nasa-gesdisc/es-publications-researchareas huggingface dataset:
Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using (2021)

We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire growing season. We find that overall, the BEPS-simulated crop gross primary production (GPP), net primary production (NPP), and LAI time-series can explain 82%, 83%, and 85%, respectively, of the variation in the above-ground biomass (AGB) for six selected annual crops, while an application of individual crop LAI explains only 50% of the variation in AGB. The linear relationships between the AGB and these three indicators (GPP, NPP and LAI time-series) are rather high for the six crops, while the slopes of the regression models vary for individual crop type, indicating the need for calibration of key photosynthetic parameters and carbon allocation coefficients. This study demonstrates that accumulated GPP and NPP derived from an ecosystem model, driven by Sentinel-2 LAI data and abiotic data, can be effectively used for crop AGB mapping; the temporal information from LAI is also effective in AGB mapping for some crop types.

Research areas: Agriculture
Understanding spatial variability of forage production in California grasslands: delineating climate, topography and soil controls (2021)

Abstract
            
              Rangelands are a key global resource, providing a broad range of ecological services and economic benefits. Californias predominantly annual rangelands cover 12% of the states land area, and the forage production is highly heterogeneous, making balancing economic (grazing), conservation (habitat) and environmental (erosion/water quality) objectives a big challenge. Herein, we examined how climate and environmental factors regulate annual grassland forage production spatially across the state and among four ecoregions using machine learning models. We estimated annual forage production at 30 m resolution over a 14 year period (20042017) using satellite images and data fusion techniques. Our satellite-based estimation agreed well with independent field measurements, with a
              R
              2
              of 0.83 and RMSE of 682 kg ha
              1
              . Forage production (14 year average) showed large spatial variability (2940  934 kg ha
              -1
              yr
              -1
              ; CV = 35%) across the study area. The gradient boosted regression tree with 11 feature variables explained 67% of the variability in forage production across the state. Precipitation amount, especially in November (germination) and April (rapid growth), was found as the dominant driver for spatial variation in forage production, especially in drier ecoregions and during drier years. Seasonal distribution of precipitation and minimum air temperature showed a relatively stronger control on forage production in wetter regions and during wet years. Additionally, solar energy became more important in wetter ecoregions. Drought reduced forage production from the long-term mean, i.e. a 33%  19% decrease in production (2397  926 kg ha
              -1
              yr
              -1
              ; CV = 38%) resulting from a 29%  5% decrease in precipitation. The machine learning based spatial analysis using big data provided insights on impacts of climate and environmental factors on forage production variation at various scales. This study demonstrates a cost-effective approach for rapid mapping and assessment of annual forage production with the potential for near real-time application.

Research areas: Agriculture, Ecosystems
Drivers of within-field spatial and temporal variability of crop yield across the US Midwest (2018)

Abstract
            
              Not all areas of a farmers field are equal; some always produce more relative to the rest of the field, others always less, while still other areas fluctuate in their production capacity from one year to the next, depending on the interaction between climate, soil, topography and management. Understanding why the yield in certain portions of a field has a high variability over timewe call these areas
              unstable
              is of paramount importance both from an economic and an environmental point of view, as it is through the better management of these areas that we can improve yields or reduce input costs and environmental impact. In this research, we analyzed data from 338 fields cultivated with maize, soybean, wheat and cotton in the US Midwest to understand how topographic attributes and rain affect yield stability over time. In addition to this high resolution yield monitor dataset, we used publicly available data on topography, rain and soil information to test the hypothesis that within-field areas characterized by a low topographic wetness index (proxy for areas with probability of lower water content) always perform poorly (low and stable yield) compared to the rest of the field because they are drier, and that areas of a field characterized by a mid-high wetness index (high and stable yield) always perform well relative to rest of the field because they have greater water availability to plants. The relative performance of areas of a field with a very high wetness index (e.g. depressions) strongly depends on rain patterns because they may be waterlogged in wet years, yielding less than the rest of the field, or wetter during dry years, yielding more than the rest of the field. We present three different observations from this dataset to support our hypothesis. First, we show that the average topographic wetness index in the different stability zones is lower in low and stable yield areas, high in high and stable yield areas and even higher in unstable yield areas (p 

