woody forest biomass C stocks that is based on extensive and statistically rigorous field sampling and subsequent upscaling, We used data available at the state level for the year 2014 -- again, the only year in which we could obtain data partitioned by AGBC and BGBC. Like our FRA comparison, we found a tight relationship between our woody AGBC totals and those reported by the FIA (Fig. 9b; RMSECV = 25.7%, R2 = 0.960, slope = 1.10, n = 48). Our woody BGBC estimates, though, were systematically greater than those reported by the FIA (Fig. 9d; RMSECV = 86.4%, R2 = 0.95, slope = 1.51, n = 48). This trend has been noted by others27 and suggests that the global model that we used to estimate woody BGBC may not be appropriate for some finer scale applications as is foretold by the elevated uncertainty reported in our corresponding uncertainty layer (Fig. 5b). Our total woody C (AGBC + BGBC) estimates (Fig. 9f), however, agreed well with the FIA (RMSECV = 34.1%, R2 = 0.961, slope = 1.17, n = 48) and thus reflect the outsized contribution of AGBC to the total woody C stock. When the contribution of herbaceous C stocks is further added to these comparisons, our stock estimates intuitively increase in rough proportion to a state's proportional extent of herbaceous cover. The effect of this addition is particularly pronounced for BGBC estimates due to the large root-to-shoot ratios of grassland vegetation. The relative congruence of our results with higher-tier stock estimates suggests that our maps could be used to facilitate broader adoption of higher-tier methods among countries currently lacking the requisite data and those seeking to better account for C in non-woody biomass. This congruence spans a comprehensive range of biophysical conditions and spatial scales ranging from small states to large nations. Moreover, a recent study suggests that the fidelity of the underlying GlobBiomass AGB map may extend to even finer scales31. While our BGBC estimates may differ from some fine-scale estimates (Fig. 9d), their tight agreement with high tier BGBC totals at the national level (Fig. 9c) suggests that they may still be well suited for many national-scale C inventories -- especially for countries lacking requisite high tier data. Use of our maps is unlikely to introduce error in excess of that currently implicit in Tier-1 estimates. Credence, though, should be given to the associated uncertainty estimates. To facilitate wider adoption of higher-tier methodologies, our maps could be used to derive new, region-specific default values for use in Tier-2 frameworks76 or to either represent or calibrate 2010 baseline conditions in Tier-3 frameworks. In so doing, inventories and studies alike could more accurately account for the nuanced global geographies of biomass C. Usage Notes These maps are intended for global applications in which continuous spatial estimates of live AGBC and/or BGBC density are needed that span a broad range of vegetation types and/or require estimates circa 2010. They are loosely based upon and share the spatial resolution of the ESA CCI Landcover 2010 map37, which can be used to extract landcover specific C totals. However, our products notably do not account for C stored in non-living C pools like litter or coarse woody debris, nor soil organic matter, though these both represent large, additional ecosystem C stocks77,78,79. Our maps are explicitly intended for global scale applications seeking to consider C in the collective living biomass of multiple vegetation types. For global scale applications focused exclusively on the C stocks of a single vegetation type, we strongly encourage users to instead use the respective input map or model referenced in Table 1 to avoid potential errors that may have been introduced by our harmonization procedure. For AGB applications over smaller extents, users should further consider whether locally specific products are available. If such maps are not available and our maps are considered instead, credence should be given to their pixel-level uncertainty estimates. As mentioned above, the biomass of shrublands was only explicitly accounted for in Africa and the Arctic tundra, since neither broad-scale maps nor models generalizable to other areas were available in the existing literature. As such, we caution against the use of our maps outside of these areas when shrubland biomass is of particular interest or importance. Moreover, in contrast to the estimates for all other vegetation types considered, which we upscaled to a 300 m resolution, cropland C estimates were largely based on relatively coarse 8 km resolution data that were downscaled using bilinear resampling to achieve a 300 m spatial resolution. As such, these estimates may not adequately capture the underlying finer-scale spatial variation and should be interpreted with that in mind. Likewise, we reiterate that some BGBC estimates may differ from locally derived Tier-3 estimates, and attention should thus be given to our reported pixel-level uncertainty for all applications. Finally, our maps should not be used in comparison with the IPCC Tier-1 map of Ruesch and Gibbs (2008) to detect biomass change between the two study periods due to significant methodological differences between these products. An estimated 720 and 811 million people in the world faced hunger in 2020, according to the United Nations (UN), and nearly one in three people in the world (2.37 billion) did not have access to adequate food in 2020. The vulnerabilities and inadequacies of global food systems are expected to further intensify over the coming years. The combination of NASA Earth science data with socioeconomic data provides key information for sustainable use of available resources. NASA's Socioeconomic Data and Applications Center (SEDAC) is the home for NASA socioeconomic data and is a gateway between the social sciences and the Earth sciences. SEDAC provides numerous datasets and data collections that may be useful for studies into agriculture and water management. SEDAC also provides information about the connections that support efforts to end hunger, achieve food security and improved nutrition, and promote sustainable agriculture. Women in the West African country of Senegal take a break from crushing millet. The United Nations World Food Program estimates that 46 percent of households in Senegal lack reliable access to adequate amounts of food. Credit: Molly Brown Women in the West African country of Senegal take a break from crushing millet. The United Nations World Food Program estimates that 46% of households in Senegal lack reliable access to adequate amounts of food. Credit: Molly Brown. NASA helps develop tools to address food security and works with decision-makers and data users to tailor these tools to specific locations and user needs. These efforts help address issues like water management for irrigation, crop-type identification and land use, coastal and lake water quality monitoring, drought preparedness, and famine early warnings. Much of this work is carried out and supported fully or in part by the agency's Applied Sciences Program, which works with individuals and institutions worldwide to inform decision-making, enhance quality of life, and strengthen the economy. The Applied Sciences Program co-leads the international Earth Observations for Sustainable Development Goals initiative, which advances global knowledge about effective ways that Earth observations and geospatial information can support the SDGs. The NASA datasets and resources listed below, coupled with other data and resources in this Data Pathfinder, also help measure progress toward meeting United Nations' Sustainable Development Goals (SDGs), particularly SDG 2: Zero Hunger. These data can provide a better overall view for monitoring the food insecurity of vulnerable populations, tracking agricultural production related to incomes of small-scale food producers, and monitoring environmental impacts to soil, water, fertilizer, pesticide pollution, and changes in biodiversity. More information is available in the Connection of Sustainable Development Goals to Agriculture and Water Management section on the main Pathfinder landing page. Agriculture and Human Dimensions Agriculture and Food Security theme landing page Global Agricultural Inputs, v1 The five datasets in this data collection provide global gridded data and maps on pesticide application, phosphorus in manure and chemical fertilizers, and nitrogen in manure and chemical fertilizers Global Pesticide Grids (PEST-CHEMGRIDS), v1.01 (2015, 2020, 2025) Global coverage; 5 arc-min spatial resolution; GeoTIFF, netCDF-4 Web Map Service Layers Food Supply Effects of Climate Change on Global Food Production from SRES Emissions and Socioeconomic Scenarios, v1 (1970 -- 2080) Global coverage; national resolution; .xlsx Web Map Service Layers Food Insecurity Hotspots Data Set, v1 (2009 -- 2019) Global coverage; national resolution; GeoTIFF, Shapefile Web Map Service Layers Groundswell Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, v1 (2010 -- 2050) Allows users to understand how slow-onset climate change impacts on water availability and crop productivity, coupled with sea-level rise and storm surge, may affect the future population distribution and climate-related internal migration in low to middle income countries Crop Production Twentieth Century Crop Statistics, v1 (1900 -- 2017) Global coverage (selected countries); national/sub-national resolution; annual Global Population Projection Grid Data Groundswell Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, v1 (2010 -- 2050) Climate Change Impact Effects of Climate Change on Global Food Production from SRES Emissions and Socioeconomic Scenarios, v1 (1970 -- 2080) Global coverage; national resolution; .xlsx Web Map Service Layers Groundswell Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, v1 (2010 -- 2050) Environmental Performance 2022 Environmental Performance Index Global coverage; national resolution; .xlsx, csv 15 static maps Poverty-related Data Humidity is a measure of the amount of water vapor present in the air. High humidity impairs heat exchange efficiency by reducing the rate of moisture evaporation from the skin and other surfaces. This can create challenges for agricultural workers, as well as the crops they grow. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns as well as anomalies. There are several options available: 1-hourly, 3-hourly, 6-hourly, daily, and monthly. These options provide information on precipitation. The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of high-resolution, bias-corrected global downscaled climate projections derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across all four "Tier 1" greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). This dataset provides a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions. Uses include: air temperature, precipitation volume, humidity, stellar radiation, and atmospheric wind speed. The atmosphere is a mixture of gases that surrounds the Earth. It helps make life possible by providing us with air to breathe, shielding us from harmful ultraviolet (UV) radiation coming from the Sun, trapping heat to warm the planet, and preventing extreme temperature differences between day and night. Without the atmosphere, temperatures would be well below freezing everywhere on Earth's surface. Instead, the heat absorbed and trapped by our atmosphere keeps our planet's average surface temperature at a balmy 15°C (59°F). Some of the atmosphere's gases, like carbon dioxide, are particularly good at absorbing and trapping radiation. Changes in the amounts of these gases directly affect our climate. Gases in Earth's Atmosphere Each of the planets in our solar system has an atmosphere, but none of them have the same ratio of gases or layered structure as Earth's atmosphere. Nitrogen and oxygen are by far the most common gases in our atmosphere. Dry air is composed of about 78% nitrogen (N2) and about 21% oxygen (O2). The remaining less than 1% of the atmosphere is a mixture of gases, including argon (Ar) and carbon dioxide (CO2). The atmosphere also contains varying amounts of water vapor, on average about 1%. There are also many, tiny, solid or liquid particles, called aerosols, in the atmosphere. Aerosols can be made of dust, spores and pollen, salt from sea spray, volcanic ash, smoke, and pollutants introduced through human activity. Earth's Atmosphere Has Layers The atmosphere becomes thinner (less dense and lower in air pressure) the further it extends from the Earth's surface. It gradually gives way to the vacuum of space. There is no precise top of the atmosphere, but the area between 100-120 km (62-75 miles) above the Earth's surface is often considered the boundary between the atmosphere and space because the air is so thin here. However, there are measurable traces of atmospheric gases beyond this boundary, detectable for hundreds of kilometers/miles from Earth's surface. There are several unique layers in Earth's atmosphere. Each has characteristic temperatures, pressures, and phenomena. We live in the troposphere, the layer closest to Earth's surface, where most clouds are found and almost all weather occurs. Some jet aircraft fly in the next layer, the stratosphere, which contains the jet streams and a region called the ozone layer. The next layer, the mesosphere, is the coldest because the there are almost no air molecules there to absorb heat energy. There are so few molecules for light to refract off of that the sky also changes from blue to black in this layer. And farthest from the surface we have the thermosphere, which absorbs much of the harmful radiation that reaches Earth from the Sun, causing this layer to reach extremely high temperatures. Beyond the thermosphere is the exosphere, which represents the transition from Earth's atmosphere to space. Planetary Atmospheres Earth is not the only world with an atmosphere. Each of the planets - and even a few moons - in our solar system have an atmosphere. Some planets have active atmospheres with clouds, wind, rain and powerful storms. Scientists use light spectroscopy to observe the atmospheres of planets and moons in other solar systems . Each of the planets in our solar system has a uniquely structured atmosphere. The atmosphere of Mercury is extremely thin and is not very different from the vacuum of space. The gas giant planets in our solar system - Jupiter, Saturn, Uranus and Neptune - each have a thick, deep atmosphere. The smaller, rocky planets - Earth, Venus and Mars - each have thinner atmospheres, hovering above their solid surfaces. The moons in our solar system typically have thin atmospheres, with the exception of Saturn's moon, Titan. Air pressure at the surface of Titan is higher than on Earth! Of the five officially recognized dwarf planets, Pluto has a thin atmosphere that expands and collapses seasonally, and Ceres has an extremely thin and transient atmosphere made of water vapor. But only Earth's atmosphere has the layered structure that traps enough of the Sun's energy for warmth while also blocking much of the harmful radiation from the Sun. This important balance is necessary to maintain life on Earth. Forests are one of the world's largest banks of carbon-rich biomass. This is why when researchers mapped biomass in the past, they typically focused on the world's forests. But this approach leaves out considerable amounts of biomass in grasslands, shrublands, croplands, and other biomes. New maps, published at NASA's Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) and described in Nature Scientific Data, combine remotely sensed biomass data for different land cover types into harmonized global maps of above and belowground biomass for the year 2010. People often conflate forest biomass with total biomass," said Seth Spawn, lead author of the research and doctoral candidate at the University of Wisconsin, Madison. "Researchers have spent a lot of time developing nice remotely sensed maps of aboveground forest biomass, but they intentionally omit other land covers and the carbon stored below ground in plant roots. We haven't had the whole picture." Spawn and his team combined maps of forest biomass with other land cover specific biomass maps that use remotely sensed data. They allocated fractional contributions to a given grid cell using data on land cover, percent tree cover, and the presence of secondary vegetation. These maps show a sizable stock of biomass outside of forested areas, especially in the trees located in savannas and farm fields. "There's more carbon on croplands than I would have expected," said Spawn. Trees on orchards and farms practicing agroforestry have a carbon stock that is overlooked in previous biomass maps. Globally, trees on croplands stored about 28 metric gigatons of carbon in 2010, which is 7 percent of the total stock of carbon in plants, according to these new maps. The team produced maps of uncertainty with the data they used and published them along with the dataset. Published in March, the dataset is already being used for a number of applications. Researchers are using the dataset in integrated assessment models that incorporate economics and the Earth system. It is also being used to model carbon emissions from past and potential land use changes and the carbon impacts of bioenergy transitions. Water is a key component of the overall Earth system, cycling through each component, moving within the atmosphere, the ocean, the cryosphere (including snow cover and snowpack), surface water of rivers and lakes, and subsurface water. Water availability is critical for human consumption, agriculture and food security, industry, and energy development. Assessing water availability, including the amount and type of precipitation is critical to monitoring agricultural practices and water resource availability and for providing interventions when necessary. According to the U.N., water use has been growing globally at twice the rate as the global population is increasing. More and more areas are reaching the limit at which water services can be sustainably delivered, especially in arid regions. Groundwater, a major water resource for maintaining cropland productivity, is declining through the extensive use of water for agricultural irrigation, where aquifer recharge cannot keep up with groundwater extraction. Unfortunately, changes in terrestrial water storage, especially with regard to groundwater, are poorly known and sparsely sampled. Complicating matters further, global freshwater is not only unevenly distributed, but sources of freshwater such as lakes and rivers often cross geopolitical boundaries. Integrating satellite data with land-based and other measurements, geospatial data, and hydrologic models help to better understand controls on global water resources and how changing water resources impact social-environmental systems across geopolitical boundaries. Earth Observation Data by Sensor According to the U.N., water use has been growing globally at twice the rate as the global population is increasing. More and more areas are reaching the limit at which water services can be sustainably delivered, especially in arid regions. Groundwater, a major water resource for maintaining cropland productivity, is declining through the extensive use of water for agricultural irrigation, where aquifer recharge cannot keep up with groundwater extraction. Unfortunately, changes in terrestrial water storage, especially with regard to groundwater, are poorly known and sparsely sampled. Complicating matters further, global freshwater is not only unevenly distributed, but sources of freshwater such as lakes and rivers often cross geopolitical boundaries. Integrating satellite data with land-based and other measurements, geospatial data, and hydrologic models help to better understand controls on global water resources and how changing water resources impact social-environmental systems across geopolitical boundaries. Earth Observation Data by Sensor GRACE, GRACE-FO Instruments aboard the joint NASA/German Space Agency Gravity Recovery And Climate Experiment (GRACE, operational 2002 to 2017) and GRACE Follow-On (GRACE-FO, launched in 2018) satellites obtain measurements about changes in Earth's gravity. Since water has mass, changes in groundwater storage can be detected as changes in gravity. GRACE and GRACE-FO measurements help assess water storage changes in monthly, total surface, and groundwater depth. These data are available from 2002 to present; the data track total water storage time-variations and anomalies (changes from the time-mean) at a resolution of approximately 90,000 km2 and larger. These measurements are unimpeded by clouds and track the entire land water column from the surface down to deep aquifers. GRACE and GRACE-FO data are uniquely valuable for regional studies to determine general trends in land water storage as well as for assessing basin-scale water budgets (e.g., the balance between precipitation, evapotranspiration, and runoff). GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height dataset provides gridded monthly global water storage/height anomalies relative to a time-mean. The data are processed at NASA's Jet Propulsion Laboratory (JPL) using the mascon approach. Mass Concentration blocks (mascons) are a form of gravity field basis functions to which GRACE observations are optimally fit. For more information on this approach, see the JPL Monthly Mass Grids webpage. Data are represented as Water Equivalent Thickness (WET), representing the total terrestrial water storage anomalies from soil moisture, snow, surface water (including rivers, lakes, and reservoirs), as well as groundwater and aquifers. Scientists at NASA's Goddard Space Flight Center use GRACE-FO data to generate weekly groundwater and soil moisture drought indicators. The drought indicators describe current wet or dry conditions, expressed as a percentile showing the probability of occurrence for a specific location and time of year, with lower values (orange/red) indicating drier than normal conditions and higher values (blues) indicating wetter than normal conditions. The drought model is also used to make forecasts of expected drought conditions one, two, and three months into the future. NASA, in collaboration with other agencies, has developed models of groundwater that incorporate satellite information with ground-based data (when ground-based data are available). These models are part of the Land Data Assimilation System (LDAS), which includes a global collection (GLDAS) and a North American collection (NLDAS). NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) optimally reorganized some large hydrological datasets as time series (also known as data rods) for a set of water cycle-related variables from the NLDAS and GLDAS, the Land Parameter Parameter Model (LPRM), TRMM, and GRACE data assimilation. These are available at GES DISC Hydrology Data Rods. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provides data beginning in 1980. Due to the amount of historical data available, MERRA-2 data can be used to look for trends and patterns, as well as anomalies. There are several options available: hourly and monthly from 1980. Remote sensing is the acquiring of information from a distance. NASA observes Earth and other planetary bodies via remote sensors on satellites and aircraft that detect and record reflected or emitted energy. Remote sensors, which provide a global perspective and a wealth of data about Earth systems, enable data-informed decision making based on the current and future state of our planet. Satellites can be placed in several types of orbits around Earth. The three common classes of orbits are low-Earth orbit (approximately 160 to 2,000 km above Earth), medium-Earth orbit (approximately 2,000 to 35,500 km above Earth), and high-Earth orbit (above 35,500 km above Earth). Satellites orbiting at 35,786 km are at an altitude at which their orbital speed matches the planet's rotation, and are in what is called geosynchronous orbit (GSO). In addition, a satellite in GSO directly over the equator will have a geostationary orbit. A geostationary orbit enables a satellite to maintain its position directly over the same place on Earth's surface. Aqua satellite orbit illustrating polar orbital track. NASA's Aqua satellite completes one orbit every 99 minutes and passes within 10 degrees of each pole. This enables the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua to acquire full global imagery every 1-2 days. Credit: NASA Aqua. Low-Earth orbit is a commonly used orbit since satellites can follow several orbital tracks around the planet. Polar-orbiting satellites, for example, are inclined nearly 90 degrees to the equatorial plane and travel from pole to pole as Earth rotates. This enables sensors aboard the satellite to acquire data for the entire globe rapidly, including the polar regions. Many polar-orbiting satellites are considered Sun-synchronous, meaning that the satellite passes over the same location at the same solar time each cycle. One example of a Sun-synchronous, polar-orbiting satellite is NASA's Aqua satellite, which orbits approximately 705 km above Earth's surface. Non-polar low-Earth orbit satellites, on the other hand, do not provide global coverage but instead cover only a partial range of latitudes. The joint NASA/Japan Aerospace Exploration Agency Global Precipitation Measurement (GPM) Core Observatory is an example of a non-Sun-synchronous low-Earth orbit satellite. Its orbital track acquires data between 65 degrees north and south latitude from 407 km above the planet. A medium-Earth orbit satellite takes approximately 12 hours to complete an orbit. In 24-hours, the satellite crosses over the same two spots on the equator every day. This orbit is consistent and highly predictable. As a result, this is an orbit used by many telecommunications and GPS satellites. One example of a medium-Earth orbit satellite constellation is the European Space Agency's Galileo global navigation satellite system (GNSS), which orbits 23,222 km above Earth. While both geosynchronous and geostationary satellites orbit at 35,786 km above Earth, geosynchronous satellites have orbits that can be tilted above or below the equator. Geostationary satellites, on the other hand, orbit Earth on the same plane as the equator. These satellites capture identical views of Earth with each observation and provide almost continuous coverage of one area. The joint NASA/NOAA Geostationary Operational Environmental Satellite (GOES) series of weather satellites are in geostationary orbits above the equator. Observing with the Electromagnetic Spectrum Electromagnetic energy, produced by the vibration of charged particles, travels in the form of waves through the atmosphere and the vacuum of space. These waves have different wavelengths (the distance from wave crest to wave crest) and frequencies; a shorter wavelength means a higher frequency. Some, like radio, microwave, and infrared waves, have a longer wavelength, while others, such as ultraviolet, x-rays, and gamma rays, have a much shorter wavelength. Visible light sits in the middle of that range of long to shortwave radiation. This small portion of energy is all that the human eye is able to detect. Instrumentation is needed to detect all other forms of electromagnetic energy. NASA instrumentation utilizes the full range of the spectrum to explore and understand