Research areas: Agriculture
Ocean seascapes predict distant-water fishing vessel incursions into (2021)

Many of the world's most important fisheries are experiencing illegal, unreported and unregulated (IUU) fishing, thereby undermining efforts to sustainably conserve and manage fish stocks. A major challenge to ending IUU fishing is improving our ability to identify whether a vessel is fishing illegally and where illegal fishing is likely to occur in the ocean. However, monitoring the oceans is costly, time‑consuming, and logistically challenging for maritime authorities to patrol. To address this problem, we use vessel tracking data and machine learning to predict whether a distant‑water fishing vessel is fishing within the Argentine exclusive economic zone (EEZ) on the Patagonian Shelf, one of the world's most productive regions for fisheries. We combine vessel location data with oceanographic seascapes—classes of oceanic areas based on oceanographic variables—and other remotely sensed oceanographic variables to train a series of machine learning models of varying levels of complexity. These models are able to predict whether a distant‑water fishing vessel is operating inside the EEZ with 69%–96% confidence, depending on the year and predictor variables used. These results offer a promising step towards pre‑empting illegal activities, rather than reacting to them forensically.

Research areas: Agriculture
Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based (2021)

In tropical/subtropical monsoon regions, accurate rice mapping is hampered by the following factors: (1) The frequent occurrence of clouds in such areas during the rice-growing season interferes strongly with optical remote sensing observations; (2) The agro-landscape in such regions is fragmented and scattered. Rice maps produced using low spatial resolution data cannot well delineate the detailed distribution of rice, while pixel-based mapping using medium and high resolutions has significant salt-and-pepper noise. (3) The cropping system is complex, and rice has a rotation schedule with other crops. Therefore, the Phenology-, Object- and Double Source-based (PODS) paddy rice mapping algorithm is implemented, which consists of three steps: (1) object extraction from multi-temporal 10-m Sentinel-2 images where the extracted objects (fields) are the basic classification units; (2) specifying the phenological stage of transplanting from SavitzkyGolay filtered enhanced vegetation index (EVI) time series using the PhenoRice algorithm; and (3) the identification of rice objects based on flood signal detection from time-series microwave and optical signals of the Sentinel-1/2. This study evaluated the potential of the combined use of the Sentinel-1/2 mission on paddy rice mapping in monsoon regions with the Hangzhou-Jiaxin-Huzhou (HJH) plain in China as the case study. A cloud computing approach was used to process the available Sentinel-1/2 imagery from 2019 and MODIS images from 2018 to 2020 in the HJH plain on the Google Earth Engine (GEE) platform. An accuracy assessment showed that the resultant object-based paddy rice map has a high accuracy with a producer (user) accuracy of 0.937 (0.926). The resultant 10-m paddy rice map is expected to provide unprecedented detail, spatial distribution, and landscape patterns for paddy rice fields in monsoon regions.

Research areas: Agriculture
Diverse management strategies produce similar ecological outcomes on ranches in Western Great Plains: social-ecological assessment (2018)