processes occurring here on Earth and on other planetary bodies. Some waves are absorbed or reflected by atmospheric components, like water vapor and carbon dioxide, while some wavelengths allow for unimpeded movement through the atmosphere; visible light has wavelengths that can be transmitted through the atmosphere. Microwave energy has wavelengths that can pass through clouds, an attribute utilized by many weather and communication satellites. The primary source of the energy observed by satellites, is the Sun. The amount of the Sun's energy reflected depends on the roughness of the surface and its albedo, which is how well a surface reflects light instead of absorbing it. Snow, for example, has a very high albedo and reflects up to 90% of incoming solar radiation. The ocean, on the other hand, reflects only about 6% of incoming solar radiation and absorbs the rest. Often, when energy is absorbed, it is re-emitted, usually at longer wavelengths. For example, the energy absorbed by the ocean gets re-emitted as infrared radiation. All things on Earth reflect, absorb, or transmit energy, the amount of which varies by wavelength. Just as your fingerprint is unique to you, everything on Earth has a unique spectral fingerprint. Researchers can use this information to identify different Earth features as well as different rock and mineral types. The number of spectral bands detected by a given instrument, its spectral resolution, determines how much differentiation a researcher can identify between materials. Drought, vegetation health, and soil moisture all can be tracked remotely. This Data Pathfinder provides links to NASA Earth observations, tools, and other resources applicable to agricultural production and water management. The planet NASA studies the most is Earth. NASA's end-to-end Earth observations enable agricultural producers to make informed decisions about global market conditions, water management, in-season crop conditions, severe weather, and sustainability. This Data Pathfinder will help guide you through the process of selecting and using datasets applicable to agriculture and water management, and provides links to specific data sources. If you are new to remote sensing, the What is Remote Sensing? Backgrounder provides a comprehensive overview. In addition, NASA's Applied Remote Sensing Training Program (ARSET) provides numerous training What's big, covered in water, yet 100 times drier than the Sahara Desert? It's not a riddle, it's the Moon! For centuries, astronomers debated whether water exists on Earth's closest neighbor. In 2020, data from NASA's SOFIA mission confirmed water exists in the sunlit area of the lunar surface as molecules of H2O embedded within, or perhaps sticking to the surface of, grains of lunar dust. Here is a brief history of the discoveries leading up to the confirmation of water on the Moon. When early astronomers looked up at the Moon, they were struck by the large, dark spots on its surface. In 1645, Dutch astronomer Michael van Langren published the first-known map of the Moon referring to the dark spots as "maria" -- the Latin word for "seas" -- and putting into writing the widely-held view that the marks were oceans on the lunar surface. Similar maps from Johannes Hevelius (1647), Giovanni Riccioli and Francesco Grimaldi (1651) were published over the next few years. We now know these spots to be plains of basalt created by early volcanic eruptions, but the nomenclature of 'maria' (plural) or 'mare' (singular) remains. American astronomer William Pickering made measurements in the late 1800s that led him to conclude the Moon essentially has no atmosphere. With no clouds and no atmosphere, scientists generally agreed that any water on the lunar surface would evaporate immediately. Pickering's measurements led to a widespread view that the Moon was devoid of water. As scientists made headway in understanding the behavior of substances that are prone to vaporize at relatively low temperatures -- called volatiles -- theoretical physicist Kenneth Watson published a paper in 1961 describing how a substance like water could exist on the Moon. Watson's paper first popularized the idea that water ice could stick to the bottom of craters on the Moon that never receive light from the Sun, while sunlit areas on the Moon would be so hot that water would evaporate near-instantly. These lightless areas of the Moon are called "permanently shadowed regions." The Apollo era brought humans to the lunar surface for the very first time, giving researchers the opportunity to directly look for signs of water on the Moon. When tested, soil samples brought back by Apollo astronauts revealed no sign of water. Scientists concluded that the lunar surface must be completely dry, and the prospect of water wasn't seriously considered again for decades. NASA's Clementine mission launched in 1994 to orbit the Moon for two months and collect information about its minerals. Clementine data suggested there was ice in a permanently shadowed region of the Moon. The Lunar Prospector Mission focused on permanently shadowed craters to look deeper into the discovery and in 1998 found that the largest concentrations of hydrogen exist in the areas of the lunar surface that are never exposed to sunlight. The results indicated water ice at the lunar poles. However, the images were low resolution so no strong conclusions could be made. Capitalizing on major advances in technology since the Apollo era, researchers from Brown University revisited the Apollo samples. They found hydrogen inside tiny beads of volcanic glass. Since no volcanoes are erupting on the Moon today, the discovery presented evidence that water had existed in the Moon when the volcanoes erupted in the Moon's ancient past. Additionally, the preserved hydrogen provided clues to the origins of lunar water: if it emerged from erupting volcanoes, it must have come from within the Moon. The discovery suggested that water was a part of the Moon since its early existence -- and perhaps since it first formed. A suite of spacecraft enabled exciting discoveries in 2009. None were designed to look for water on the Moon, yet the Indian Space Research Organization's Chandrayaan-1 and NASA's Cassini and Deep Impact missions detected signs of hydrated minerals in the form of oxygen and hydrogen molecules in sunlit areas of the Moon. Researchers couldn't determine whether they were seeing hydration by hydroxyl (OH) or water (H2O). They also debated whether the amount of hydration depended on the time of day. The Lunar Crater Observation and Sensing Satellite (LCROSS) spacecraft and Lunar Reconnaissance Orbiter (LRO) launched together in 2009. Later that year, LCROSS intentionally discharged a projectile into a crater believed to contain water ice, and flew through the debris from the projectile's impact. Four minutes later, LCROSS itself intentionally impacted the Moon while LRO observed. The combined observations showed grains of water ice in the ejected material. The LRO and LCROSS findings added to a growing body of evidence that water exists on the Moon in the form of ice within permanently shadowed regions. LRO continues to orbit the Moon and provide data used to characterize and map lunar resources, including hydrogen. Data from Moon Mineralogy Mapper (M3), carried by ISRO's Chandrayaan-1, provided scientists with the first high-resolution map of the minerals that make up the lunar surface. The NASA instrument flew aboard India's Chandrayaan-1 mission in 2009. An analysis of the full set of data from M3, announced in 2018, revealed multiple confirmed locations of water ice in permanently shadowed regions of the Moon. In 2020, NASA announced the discovery of water on the sunlit surface of the Moon. Data from the Strategic Observatory for Infrared Astronomy (SOFIA), revealed that in Clavius crater, water exists in concentrations roughly equivalent to a 12-ounce bottle of water within a cubic meter of soil across the lunar surface. The discovery showed that water could be distributed across the lunar surface, even on sunlit portions, and not confined to cold, dark areas. In 2023, a new map of water distribution on the Moon provided hints about how water may be moving across the Moon's surface. The map, made using SOFIA data, extends to the Moon's South Pole -- the intended region of study for NASA's Artemis missions, including the water-hunting rover, VIPER. Researchers have confirmed that water exists both in the sunlit and shadowed surfaces of the Moon, yet many questions remain. Lunar scientists continue to investigate the origins of water and its behavior. There is evidence that the water on the Moon comes from ancient and current comet impacts, icy micrometeorites colliding on the lunar surface, and lunar dust interactions with the solar wind. However, more research is needed to understand the full history, present, and future of water on the Moon. Writer: Allison Gasparini and Molly Wasser`\nScience `{=tex}Advisors: Casey Honniball, Tim Livengood, NASA's Goddard Space Flight Center In 2019, scientists discovered that water is being released from the Moon during meteor showers. Water on the Moon could come from a surprising source, our Sun. In 2020, NASA scientists confirmed the presence of H2O on the Moon. Viewed from space, one of the most striking features of our home planet is the water, in both liquid and frozen forms, that covers approximately 75% of the Earth's surface. Geologic evidence suggests that large amounts of water have likely flowed on Earth for the past 3.8 billion years---most of its existence. Believed to have initially arrived on the surface through the emissions of ancient volcanoes, water is a vital substance that sets the Earth apart from the rest of the planets in our solar system. In particular, water appears to be a necessary ingredient for the development and nourishment of life. Water is practically everywhere on Earth. Moreover, it is the only known substance that can naturally exist as a gas, a liquid, and solid within the relatively small range of air temperatures and pressures found at the Earth's surface. In all, the Earth's water content is about 1.39 billion cubic kilometers (331 million cubic miles), with the bulk of it, about 96.5%, being in the global oceans. As for the rest, approximately 1.7% is stored in the polar icecaps, glaciers, and permanent snow, and another 1.7% is stored in groundwater, lakes, rivers, streams, and soil. Only a thousandth of 1% of the water on Earth exists as water vapor in the atmosphere. Despite its small amount, this water vapor has a huge influence on the planet. Water vapor is a powerful greenhouse gas, and it is a major driver of the Earth's weather and climate as it travels around the globe, transporting latent heat with it. Latent heat is heat obtained by water molecules as they transition from liquid or solid to vapor; the heat is released when the molecules condense from vapor back to liquid or solid form, creating cloud droplets and various forms of precipitation. For human needs, the amount of freshwater on Earth---for drinking and agriculture---is particularly important. Freshwater exists in lakes, rivers, groundwater, and frozen as snow and ice. Estimates of groundwater are particularly difficult to make, and they vary widely. (The value in the above table is near the high end of the range.) Groundwater may constitute anywhere from approximately 22 to 30% of fresh water, with ice (including ice caps, glaciers, permanent snow, ground ice, and permafrost) accounting for most of the remaining 78 to 70%. The water, or hydrologic, cycle describes the pilgrimage of water as water molecules make their way from the Earth's surface to the atmosphere and back again, in some cases to below the surface. This gigantic system, powered by energy from the Sun, is a continuous exchange of moisture between the oceans, the atmosphere, and the land. Studies have revealed that evaporation---the process by which water changes from a liquid to a gas---from oceans, seas, and other bodies of water (lakes, rivers, streams) provides nearly 90% of the moisture in our atmosphere. Most of the remaining 10% found in the atmosphere is released by plants through transpiration. Plants take in water through their roots, then release it through small pores on the underside of their leaves. In addition, a very small portion of water vapor enters the atmosphere through sublimation, the process by which water changes directly from a solid (ice or snow) to a gas. The gradual shrinking of snow banks in cases when the temperature remains below freezing results from sublimation. Together, evaporation, transpiration, and sublimation, plus volcanic emissions, account for almost all the water vapor in the atmosphere that isn't inserted through human activities. While evaporation from the oceans is the primary vehicle for driving the surface-to-atmosphere portion of the hydrologic cycle, transpiration is also significant. For example, a cornfield 1 acre in size can transpire as much as 4,000 gallons of water every day. After the water enters the lower atmosphere, rising air currents carry it upward, often high into the atmosphere, where the air is cooler. In the cool air, water vapor is more likely to condense from a gas to a liquid to form cloud droplets. Cloud droplets can grow and produce precipitation (including rain, snow, sleet, freezing rain, and hail), which is the primary mechanism for transporting water from the atmosphere back to the Earth's surface. When precipitation falls over the land surface, it follows various routes in its subsequent paths. Some of it evaporates, returning to the atmosphere; some seeps into the ground as soil moisture or groundwater; and some runs off into rivers and streams. Almost all of the water eventually flows into the oceans or other bodies of water, where the cycle continues. At different stages of the cycle, some of the water is intercepted by humans or other life forms for drinking, washing, irrigating, and a large variety of other uses. Groundwater is found in two broadly defined layers of the soil, the "zone of aeration," where gaps in the soil are filled with both air and water, and, further down, the "zone of saturation," where the gaps are completely filled with water. The boundary between these two zones is known as the water table, which rises or falls as the amount of groundwater changes. The amount of water in the atmosphere at any moment in time is only 12,900 cubic kilometers, a minute fraction of Earth's total water supply: if it were to completely rain out, atmospheric moisture would cover the Earth's surface to a depth of only 2.5 centimeters. However, far more water---in fact, some 495,000 cubic kilometers of it---are cycled through the atmosphere every year. It is as if the entire amount of water in the air were removed and replenished nearly 40 times a year. Water continually evaporates, condenses, and precipitates, and on a global basis, evaporation approximately equals precipitation. Because of this equality, the total amount of water vapor in the atmosphere remains approximately the same over time. However, over the continents, precipitation routinely exceeds evaporation, and conversely, over the oceans, evaporation exceeds precipitation. In the case of the oceans, the continual excess of evaporation versus precipitation would eventually leave the oceans empty if they were not being replenished by additional means. Not only are they being replenished, largely through runoff from the land areas, but over the past 100 years, they have been over-replenished: sea level around the globe has risen approximately 17 centimeters over the course of the twentieth century. Sea level has risen both because of warming of the oceans, causing water to expand and increase in volume, and because more water has been entering the ocean than the amount leaving it through evaporation or other means. A primary cause for increased mass of water entering the ocean is the calving or melting of land ice (ice sheets and glaciers). Sea ice is already in the ocean, so increases or decreases in the annual amount of sea ice do not significantly affect sea level. Throughout the hydrologic cycle, there are many paths that a water molecule might follow. Water at the bottom of Lake Superior may eventually rise into the atmosphere and fall as rain in Massachusetts. Runoff from the Massachusetts rain may drain into the Atlantic Ocean and circulate northeastward toward Iceland, destined to become part of a floe of sea ice, or, after evaporation to the atmosphere and precipitation as snow, part of a glacier. Water molecules can take an immense variety of routes and branching trails that lead them again and again through the three phases of ice, liquid water, and water vapor. For instance, the water molecules that once fell 100 years ago as rain on your great- grandparents' farmhouse in Iowa might now be falling as snow on your driveway in California. Among the most serious Earth science and environmental policy issues confronting society are the potential changes in the Earth's water cycle due to climate change. The science community now generally agrees that the Earth's climate is undergoing changes in response to natural variability, including solar variability, and increasing concentrations of greenhouse gases and aerosols. Furthermore, agreement is widespread that these changes may profoundly affect atmospheric water vapor concentrations, clouds, precipitation patterns, and runoff and stream flow patterns. For example, as the lower atmosphere becomes warmer, evaporation rates will increase, resulting in an increase in the amount of moisture circulating throughout the troposphere (lower atmosphere). An observed consequence of higher water vapor concentrations is the increased frequency of intense precipitation events, mainly over land areas. Furthermore, because of warmer temperatures, more precipitation is falling as rain rather than snow. In parts of the Northern Hemisphere, an earlier arrival of spring-like conditions is leading to earlier peaks in snowmelt and resulting river flows. As a consequence, seasons with the highest water demand, typically summer and fall, are being impacted by a reduced availability of fresh water. Warmer temperatures have led to increased drying of the land surface in some areas, with the effect of an increased incidence and severity of drought. The Palmer Drought Severity Index, which is a measure of soil moisture using precipitation measurements and rough estimates of changes in evaporation, has shown that from 1900 to 2002, the Sahel region of Africa has been experiencing harsher drought conditions. This same index also indicates an opposite trend in southern South America and the south central United States. While the brief scenarios described above represent a small portion of the observed changes in the water cycle, it should be noted that many uncertainties remain in the prediction of future climate. These uncertainties derive from the sheer complexity of the climate system, insufficient and incomplete data sets, and inconsistent results given by current climate models. However, state of the art (but still incomplete and imperfect) climate models do consistently predict that precipitation will become more variable, with increased risks of drought and floods at different times and places. Orbiting satellites are now collecting data relevant to all aspects of the hydrologic cycle, including evaporation, transpiration, condensation, precipitation, and runoff. NASA even has one satellite, Aqua, named specifically for the information it is collecting about the many components of the water cycle. Aqua launched on May 4, 2002, with six Earth-observing instruments: the Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), the Humidity Sounder for Brazil (HSB), the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Moderate Resolution Imaging Spectroradiometer (MODIS), and Clouds and the Earth's Radiant Energy System (CERES). Since water vapor is the Earth's primary greenhouse gas, and it contributes significantly to uncertainties in projections of future global warming, it is critical to understand how it varies in the Earth system. In the first years of the Aqua mission, AIRS, AMSU, and HSB provided space-based measurements of atmospheric temperature and water vapor that were more accurate than any obtained before; the sensors also made measurements from more altitudes than any previous sensor. The HSB is no longer operational, but the AIRS/AMSU system continues to provide high-quality atmospheric temperature and water vapor measurements. More recent studies using AIRS data have demonstrated that most of the warming caused by carbon dioxide does not come directly from carbon dioxide, but rather from increased water vapor and other factors that amplify the initial warming. Other studies have shown improved estimation of the landfall of a hurricane in the Bay of Bengal by incorporating AIRS temperature measurements, and improved understanding of large-scale atmospheric patterns such as the Madden-Julian Oscillation. In addition to their importance to our weather, clouds play a major role in regulating Earth's climate system. MODIS, CERES, and AIRS all collect data relevant to the study of clouds. The cloud data include the height and area of clouds, the liquid water they contain, and the sizes of cloud droplets and ice particles. The size of cloud particles affects how they reflect and absorb incoming sunlight, and the reflectivity (albedo) of clouds plays a major role in Earth's energy balance. One of the many variables AMSR-E monitors is global precipitation. The sensor measures microwave energy, some of which passes through clouds, and so the sensor can detect the rainfall even under the clouds. Water in the atmosphere is hardly the only focus of the Aqua mission. Among much else, AMSR-E and MODIS are being used to study sea ice. Sea ice is important to the Earth system not just as an important element in the habitat of polar bears, penguins, and some species of seals, but also because it can insulate the underlying liquid water against heat loss to the often frigid overlying polar atmosphere and because it reflects sunlight that would otherwise be available to warm the ocean. When it comes to sea ice, AMSR-E and MODIS provide complementary information. AMSR-E doesn't record as much detail about ice features as MODIS does, but it can distinguish ice versus open water even when it is cloudy. The AMSR-E measurements continue, with improved resolution and accuracy, a satellite record of changes in the extent of polar ice that extends back to the 1970s. AMSR-E and MODIS also provide monitoring of snow coverage over land, another key indicator of climate change. As with sea ice, AMSR-E allows routine monitoring of the snow, irrespective of cloud cover, but with less spatial detail, while MODIS sees greater spatial detail, but only under cloud-free conditions. As for liquid water on land, AMSR-E provides information about soil moisture, which is crucial for vegetation including agricultural crops. AMSR-E's monitoring of soil moisture globally permits, for example, the early identification of signs of drought. Aqua is the most comprehensive of NASA's water cycle missions, but it isn't alone. In fact, the Terra satellite also has MODIS and CERES instruments onboard, and several other spacecraft have made or are making unique water-cycle measurements. The Ice, Cloud, and Land Elevation Satellite (ICESat) was launched in January 2003, and it collected data on the topography of the Earth's ice sheets, clouds, vegetation, and the thickness of sea ice off and on until October 2009. A new ICESat mission, ICESat-2, is now under development and is scheduled to launch in 2015. The Gravity Recovery and Climate Experiment (GRACE) is a unique mission that consists of two spacecraft orbiting one behind the other; changes in the distance between the two provide information about the gravity field on the Earth below. Because gravity depends on mass, some of the changes in gravity over time signal a shift in water from one place on Earth to another. Through measurements of changing gravity fields, GRACE scientists are able to derive information about changes in the mass of ice sheets and glaciers and even changes in groundwater around the world. CloudSat is advancing scientists' understanding of cloud abundance, distribution, structure, and radiative properties (how they absorb and emit energy, including thermal infrared energy escaping from Earth's surface). Since 2006, CloudSat has flown the first satellite-based, millimeter-wavelength cloud radar---an instrument that is 1000 times more sensitive than existing weather radars on the ground. Unlike ground-based weather radars that use centimeter wavelengths to detect raindrop-sized particles, CloudSat's radar allows the detection of the much smaller particles of liquid water and ice in the large cloud masses that contribute significantly to our weather. The joint NASA and French Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) is providing new insight into the role that clouds and atmospheric aerosols (particles like dust and pollution) play in regulating Earth's weather, climate, and air quality. CALIPSO combines an active laser instrument with passive infrared and visible imagers to probe the vertical structure and properties of thin clouds and aerosols over the globe. July through October fall within the dry season in the western and northern Amazon rainforest, but a particularly acute lack of rain during this period in 2023 has pushed the region into a severe drought. The OLI (Operational Land Imager) instrument on Landsat 8 captured this image (right) of the parched Rio Negro in the Brazilian province of Amazonas near the city of Manaus on October 3, 2023. On that date, the level of the river, the largest tributary of the Amazon River, had dropped to 15.14 meters (50.52 feet), according to data collected by the Port of Manaus. For comparison, the image on the left shows the same area on October 8, 2022, when the water level was 19.59 meters, a more typical level for October. Rio Negro water levels continued to drop in the days after the image was collected, reaching a record low of 13.49 meters on October 17, 2023. Some areas in the Amazon River's watershed have received less rain between July and September than any year since 1980, Reuters reported. The drought has been particularly severe in the Rio Negro watershed in northern Amazonas, as well as parts of southern Venezuela and southern Colombia. "Overall, this is a pretty unusual and extreme situation," said René Garreaud, an atmospheric scientist at the University of Chile. "The primary culprit exacerbating the drought appears to be El Niño." This cyclical warming of surface waters in the central-eastern Pacific functions somewhat like a boulder in the middle of a stream, disrupting atmospheric circulation patterns in ways that lead to wetter conditions over the equatorial Pacific and drier conditions over the Amazon Basin. According to news outlets, the low river water levels on the Rio Negro and other nearby rivers have disrupted drinking water supplies in hundreds of communities, slowed commercial navigation, and led to fish and dolphin die-offs. Manaus, the capital and largest city of the Brazilian state of Amazonas, is the primary transportation hub for the upper Amazon, serving as an important transit point for soap, beef, and animal hides. Other industries with a