Experiments investigating grazing systems have often excluded ranch-scale decision making, which has limited our understanding of the processes and consequences of adaptive management. We conducted interviews and vegetation monitoring on 17 ranches in eastern Colorado and eastern Wyoming to investigate rancher decision-making processes and the associated ecological consequences. Management variables investigated were grazing strategy, grazing intensity, planning style, and operation type. Ecological attributes included the relative abundance of plant functional groups and categories of ground cover. We examined the environmental and management correlates of plant species and functional group composition using nonmetric multidimensional scaling and linear mixed models. After accounting for environmental variation across the study region, species composition did not differ between grazing management strategy and planning style. Operation type was significantly correlated with plant community composition. Integrated cow-calf plus yearling operations had greater annual and less key perennial cool-season grass species cover relative to cow-calfonly operations. Integrated cow-calf plus yearling ranches were able to more rapidly restock following drought compared with cow-calf operations. Differences in types of livestock operations contributed to variability in plant species composition across the landscape that may support diverse native faunal species in these rangeland ecosystems. Three broad themes emerged from the interviews: 1) long-term goals, 2) flexibility, and 3) adaptive learning. Stocking-rate decisions appear to be slow, path-dependent choices that are shaped by broader social, economic, and political dynamics. Ranchers described having greater flexibility in altering grazing strategies than ranch-level, long-term, annual stocking rates. These results reflect the complexity of the social-ecological systems ranchers navigate in their adaptive decision-making processes. Ranch decision-making process diversity within these environments precludes development of a single best strategy to manage livestock grazing.

Research areas: Agriculture
Finding the sweet spot: Shifting optimal climate for maple syrup production in North America (2019)

Climate change is affecting the benefits society derives from forests. One such forest ecosystem service is maple syrup, which is primarily derived from Acer saccharum (sugar maple), currently an abundant and widespread tree species in eastern North America. Two climate sensitive components of sap affect syrup production: sugar content and sap flow. The sugar in maple sap derives from carbohydrate stores influenced by prior year growing season conditions. Sap flow is tied to freeze/thaw cycles during early spring. Predicting climate effects on syrup production thus requires integrating observations across scales and biological processes. We observed sap at 6 sugar maple stands spanning sugar maple's latitudinal range over 2–6 years to predict the role of climate variation on sugar content and sap flow. We found that the timing of sap collection advanced by 4.3 days for every 1 °C increase in March mean temperature, sap volume peaked at a January-May mean temperature of 1 °C, and sap sugar content declined by 0.1 °Brix for every 1 °C increase in previous May-October mean temperature. Using these empirical relationships, we projected that the sap collection season midpoint will be 1 month earlier and sap sugar content will decline by 0.7 °Brix across sugar maple's range by the year 2100 in an RCP 8.5 climate change scenario. The region of maximum sap flow is expected to shift northward by 400 km, from near the 43rd parallel to the 48th parallel by 2100. Our findings suggest climate change will have profound effects on syrup yield across most of sugar maple's range; drastic shifts in the timing of the tapping season accompanied by flat to moderate increases in syrup yield per tap in Canada contrast with declines in syrup yield and higher frequencies of poor syrup production years across most of the U.S. range.

Research areas: Agriculture
Annual dynamics of carbon dioxide fluxes over a rainfed alfalfa field in the US Southern Great Plains (2019)

A thorough investigation of annual dynamics of carbon dioxide (CO2) fluxes with respect to major controlling factors and harvest management is lacking for rainfed alfalfa (Medicago sativa L.), a high quality perennial legume forage. To address this knowledge gap, this study reports two years (April 2016 - March 2018) of eddy covariance measurements of CO2 fluxes over a rainfed alfalfa field in central Oklahoma, USA. Alfalfa yields were strongly regulated by amount and timing of rainfall. As a result, cumulative dry forage yield was 7.5 t ha1 (four harvests) in 2016 (dry year) and 10 t ha1 (five harvests) in 2017 (wet year). An optimum air temperature (Ta) and vapor pressure deficit (VPD) for net ecosystem CO2 exchange (NEE) was approximately 25 C and 2.2 kPa, respectively. The response of gross primary production (GPP) to photosynthetically active radiation (PAR) varied with growth stage of alfalfa and climatic conditions (i.e., dry or normal/wet periods). Daily (8-day averages) NEE and gross primary production (GPP) reached -8.17 and 16.69 g C m-2 d1, respectively. Magnitudes of GPP (GPPMOD) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) were < 50% of tower-derived GPP, most likely due to a smaller value (0.15 g C mol1 PAR) for light use efficiency in the GPPMOD algorithm. The observed 8-day composite ecosystem light use efficiency (ELUE) was up to 0.36 g C mol1 PAR in this study. The rainfed alfalfa field with 45 hay harvests per year showed large carbon uptake potential (e.g., cumulative NEE of -454 g C m-2 in 2017) at an annual scale. The GPP and ELUE showed a strong correspondence with MODIS-derived vegetation indices, indicating the potential of applying satellite remote sensing to upscale site-level observations of CO2 fluxes for alfalfa to larger spatial scales.