presence in the city of two million people include chemical, ship, and electrical equipment manufacturing. After rapidly growing in volume just a few years earlier, northwest Iran's Lake Urmia nearly dried out in autumn 2023. The largest lake in the Middle East and one of the largest hypersaline lakes on Earth at its greatest extent, Lake Urmia has for the most part transformed into a vast, dry salt flat. It stands in contrast to the image from three years earlier (left), acquired by the OLI on Landsat 8 on September 8, 2020, when water filled most of the basin and salt deposits were only visible around the perimeter of the lake. The replenishment followed a period of above-average precipitation that sent a surge of freshwater into the basin, expanding its watery footprint. Drier conditions have since brought levels back down. The longer-term trend for Urmia has been one toward drying. In 1995, Lake Urmia reached a high-water mark; then in the ensuing two decades, the lake level dropped more than 7 meters (23 feet) and lost approximately 90 percent of its area. Consecutive droughts, agricultural water use, and dam construction on rivers feeding the lake have contributed to the decline. A shrinking Lake Urmia has implications for ecological and human health. The lake, its islands, and surrounding wetlands comprise valuable habitat and are recognized as a UNESCO Biosphere Reserve, Ramsar site, and national park. The area provides breeding grounds for waterbirds such as flamingos, white pelicans, and white-headed ducks, as well as a stopover for migratory species. However, with low lake levels, what water remains becomes more saline and taxes the populations of brine shrimp and other food sources for larger animals. A shrinking lake also increases the likelihood of dust from the exposed lakebed becoming swept up by winds and degrading air quality. Recent studies have linked the low water levels in Lake Urmia with respiratory health impacts among the local population. The relative effects of climate, water usage, and dams on Lake Urmia's water level is a topic of debate. The lake did see some recovery during a 10-year restoration program beginning in 2013. However, the efficacy of that effort has been difficult to parse since strong rains also fell during that period. Some research has concluded that climatic factors were primarily responsible for the recovery. The deep-blue sea is turning a touch greener. While that may not seem as consequential as, say, record warm sea surface temperatures, the color of the ocean surface is indicative of the ecosystem that lies beneath. Communities of phytoplankton, microscopic photosynthesizing organisms, abound in near-surface waters and are foundational to the aquatic food web and carbon cycle. This shift in the water's hue confirms a trend expected under climate change and signals changes to ecosystems within the global ocean, which covers 70 percent of Earth's surface. Researchers led by B. B. Cael, a principal scientist at the U.K.'s National Oceanography Centre, revealed that 56 percent of the global sea surface has undergone a significant change in color in the past 20 years. After analyzing ocean color data from the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on NASA's Aqua satellite, they found that much of the change stems from the ocean turning more green. The map above highlights the areas where ocean surface color changed between 2002 and 2022, with darker shades of green representing more-significant differences (higher signal-to-noise ratio). By extension, said Cael, "these are places we can detect a change in the ocean ecosystem in the last 20 years." The study focused on tropical and subtropical regions, excluding higher latitudes, which are dark for part of the year, and coastal waters, where the data are naturally very noisy. The black dots on the map indicate the area, covering 12 percent of the ocean's surface, where chlorophyll levels also changed over the study period. Chlorophyll has been the go-to measurement for remote sensing scientists to gauge phytoplankton abundance and productivity. However, those estimates use only a few colors in the visible light spectrum. The values shown in green are based on the whole gamut of colors and therefore capture more information about the ecosystem as a whole. A long time series from a single sensor is relatively rare in the remote sensing world. As the Aqua satellite was celebrating its 20th year in orbit in 2022---far exceeding its design life of 6 years---Cael wondered what long term trends could be discovered in the data. In particular, he was curious what might have been missed in all the ocean color information it had collected. "There's more encoded in the data than we actually make use of," he said. By going big with the data, the team discerned an ocean color trend that had been predicted by climate modeling, but one that was expected to take 30-40 years of data to detect using satellite-based chlorophyll estimates. That's because the natural variability in chlorophyll is high relative to the climate change trend. The new method, incorporating all visible light, was robust enough to confirm the trend in 20 years. At this stage, it is difficult to say what exact ecological changes are responsible for the new hues. However, the authors posit, they could result from different assemblages of plankton, more detrital particles, or other organisms such as zooplankton. It is unlikely the color changes come from materials such as plastics or other pollutants, said Cael, since they are not widespread enough to register at large scales. "What we do know is that in the last 20 years, the ocean has become more stratified," he said. Surface waters have absorbed excess heat from the warming climate, and as a result, they are less prone to mixing with deeper, more nutrient-rich layers. This scenario would favor plankton adapted to a nutrient-poor environment. The areas of ocean color change align well with where the sea has become more stratified, said Cael, but there is no such overlap with sea surface temperature changes. More insights into Earth's aquatic ecosystems may soon be on the way. NASA's PACE (Plankton, Aerosol, Cloud, ocean Ecosystem) satellite, set to launch in 2024, will return observations in finer color resolution. The new data will enable researchers to infer more information about ocean ecology, such as the diversity of phytoplankton species and the rates of phytoplankton growth. On September 10, 2023, a low-pressure storm brought heavy rains to northeastern Libya, causing deadly flooding and devastation in cities along the Mediterranean coast. On the coast of Libya's Cyrenaica region, Al Bayda recorded 414 millimeters (16 inches) of rain in one day. Nearby, the port city of Derna received more than 100 millimeters (4 inches) over the course of the storm---far exceeding the city's average monthly rainfall for September of less than 1.5 millimeters (0.1 inches). Derna lies at the end of a long, narrow valley, called a wadi, which is dry for most of the year. Floods triggered two dams along Wadi Derna to collapse, sending floodwater and mud to the city. According to news reports, floodwater swept away roads and entire neighborhoods. The images above show the Cyrenaica region before and after the storm. They are false color, which makes water (blue) stand out from the surroundings. The image on the right, acquired on September 13, shows water filling low-lying areas and wadis inland from the coast. The image on the left shows the same area on September 7. Both images were acquired with the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite. The flooding and damage in Derna is difficult to see at this resolution, although sediment flowing into the Mediterranean is visible in natural color images. In the days prior to making landfall in Libya, the same low-pressure storm (named Storm Daniel by the Hellenic National Meteorological Service) swamped parts of Greece, Turkey, and Bulgaria. As the storm approached Libya, it developed characteristics of a tropical-like cyclone, or "medicane," with winds measuring around 70 to 80 kilometers (43 to 50 miles) per hour. The natural-color image above, acquired with MODIS on NASA's Terra satellite, shows the storm on September 10 as it made landfall in northeastern Libya. Only one or two medicanes typically develop in a year, according to NOAA. As of September 13, authorities were still conducting search and rescue operations in the region. Derna was still largely inaccessible on that day, making it difficult to assess the full impact of the flood. Sea surface temperatures have a large influence on climate and weather. For example, every 3 to 7 years a wide swath of the Pacific Ocean along the equator warms by 2 to 3 degrees Celsius. This warming is a hallmark of the climate pattern El Niño, which changes rainfall patterns around the globe, causing heavy rainfall in the southern United States and severe drought in Australia, Indonesia, and southern Asia. On a smaller scale, ocean temperatures influence the development of tropical cyclones (hurricanes and typhoons), which draw energy from warm ocean waters to form and intensify. These sea surface temperature maps are based on observations by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua satellite. The satellite measures the temperature of the top millimeter of the ocean surface. In this map, the coolest waters appear in blue (approximately -2 degrees Celsius), and the warmest temperatures appear in pink-yellow (35 degrees Celsius). Landmasses and the large area of sea ice around Antarctica appear in shades of gray, indicating no data were collected. The most obvious pattern shown in the time series is the year-round difference in sea surface temperatures between equatorial regions and the poles. Various warm and cool currents stand out even in monthly averages of sea surface temperature. A band of warm waters snakes up the East Coast of the United States and veers across the North Atlanticâ€"the Gulf Stream. Although short-lived weather events that influence ocean temperature are often washed out in monthly averages, a few events show up. For example, in December 2003, strong winds blew southwest from the Gulf of Mexico over Central America toward the Pacific Ocean, driving surface waters away from the coast, and allowing cold water from deeper in the ocean to well up to the surface. These winds are a recurring phenomenon in the area in the winter; they are known as Tehuano winds. At the base of the ocean food web are single-celled algae and other plant-like organisms known as phytoplankton. Like plants on land, phytoplankton use chlorophyll and other light-harvesting pigments to carry out photosynthesis, absorbing atmospheric carbon dioxide to produce sugars for fuel. Chlorophyll in the water changes the way it reflects and absorbs sunlight, allowing scientists to map the amount and location of phytoplankton. These measurements give scientists valuable insights into the health of the ocean environment, and help scientists study the ocean carbon cycle. These chlorophyll maps show milligrams of chlorophyll per cubic meter of seawater each month. Places where chlorophyll amounts were very low, indicating very low numbers of phytoplankton are blue. Places where chlorophyll concentrations were high, meaning many phytoplankton were growing, are dark green. The observations come from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua satellite. Land is dark gray, and places where MODIS could not collect data because of sea ice, polar darkness, or clouds are light gray. The highest chlorophyll concentrations, where tiny surface-dwelling ocean plants are thriving, are in cold polar waters or in places where ocean currents bring cold water to the surface, such as around the equator and along the shores of continents. It is not the cold water itself that stimulates the phytoplankton. Instead, the cool temperatures are often a sign that the water has welled up to the surface from deeper in the ocean, carrying nutrients that have built up over time. In polar waters, nutrients accumulate in surface waters during the dark winter months when plants can't grow. When sunlight returns in the spring and summer, the plants flourish in high concentrations. A band of cool, plant-rich waters circles the globe at the Equator, with the strongest signal in the Atlantic Ocean and the open waters of the Pacific Ocean. This zone of enhanced phytoplankton growth comes from the frequent upwelling of cooler, deeper water as a result of the dominant easterly trade winds blowing across the ocean surface. In many coastal areas, the rising slope of the sea floor pushes cold water from the lowest layers of the ocean to the surface. The rising, or upwelling water carries iron and other nutrients from the ocean floor. Cold coastal upwelling and subsequent phytoplankton growth are most evident along the west coasts of North and South America and southern Africa. In March and April 2023, some earth scientists began to point out that average sea surface temperatures had surpassed the highest levels seen in a key data record maintained by NOAA. Months later, they remain at record levels, with global sea surface temperatures 0.99°C (1.78°F) above average in July. That was the fourth consecutive month they were at record levels. Scientists from NASA have taken a closer look at why. "There are a lot of things that affect the world's sea surface temperatures, but two main factors have pushed them to record heights," said Josh Willis, an oceanographer at NASA's Jet Propulsion Laboratory (JPL). "We have an El Niño developing in the Pacific, and that's on top of long-term global warming that has been pushing ocean temperatures steadily upward almost everywhere for a century." The map above shows sea surface temperature anomalies on August 21, 2023, when many areas were more than 3°C (5.4°F) warmer than normal. On that date, much of the central and eastern regions of the equatorial Pacific were unusually warm, the signature of a developing El Niño. As has been the case for weeks, large patches of warm water were also present in the Northwest Pacific near Japan and the Northeast Pacific near California and Oregon. Portions of the Indian, Southern, and Arctic Oceans also showed unusual warmth. The map is based on data from the Multiscale Ultrahigh Resolution Sea Surface Temperature (MUR SST) project, a JPL effort that blends measurements of sea surface temperatures from multiple NASA, NOAA, and international satellites, as well as ship and buoy observations. Rather than showing absolute temperature, the anomaly reflects the difference between the sea surface temperature on August 21, 2023, and the 2003-2014 average for that day. The video below, also based on MUR SST data, shows global sea surface temperatures since April 1, 2023, the period when they have been at record-breaking levels. The warmest waters appear dark red. In March and April 2023, some earth scientists began to point out that average sea surface temperatures had surpassed the highest levels seen in a key data record maintained by NOAA. Months later, they remain at record levels, with global sea surface temperatures 0.99°C (1.78°F) above average in July. That was the fourth consecutive month they were at record levels. Scientists from NASA have taken a closer look at why. "There are a lot of things that affect the world's sea surface temperatures, but two main factors have pushed them to record heights," said Josh Willis, an oceanographer at NASA's Jet Propulsion Laboratory (JPL). "We have an El Niño developing in the Pacific, and that's on top of long-term global warming that has been pushing ocean temperatures steadily upward almost everywhere for a century." The map above shows sea surface temperature anomalies on August 21, 2023, when many areas were more than 3°C (5.4°F) warmer than normal. On that date, much of the central and eastern regions of the equatorial Pacific were unusually warm, the signature of a developing El Niño. As has been the case for weeks, large patches of warm water were also present in the Northwest Pacific near Japan and the Northeast Pacific near California and Oregon. Portions of the Indian, Southern, and Arctic Oceans also showed unusual warmth. The map is based on data from the Multiscale Ultrahigh Resolution Sea Surface Temperature (MUR SST) project, a JPL effort that blends measurements of sea surface temperatures from multiple NASA, NOAA, and international satellites, as well as ship and buoy observations. Rather than showing absolute temperature, the anomaly reflects the difference between the sea surface temperature on August 21, 2023, and the 2003-2014 average for that day. The video below, also based on MUR SST data, shows global sea surface temperatures since April 1, 2023, the period when they have been at record-breaking levels. The warmest waters appear dark red. "Over the long term, we're seeing more heat and warmer sea surface temperatures pretty much everywhere," said Gavin Schmidt, the director of NASA's Goddard Institute for Space Studies. "That long-term trend is almost entirely attributable to human forcing---the fact that we've put such a huge amount of greenhouse gas in the atmosphere since the start of the industrial era." Schmidt noted that other factors---such as weather and wind patterns or the distribution of dust and aerosols---have short-term effects on sea surface temperatures in certain regions, but they generally have a minor effect on the longer-term global mean. Previous research shows that as much as 90 percent of the excess heat that has occurred in recent decades due to increasing greenhouse gas emissions is absorbed by the ocean, with much of that heat stored near the surface. The most important factor that helped push sea surface temperatures into record territory in 2023 was the evolving El Niño in the Pacific, according to Willis. He came to that conclusion by analyzing the timing and intensity of sea surface temperature anomalies in several regions and comparing them to the global trend. "We had a big jump in global surface temperature at the beginning of April---exactly when the Pacific temperatures jumped up and also when sea levels in the eastern Pacific started to rise," Willis said. "The heat waves in the Atlantic are important and will have serious effects on marine life and weather in Europe in the coming months. But it's the Pacific that has taken the global mean on a wild ride this year." What happens in the Pacific tends to have a large influence on the global sea surface temperatures partly because of its size. The Pacific represents about half of the world's ocean area. Marine heat waves---defined as periods of persistent anomalously warm ocean temperatures (warmer than 90 percent of the previous observations for a given time of year)---have occurred recently in several areas. One NOAA analysis showed that 48 percent of the global oceans were in the midst of a marine heat wave in August---a larger area than for any other month since the start of the record in 1991. Particularly intense events have warmed the North Atlantic and parts of the Caribbean in recent months. Willis expects the heat in the equatorial Pacific to have more staying power than many of the other marine heat waves simmering around the world. "Many of the marine heat waves we're seeing are ephemeral and 'skin' deep, generally lasting on the order of weeks and driven by atmospheric forces," explained Willis. The unusually warm water in the equatorial Pacific associated with the developing El Niño after three consecutive years of La Niña is expected to weaken trade winds in ways that reinforce and amplify the warming of surface waters, fueling the El Niño further. Forecasters from NOAA say that there is a greater than 95 percent chance that El Niño conditions will persist throughout the Northern Hemisphere winter. "What's happening in the Pacific with El Niño will influence global weather patterns and sea surface temperatures well into the winter and possibly even longer," Willis said. To monitor sea surface temperatures, scientists at NOAA and NASA analyze observations from sensors and buoys in the oceans, ships, and several different polar-orbiting and geostationary satellites. Groups of scientists with NOAA's Physical Sciences Laboratory, NOAA's Coral Reef Watch, and NASA's Jet Propulsion Laboratory track marine heat waves and sea surface temperature anomalies closely. You can use NASA's State of the Ocean Tool on Worldview to monitor daily sea surface temperature anomalies. One of the wettest wet seasons in northern Australia transformed large areas of the country's desert landscape over the course of many months in 2023. A string of major rainfall events that dropped 690 millimeters (27 inches) between October 2022 and April 2023 made it the sixth-wettest season on record since 1900--1901. This series of false-color images illustrates the rainfall's months-long effects downstream in the Lake Eyre Basin. Water appears in shades of blue, vegetation is green, and bare land is brown. The images were acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite between January and July 2023. In the January 22 image (left), water was coursing through seasonally dry channels of the Georgina River and Eyre Creek following weeks of heavy rains in northern Queensland. By April 21 (middle), floodwaters had reached further downstream after another intense period of precipitation in March. This scene shows that water had filled in some of the north-northwest trending ridges that are part of a vast fossil landscape of wind-formed dunes, while vegetation had emerged in wet soil upstream. Then by July 26 (right), the riverbed had filled with even more vegetation. The Georgina River and Eyre Creek drain approximately 210,000 square kilometers (81,000 square miles), nearly the area of the United Kingdom. Visible in the lower part of the images, the lake gets refreshed about every three years; when it reaches especially high levels, it may take 18 months to 2 years to dry up. Two smaller neighboring lakes flood seasonally. These three lakes and surrounding floodplains support hundreds of thousands of waterbirds and are designated as an Important Bird Area. Seasonal flooding is a regular occurrence in these desert river systems. However, the events of the 2022-2023 rainy season stood out in several ways. They occurred while La Niña conditions were in place over the tropical Pacific Ocean. (The wettest seasons in northern Australia have all occurred during La Niña years, according to Australia's Bureau of Meteorology.) In addition, major rains occurring in succession, as was the case with the January and March events, have the overall effect of prolonging floods. That's because vegetation that grows after the first event slows down the pulse of water that comes through in the next rain event. The high water has affected both local communities and ecosystems. Floods have inundated cattle farms and isolated towns on temporary islands. At the same time, they are a natural feature of the "boom-and-bust" ecology of Channel Country, providing habitat and nutrients that support biodiversity. After three consecutive years of La Niña, spring 2023 saw the return of El Niño---a natural climate phenomenon characterized by the presence of warmer than normal sea surface temperatures (and higher sea levels) in the central and eastern tropical Pacific Ocean. El Niño is associated with the weakening of easterly trade winds and the movement of warm water from the western Pacific toward the western coast of the Americas. The phenomenon can have widespread effects, often bringing cooler, wetter conditions to the U.S. Southwest and drought to countries in the western Pacific, such as Indonesia and Australia. Satellite- and ocean-based measurements of sea surface temperature are one way to detect the arrival of El Niño. Its signature also shows up in satellite measurements of sea surface height, which rises as ocean temperatures warm up. That's because warmer water expands to fill more volume, while cooler water contracts. The map above depicts sea surface height anomalies across the central and eastern Pacific Ocean as observed from June 1--10, 2023. Shades of blue indicate sea levels that were lower than average; normal sea level conditions appear white; and reds indicate areas where the ocean stood higher than normal. Data for the map were acquired by the Sentinel-6 Michael Freilich and Sentinel-3B satellites and processed by scientists at NASA's Jet Propulsion Laboratory (JPL). Note that signals related to seasonal cycles and long-term trends have been removed to highlight sea level anomalies associated with El Niño and other short-term natural phenomena. In a report released on June 8, 2023, the NOAA Climate Prediction Center declared El Niño conditions were present. The report pointed to sea surface temperatures in the Niño 3.4 region of the tropical Pacific (from 170° to 120° West longitude) that in May 2023 were 0.8°C (1.4°F) above the long-term average. Forecasters expected El Niño conditions to gradually strengthen into the 2023--2024 Northern Hemisphere winter, by which time they called for a 84 percent chance of a moderate strength El Niño developing and a 56 percent chance of a strong El Niño. As of June 2023, however, El Niño was not as far along as past El Niño events by the same time of year, according to Josh Willis, an oceanographer and Sentinel-6 Michael Freilich project scientist at JPL. "It's still a bit too early to say whether this will be a big one," Willis said. "It will probably have some global impacts, but there's still time for this El Niño to underwhelm." As spring turned to summer, phytoplankton came to life in the shallow waters of the North Sea. Sunlight and warm ocean temperatures in June 2023 enabled the microscopic plant-like organisms to rapidly multiply and form a dazzling turquoise display visible to satellites. Satellites observed hints of the bloom developing between Scotland and Norway for about two weeks, but the view from above was mostly hidden by clouds. Then, mostly clear skies on the afternoon of June 15, 2023, allowed the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA-20 satellite to acquire this natural-color image of the abundant phytoplankton. Phytoplankton are to the ocean what plants are to land: primary producers, an essential food source for other life, and the main carbon recycler for the marine environment. Diatoms, coccolithophores, algae, and other forms of phytoplankton are floating, plant-like organisms that soak up sunshine, carbon dioxide, and nutrients to create their own energy. This bloom might contain some diatoms---a type of phytoplankton with silica shells and ample chlorophyll that color the surface waters green. The color of the water, however, indicates that coccolithophores are likely abundant. Coccolithophores have calcium carbonate shells that make the water appear milky blue in satellite imagery, and they typically peak in abundance at these latitudes around the summer solstice. Phytoplankton are typically most abundant in the North Sea in late spring and early summer when high levels of nutrients are available in the water. Melting sea ice and increased runoff from European rivers---a product of melting snow and spring rains---carry a heavy load of nutrients out to sea. Intense seasonal winds blowing over the relatively shallow sea also cause a lot of mixing that brings nutrients to the surface. Researchers