Research areas: Agriculture
From sunlight to seed: Assessing limits to solar radiation capture and conversion in agro-ecosystems (2020)

Yield depends upon the amount of photosynthetically active radiation (PAR) absorbed by the crop during the growing season and its conversion into harvestable biomass. Little is known about attainable and actual efficiencies involved in absorbing incident PAR (ea) and converting it into yield (ec) in producer fields. We developed a novel approach consisting of producer data, satellite imagery, and crop modelling to assess ea and ec in agro-ecosystems. Simulated phenology and satellite imagery were used to estimate incident (IPAR) and absorbed (APAR) PAR during the crop season for 3096 soybean fields sown across the US North Central region during 20142016. Quantile regression was used to derive upper limits for ea and ec and multiple-regression analysis was performed to identify biophysical drivers of the observed variation in these two efficiencies. Differences in weather, soil, and management resulted in a wide range in crop cycle length (58126 d), total IPAR (5801250 MJ m2), and seed yield (0.86.3 Mg ha1). The relationship between yield and total APAR was curvilinear, indicating marginal yield gains in the upper APAR range. Attainable ea represented 65% of total IPAR, while attainable ec ranged from 0.6 to 1.2 g seed MJ1 depending upon total APAR. Average producer ea and ec were 14 and 29% below their attainable efficiencies. Average efficiency in converting IPAR into yield was 0.8%, with an upper limit of 1.1% derived from the 95th percentile of the field data distribution. Although weak, relationships between efficiencies and meteorological factors such as IPAR, temperature, water balance, and diffuse radiation were consistent with previous literature. There was a strong trade-off between ec and ea indicating that maximizing both efficiencies simultaneously was not possible in producer fields. Our approach can be used to determine attainable and actual efficiencies in capturing and converting radiation into yield, set realistic yield limits, and understand relationships between producer yield and management practices.

Research areas: Agriculture
Influence of integrated agronomic and weed management practices on soybean canopy development and yield (2021)

The role of weed suppression by the cultivated crop is often overlooked in annual row cropping systems. Agronomic practices such as planting time, row spacing, tillage and herbicide selection may influence the time of crop canopy closure. The objective of this research was to evaluate the influence of the aforementioned agronomic practices and their interaction with the adoption of an effective preemergence (PRE) soil residual herbicide program on soybean canopy closure and yield. A field experiment was conducted in 2019 and 2020 in Arlington, WI, as a 2222 factorial in a randomized complete block design, including early (late April) and standard (late May) planting time, narrow (38 cm) and wide (76 cm) row spacing, conventional tillage and no-till, and soil-applied PRE herbicide (yes and no; flumioxazin 150 g ai ha1 + metribuzin 449 g ai ha1 + pyroxasulfone 190 g ai ha1). All plots were maintained weed-free throughout the growing season. In both years, early planted soybeans reached 90% green canopy cover (T90) before (7 to 9 d difference) and yielded more (188 to 902 kg ha1 difference) than the standard planted soybeans. Narrow-row soybeans reached T90 earlier than wide-row soybeans (4 to 7 d difference), but yield was similar between row spacing treatments. Conventional tillage resulted in a higher yield compared to a no-till system (377 kg ha1 difference). The PRE herbicide slightly delayed T90 (4 d or less) but had no impact on yield. All practices investigated herein influenced the time of soybean canopy closure but only planting time and tillage impacted yield. Planting soybeans earlier and reducing their row spacing expedites the time to canopy closure. The potential delay in canopy development and yield loss if soybeans are allowed to compete with weeds early in the season would likely outweigh the slight delay in canopy development by an effective PRE herbicide.

Research areas: Agriculture
Climate change favors rice production at higher elevations in Colombia (2019)

Rice (Oriza sativa) feeds nearly half of the worlds population. Regional and national studies in Asia suggest that rice production will suffer under climate change, but researchers conducted few studies for other parts of the world. This research identifies suitable areas for cultivating irrigated rice in Colombia under current climates and for the 2050s, according to the Representative Concentration Pathway (RCP) 8.5 scenario. The methodology uses known locations of the crop, environmental variables, and maximum entropy and probabilistic methods to develop niche-based models for estimating the potential geographic distribution of irrigated rice. Results indicate that future climate change in Colombia could reduce the area that is suitable for rice production by 60%, from 4.4 to 1.8 million hectares. Low-lying rice production regions could be the most susceptible to changing environmental conditions, while mid-altitude valleys could see improvements in rice-growing conditions. In contrast to a country like China where rice production can move to higher latitudes, rice adaptation in tropical Colombia will favor higher elevations. These results suggest adaptation strategies for the Colombian rice sector. Farmers can adopt climate-resilient varieties or change water and agronomic management practices, or both. Other farmers may consider abandoning rice production for some other crop or activity.

Research areas: Agriculture
Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016 (2020)

Abstract. Increasing global demand of vegetable oils and biofuels results in significant oil palm expansion in southeastern Asia, predominately in Malaysia and Indonesia. The land conversion to oil palm plantations has posed risks to deforestation (50 % of the oil palm was taken from forest during 19902005; Koh and Wilcove, 2008), loss of biodiversity and greenhouse gas emission over the past decades. Quantifying the consequences of oil palm expansion requires fine-scale and frequently updated datasets of land cover dynamics. Previous studies focused on total changes for a multi-year interval without identifying the exact time of conversion, causing uncertainty in the timing of carbon emission estimates from land cover change. Using Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), ALOS-2 PALSAR-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, we produced an annual oil palm area dataset (AOPD) at 100 m resolution in Malaysia and Indonesia from 2001 to 2016. We first mapped the oil palm extent using PALSAR and PALSAR-2 data for 20072010 and 20152016 and then applied a disturbance and recovery algorithm (Breaks For Additive Season and Trend  BFAST) to detect land cover change time points using MODIS data during the years without PALSAR data (20112014 and 20012006). The new oil palm land cover maps are assessed to have an accuracy of 86.61 % in the mapping step (20072010 and 20152016). During the intervening years when MODIS data are used, 75.74 % of the detected change time matched the timing of actual conversion using Google Earth and Landsat images. The AOPD revealed spatiotemporal oil palm dynamics every year and shows that plantations expanded from 2.59 to 6.39106 ha and from 3.00 to 12.66106 ha in Malaysia and Indonesia, respectively (i.e. a net increase of 146.60 % and 322.46 %) between 2001 and 2016. The higher trends from our dataset are consistent with those from the national inventories, with limited annual average difference in Malaysia (0.2106 ha) and Indonesia (0.17106 ha). We highlight the capability of combining multiple-resolution radar and optical satellite datasets in annual plantation mapping to a large extent by using image classification and statistical boundary-based change detection to achieve long time series. The consistent characterization of oil palm dynamics can be further used in downstream applications. The annual oil palm plantation maps from 2001 to 2016 at 100 m resolution are published in the Tagged Image File Format with georeferencing information (GeoTIFF) at https://doi.org/10.5281/zenodo.3467071 (Xu et al., 2019).

Research areas: Agriculture