in Norway studied the patterns and timing of phytoplankton blooms in the North Sea using data from multiple satellite sensors, including VIIRS and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). They found that between 2000 and 2020, blooms in this region of the North Sea peaked in mid-to-late April. These blooms lasted, on average, about 46 days. They also found that in the 21-year study period, phytoplankton blooms in the region were starting later in the year and lasting slightly longer. The cause of this delay, however, was not immediately clear. The composition of phytoplankton blooms near Norway may be changing over time with warmer sea surface temperatures, the researchers noted, but it is difficult to tell the species composition of blooms without taking physical samples. However, a future NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission will enable researchers to infer more information about ocean ecology, such as the species of phytoplankton present in blooms and the rates of phytoplankton growth. Sea ice in the Sea of Okhotsk put on a dazzling display in late May 2023, as the winter's ice pack thinned and broke up. The freely drifting ice, subject to wind and currents, formed a series of spirals off the coast of Russia. The Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua satellite captured this image on May 28, 2023. More-intact ice is visible on the north side of the P'yagina Peninsula (Poluostrov P'yagina), at the top of the image, with smaller pieces breaking away and drifting to the south and west. A group of islands---too small to see clearly at this scale---off the eastern tip of the land may be responsible for the small eddies in that area. Spirals such as these can form downstream of a stationary object that obstructs fluid flow. The Sea of Okhotsk, which is hemmed in by the Siberian coast and the Kamchatka Peninsula, is the southernmost sea in the Northern Hemisphere that freezes seasonally. An influx of frigid Siberian air, in addition to inflows of freshwater from rivers that lower the salinity and raise the freezing point of the water, create conducive conditions for ice to form during the colder months. During the 2022-2023 winter, the extent of sea ice in the Arctic was below average. The end-of-winter minimum extent, reached on March 6, was the sixth lowest in the satellite record, according to data maintained by the National Snow & Ice Data Center (NSIDC). The NSIDC also noted that seasonal ice decline picked up in the last several days of May, when this image was captured. In a recent study, researchers in Japan found that yearly differences in ice extent are largely governed by regional cold air masses and low-pressure systems, along with large-scale patterns associated with the El Niño/Southern Oscillation (ENSO). Looking at longer term climate-driven trends, they reported that ice extent in the Sea of Okhotsk decreased by about 9 percent per decade between 1979 and 2010. Every fall, millions of people flock to the Shuangtaizi Estuary (also called the Liao River Estuary) in northeastern China to marvel at its brilliant red coastal landscapes. They are drawn by the expanses of rare salt-loving seepweed that thrive in the estuary's alkaline tidal mudflats. The small shrubby plants, Suaeda salsa (also called Suaeda heteroptera), start out greenish-red in the spring but become a bright crimson in fall as seasonal shifts in rainfall and tides expose seepweed to slightly saltier, cooler conditions. This leads to the increased production and accumulation of the red pigment betalain. However, the estuary has changed significantly in recent decades due to coastal development, raising questions about the long-term viability of its colorful seepweed beaches and wetland habitat. The scale of change is apparent in the pair of Landsat images shown above. The image on the left, acquired by the Thematic Mapper on Landsat 5, shows the estuary in 1986; the image on the right, from the Operational Land Imager-2 (OLI-2) on Landsat 9, shows the same area in 2022. Both images were acquired in September, around the time when seepweed reaches its deepest red. The green areas along the river are dominated by Phragmites australis, a type of reed. The yellow areas are rice fields. The photo below shows seepweed (red) in the foreground transitioning into Phragmites australis (green) in the background. Large new aquaculture facilities---and a new port (lower right of image)---have replaced tidal flats where seepweed once thrived. The construction of a dyke and reservoir on the eastern bank of the river has also isolated part of the estuary from tidal waters, making the area unsuitable for seepweed. In wider views of the 1986 and 2022 images, notice how seepweed was further constrained by urbanization and the expansion of aquaculture to the east of the port, as well as the expansion of gas and oil drilling on the western bank of the Shuangtaizi River. Human activities have affected seepweed in other less direct ways in recent decades. The construction of dams, bridges, and canals caused spikes in the amount of sediment carried by the river and deposited on tidal flats downstream. The extra accumulations made it difficult for new seepweed plants to germinate in some areas. Researchers have also found evidence indicating that the construction of boardwalks and the rising number of tourists has harmed seepweed by scaring away waterbirds that feed on crabs, leading to higher numbers of crabs grazing on seepweed in certain areas. Overall, hundreds of square kilometers of wetlands have been lost since the 1980s, according to one analysis that spans three decades of Landsat observations. The amount of land with seepweed dropped by roughly 25 percent during that time, though certain parts of the estuary have seen seepweed areas expand or grow more concentrated. Seepweed-seeking tourists are not the only group affected by the loss of tidal flats and wetlands in this area. The estuary provides habitat for more than 100 water birds, including the critically endangered Siberian Crane (Leucogeranus leucogeranus), the endangered Oriental stork (Ciconia boyciana), and the red-crowned Crane (Grus japonensis). The estuary was named a national nature reserve in 1998 and a Ramsar site in 2005. For the past few decades, scientists have been observing natural ocean fertilization events---episodes when plumes of volcanic ash, glacial flour, wildfire soot, and desert dust blow out onto the sea surface and spur massive blooms of phytoplankton. But beyond these extreme events, there is a steady, long-distance rain of dust particles onto the ocean that promotes phytoplankton growth just about all year and in nearly every basin. In a new study published May 5 in the journal Science, a team of researchers from Oregon State University, the University of Maryland Baltimore County, and NASA combined satellite observations with an advanced computer model to home in on how mineral dust from land fertilizes the growth of phytoplankton in the ocean. Phytoplankton are microscopic, plant-like organisms that form the center of the marine food web. Phytoplankton float near the ocean surface primarily subsisting on sunlight and mineral nutrients that well up from the depths or float out to sea in coastal runoff. But mineral-rich desert dust---borne by strong winds and deposited in the ocean---also plays an important role in the health and abundance of phytoplankton. This image, acquired on April 8, 2011, by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite, shows Saharan dust over the Bay of Biscay. A phytoplankton bloom in the bay makes the water appear bright green and blue. Sediment is likely contributing to some of the color, especially in areas closer to the shore. According to the new study, dust deposition onto the ocean supports about 4.5 percent of yearly global export production---a measure of how much of the carbon phytoplankton take up during photosynthesis sinks into the deep ocean. However, this contribution approaches 20 percent to 40 percent in some ocean regions at middle and higher latitudes. Phytoplankton play a large role in Earth's climate and carbon cycle. Like land plants, they contain chlorophyll and derive energy from sunlight through photosynthesis. They produce oxygen and sequester a tremendous amount of carbon dioxide in the process, potentially on a scale comparable to rainforests. And they are at the bottom of an ocean-wide food pecking order that ranges from tiny zooplankton to fish to whales. Dust particles can travel thousands of miles before falling into the ocean, where they nourish phytoplankton long distances from the dust source, said study coauthor Lorraine Remer, a research professor at the University of Maryland Baltimore County. "We knew that atmospheric transport of desert dust is part of what makes the ocean 'click,' but we didn't know how to find it," she said. Seasonal allergy sufferers be warned: this story may have you reaching for the antihistamines. Researchers have determined that "slicks" on the surface of the Baltic Sea, visible in satellite images, are made up of pine pollen. Pollen slicks are visible in these images of the Baltic Sea, acquired on May 16, 2018, with the MultiSpectral Instrument (MSI) on the European Space Agency's Sentinel-2A satellite. The images are false-color (bands 8A, 3, and 2) and have been enhanced to increase the visibility of the pollen. The patterns are caused by wind-driven currents and waves moving the pollen around on the surface of the water. The composition of slicks in this region was previously unclear. Other types of floating material, such as cyanobacteria and marine debris, have been known to appear in satellite imagery. But by combining experimental results, ground-based observations, and satellite image processing, the researchers could confidently attribute the material in the eddies to pine (Pinus sylvestris) pollen. The impetus for investigating this phenomenon came from a different marine event, said Chuanmin Hu, an ocean optics expert at the University of South Florida who led the research. "This work is inspired by a recent sea snot event in the Marmara Sea that created a huge problem for Türkiye and its coastal regions," he said. Sea snot, which is caused by phytoplankton releasing a gooey substance, coated large swaths of the sea in May 2021 and caught Hu's attention when it was detected by satellites. That led him to wonder if anything comparable was occurring on other large bodies of water nearby. As it turned out, satellite images of the Baltic Sea from that time looked similar to the satellite images of sea snot in the Marmara Sea (to human eyes, at least). But Hu found it strange that there were no reports of disruptive slime from the large, heavily trafficked sea. To identify potential slicks, Hu and colleagues inspected medium-resolution satellite images from sensors such as the Moderate Resolution Imaging Spectroradiometer on NASA's Terra and Aqua satellites. When his team analyzed other satellite data for the spectral signature of the mystery Baltic Sea substance, they realized it was distinct from sea snot and other floating matter. The spectral shape had a characteristically sharp increase between wavelengths of 400 and 500 nanometers. Given the timing of the slicks and the prevalence of pine trees in the nine countries surrounding the sea, they suspected pollen as a possible culprit. Collaborators in Poland had photographs of pollen on the surface of the water, acquired during fieldwork in May 2013 (below). To dig deeper, the U.S. and Polish groups conducted laboratory and field experiments to measure the spectral reflectance of pollen. Indeed, the results matched what was captured by satellites. The researchers then looked back at springtime images of the Baltic Sea from 2000 to 2021 and saw similar slick patterns in 14 of those years. Notably, the pollen's footprint on the sea in the second half of the study period was markedly larger than in the first half. In recent years, slicks often cover some portion of the sea in parts of May and June. This observation aligns with trends toward longer pollen seasons and more pollen production that have been documented in other areas of the world. For example, one recent study found that pollen season in North America starts nearly three weeks sooner and lasts about a week longer than it did in 1990, driven by warming temperatures. In addition, more carbon dioxide in the atmosphere fueling photosynthesis may increase plants' potential to produce more pollen. The profusion of pollen may have larger impacts beyond making people sneeze. Though not well studied, pollen grains can affect aquatic ecosystems by supplying carbon to the sea. Much like leaf litter supports food webs in lakes and streams, pollen grains may be an important source of nutrients for insect larvae, crustaceans, and other invertebrates in coastal Baltic Sea waters. Having cracked the code of distinguishing pollen in satellite imagery, Hu thinks the imagery may lead to several new insights. "If we can track pollen aggregation in different places, this may provide useful data for fisheries studies," he said. Even more, the technique could complement land-based air quality sensors to monitor allergens---all the more relevant as human health impacts from allergies intensify. For several weeks in April 2023, swirls of green and turquoise grew more vibrant in the waters off the Mid-Atlantic coast of the United States. Some of the color is due to an abundance of phytoplankton. Though each of these floating plant-like organisms is microscopic, large groups of them are visible to satellites. A phytoplankton bloom was under way on April 20, 2023, when the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua satellite acquired this image (top). The detailed image below was acquired the same day with the Operational Land Imager-2 (OLI-2) on Landsat 9. Phytoplankton are responsible for nearly half of Earth's primary production. They turn carbon dioxide, sunlight, and nutrients into the food that feeds almost all other life in the sea, from zooplankton to finfish to whales. The type of phytoplankton present in the bloom cannot be definitively identified based on these natural-color images. But assessments of past blooms in the area have turned up a mix of diatoms and coccolithophores. Diatoms, a microscopic form of algae, have silica shells and plenty of chlorophyll that can make the water appear green. Coccolithophores have chalky calcium carbonate plates (coccoliths) that reflect light and can make the water appear bright blue. Color can also come from other sources, such as sediment or colored dissolved organic matter (CDOM) that have mixed in the water. Discharge from the Delaware River delivers sediment and CDOM to the coastal waters in this region. It can also supply nutrients---contained in the runoff from farms and urban and suburban areas---that help to fuel large blooms. Similar blooms have occurred in recent years, in both 2021 and 2022. Those blooms, however, developed their most striking colors almost one month later, around mid-May. In February 2023, Tropical Cyclone Gabrielle churned south across the Coral Sea and passed over the Bellona Plateau---a shallow area 600 kilometers (400 miles) west of Grande Terre, the principal island of New Caledonia. Once a sizable island during the Pleistocene ice ages, the plateau is now submerged under 25-50 meters of water. It hosts reefs that teem with corals, coralline algae, mollusks, foraminifera, and many other types of marine life with calcium carbonate skeletons or shells. Signs of underwater reefs and carbonate platforms are often subtle in satellite imagery. But Gabrielle's winds were fierce enough that the storm left a clear sign of the carbonate ecosystem below the water. The passing storm stirred up enough carbonate sediment to temporarily discolor more than 13,000 square kilometers of water, an area about the size of Puerto Rico. Resuspension events of this size are rare at Bellona Plateau, with this being only the second time it has happened at this scale since the launch of the MODIS sensor on the Terra satellite in 1999. Gabrielle was passing over the area on February 9, 2023, when the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite acquired the first image above. After the storm clouds cleared, the satellite observed carbonate sediment that had become suspended in the water (second image) on February 11, 2023. The sediment drifted in ocean currents over the span of a week, with water over the Bellona plateau returning to its normal color by February 20, 2023. The Operational Land Imager-2 (OLI-2) on Landsat 9 captured the detailed images (below) showing sediment swirling in eddies around the plateau on February 12, 2023. The sediment was likely fine-grained carbonate mud with some larger carbonate sand mixed in. It likely formed due to the erosion and accumulation of bits of coral skeletons, coralline algae, and the hard shells of marine organisms that live on the plateau. "With the right water chemistry and amount of light, plateaus like this become major calcium carbonate factories," explained James Acker, an oceanographer with ADNET Systems at the Goddard Earth Sciences Data and Information Services Center (GES DISC). Previous estimates suggest that although shallow coastal areas cover just 7 percent of the ocean's area, they generate about half of the world's marine carbonate sediment. Acker has been using satellites to observe carbonate resuspension events since the launch of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) in 1997. The goal is to develop better estimates for how much carbonate sediment from shallow areas ends up getting pushed into deeper waters by winds, currents, or other processes. "Deep ocean water dissolves carbonate muds and sands when they sink," explained Acker. "That can help counter the ongoing ocean acidification we're seeing that is caused by the rising levels of carbon dioxide in the atmosphere." In some cases, depending on the chemistry of the water, carbonates dissolve at depths as shallow as 500 meters. In others, they dissolve at depths closer to 4.5 kilometers. Estimates suggest that oceans absorb about 30 percent of the carbon dioxide that humans release into the atmosphere. Some of that carbon gets incorporated into shells and sediment and eventually stored as calcium carbonate in limestone and other sedimentary rocks, making carbonate platforms and marine sedimentary rock an important carbon sink. However, the estimates for how much carbonate these shallow carbonate reefs and plateaus produce, export, and store vary significantly. And there is considerable uncertainty about how the ocean's ability to store carbon will change as the acidity of the ocean changes. More acidic ocean waters make it harder for many marine organisms to build calcium carbonate shells and thrive, so acidification may reduce how much carbon ends up stored in sedimentary rocks. The first step to investigating how climate change might be changing the marine carbon cycle is to simply understand and document how much carbonate sediment is cycling between shallow and deep water, explained Acker. That has led Acker and sedimentologist Jude Wilber to examine decades of satellite data to find out if storm and wind events play an important role in this cycling from shallow to deep. At the American Geophysical Union's Oceans meeting in 2022, Acker and colleagues presented an analysis of a previous resuspension event that followed Tropical Cyclone Wati hitting the Bellona Plateau in 2006. That event occurred after the Category 4 storm stalled over the plateau for two days and battered it with winds that exceeded 209 kilometers (135 miles) per hour. However, the estimates for how much carbonate these shallow carbonate reefs and plateaus produce, export, and store vary significantly. And there is considerable uncertainty about how the ocean's ability to store carbon will change as the acidity of the ocean changes. More acidic ocean waters make it harder for many marine organisms to build calcium carbonate shells and thrive, so acidification may reduce how much carbon ends up stored in sedimentary rocks. The first step to investigating how climate change might be changing the marine carbon cycle is to simply understand and document how much carbonate sediment is cycling between shallow and deep water, explained Acker. That has led Acker and sedimentologist Jude Wilber to examine decades of satellite data to find out if storm and wind events play an important role in this cycling from shallow to deep. At the American Geophysical Union's Oceans meeting in 2022, Acker and colleagues presented an analysis of a previous resuspension event that followed Tropical Cyclone Wati hitting the Bellona Plateau in 2006. That event occurred after the Category 4 storm stalled over the plateau for two days and battered it with winds that exceeded 209 kilometers (135 miles) per hour. "Due to decades of satellite observations---and dramatic examples like this---we can say confidently that tropical cyclones play a very important role," said Acker. "Nothing else exports the volume of sediment into deeper water that they do. The next step is to demonstrate that in a more systematic and rigorous way by analyzing the entire satellite record with machine learning techniques and getting teams out in the field to better understand the dynamics of transport events." Water from recent winter storms is needed by farmers, wildlife, and residents in the region, where precipitation and lake levels in recent years have been among the lowest since the 1970s. However, scientists caution that similar large precipitation events in the past have not been enough to reverse the longer-term depletion of groundwater---a reserve of water that supplements surface sources used for irrigation and other purposes. "The abundant water is expected to recharge the groundwater in the next few months, as we have seen during similar events in 2011 and 2017," said Pang-Wei Liu, a scientist at NASA's Goddard Space Flight Center. "However, if the climate pattern is the same as before---dry and hot in summer followed by low precipitation---and the water demands are still high, then we expect the groundwater drawdown will continue." The chart above, produced with data provided by Liu, shows a downward trend in California's terrestrial water storage (dark blue line) spanning nearly two decades. This includes surface and groundwater, and water held within the soil and in snow. The rest of the lines show why this is happening; amid some variability in all types of stored water, it is groundwater (light blue line) that is sharply decreasing. Liu and colleagues used data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On satellite missions to show that the depletion of groundwater in California's Central Valley has been accelerating since 2003. Their results were published December 2022 in Nature Communications. "Even the wettest wet seasons are simply never enough to make up for the far greater amount of groundwater that California extracts each year," said Jay Famiglietti, a global futures professor at Arizona State University and a co-author of the paper. "Hopefully California's Sustainable Groundwater Management Act can slow what will otherwise be a speedy trip to the bottom." Earth's average surface temperature in 2022 tied with 2015 as the fifth warmest on record, according to an analysis by NASA. Continuing the planet's long-term warming trend, global temperatures in 2022 were 0.89 degrees Celsius (1.6 degrees Fahrenheit) above the average for NASA's baseline period (1951--1980), according to scientists at NASA's Goddard Institute for Space Studies (GISS). The past nine years have been the warmest years since modern recordkeeping began in 1880. This means Earth in 2022 was about 1.11°C (2°F) warmer than the late 19th century average. The map above depicts global temperature anomalies in 2022. It does not show absolute temperatures; instead, it shows how much warmer or cooler each region of Earth was compared to the average from 1951 to 1980. The bar chart below shows 2022 in context with temperature anomalies since 1880. The values represent surface temperatures averaged over the entire globe for the year. "The reason for the warming trend is that human activities continue to pump enormous amounts of greenhouse gases into the atmosphere, and the long-term planetary impacts will also continue," said Gavin Schmidt, director of GISS, NASA's leading center for climate modeling. Human-driven greenhouse gas emissions have rebounded following a short-lived dip in 2020 due to the COVID-19 pandemic. Recently, NASA scientists, as well as international scientists, determined carbon dioxide emissions were the highest on record in 2022. NASA also identified some super-emitters of methane---another powerful greenhouse gas---using the Earth Surface Mineral Dust Source Investigation (EMIT) instrument that launched to the International Space Station last year. The Arctic region continues to experience the strongest warming trends---close to four times the global average---according to GISS research presented at the 2022 annual meeting of the American Geophysical Union, as well as a separate study. Communities around the world are experiencing impacts scientists see as connected to the warming atmosphere and ocean. Climate change has intensified rainfall and tropical storms, deepened the severity of droughts, and increased the impact of storm surges. Last year brought torrential monsoon rains that devastated Pakistan and a persistent megadrought in the U.S. Southwest. In September, Hurricane Ian became one of the strongest and costliest hurricanes to strike the continental U.S. NASA's global temperature analysis is drawn from data collected by weather stations and Antarctic research stations, as well as instruments mounted on ships and ocean buoys. NASA scientists analyze these measurements to account for uncertainties in the data and to maintain consistent methods for calculating global average surface temperature differences for every year. These ground-based measurements of surface temperature are consistent with satellite data collected since 2002 by the Atmospheric Infrared Sounder on NASA's Aqua satellite and with other estimates. NASA uses the period from 1951--1980 as a baseline to understand how global temperatures change over time. That baseline includes climate patterns such as La Niña and El Niño, as well as unusually hot or cold years due to other factors, ensuring it encompasses natural variations in Earth's temperature. Many factors can affect the average temperature in any given year. For example, 2022 was one of the warmest on record despite a third consecutive year of La Niña conditions in the tropical Pacific Ocean. NASA scientists estimate that La Niña's cooling influence may have lowered global temperatures slightly (about 0.06°C or 0.11°F) from what the average would have been under more typical ocean conditions. A separate, independent analysis by the National Oceanic and Atmospheric Administration (NOAA) concluded that the global surface temperature for 2022 was the sixth highest since 1880. NOAA scientists use much of the same raw temperature data in their analysis and have a different baseline period (1901--2000) and methodology. Although rankings for specific years can differ slightly between the records, they are in broad agreement and both reflect ongoing long-term warming.