nasa-gesdisc/es-publications-researchareas huggingface dataset:
Contribution of vegetation change to the surface radiation budget: A satellite perspective (2020)

The surface radiation budget is of crucial importance to ecosystem evolution but varies with complex atmospheric and surface conditions. Vegetation change alters the surface thermal properties and the subsequent radiation budget; however, the vegetation contribution is difficult to isolate from mixed influences. Based on satellite observations, we apply a novel trajectory-based approach to detect the impact of vegetation change on the global surface radiation variation in recent decades (20012016). Satellite data on radiation and vegetation available from the Clouds and the Earth's Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are adopted for this investigation. Methodologically, the surface net radiation (Rn) in the nonchanged vegetation trajectory represents the synthetic result of atmospheric influences and serves as a reference for isolating Rn variations due to vegetation change. The results demonstrate that the multiyear mean of global Rn is 71.57 Wm2 with an increasing trend of 0.053 Wm2yr1. Vegetation change contributes an additional 0.047 Wm2yr1 of radiation in greening regions, accounting for 53.36% of the total increase in Rn. Spatially, the contribution of vegetation presents significant variability, with positive contributions located mainly in western Europe and southern Africa and negative contributions located mainly in parts of Asia and eastern Australia. Physically, the influence of vegetation change on the surface radiation budget originates from its alteration of albedo and emissivity, particularly the former. Specifically, a 1% increase in the normalized difference vegetation index (NDVI) is expected to reduce albedo by 0.003 and increase surface net shortwave radiation by 0.86 Wm2. It can be concluded that the change in albedo by vegetation change has a nonnegligible influence on the surface radiation budget in different regions. These results help capture the physical mechanism responsible for the evolution of Earth's radiation and support environmental management.

Research areas: Land Surface/Agriculture Indicators
A leaf area index data set acquired in Sahelian rangelands of Gourma in Mali over the 2005–2017 period (2019)

Abstract. The leaf area index of Sahelian rangelands and related variables such as the vegetation cover fraction, the fraction of absorbed photosynthetically active radiation and the clumping index were measured between 2005 and 2017 in the Gourma region of northern Mali. These variables, known as climate essential variables, were derived from the acquisition and the processing of hemispherical photographs taken along 1 km linear sampling transects for five contrasted canopies and one millet field. The same sampling protocol was applied in a seasonally inundated Acacia open forest, along a 0.5 km transect, by taking photographs of the understorey and the tree canopy. These observations collected over more than a decade, in a remote and not very accessible region, provide a relevant and unique data set that can be used for a better understanding of the Sahelian vegetation response to the current rainfall changes. The collected data can also be used for satellite product evaluation and land surface model development and validation. This paper aims to present the field work that was carried out during 13 successive rainy seasons, the measured vegetation variables, and the associated open database. Finally, a few examples of data use are shown. DOI of the referenced data set: https://doi.org/10.17178/AMMA-CATCH.CE.Veg_Gh.

Research areas: Land Surface/Agriculture Indicators
NDVI–Climate relationships in high-latitude mountains of Alaska and Yukon Territory (2019)

High-latitude systems in northwestern Canada and Alaska have warmed rapidly. The aim of this study was to examine how a remotely sensed proxy of vegetation productivity varied among mountain ranges with respect to elevation and climate from 20022017. Our study area included high-latitude mountains in Alaska, USA, and Yukon Territory, Canada, ranging from cold arctic mountains in the tundra biome to warmer interior mountains areas within the boreal biome. We used the annual maximum Normalized Difference Vegetation Index (NDVI) data from the 250-m MODIS NDVI product as a proxy of maximum growing season photosynthetic activity. The long-term (16-year) and interannual pattern of maximum NDVI was investigated with respect to elevation, July temperature, and July precipitation classes within four climatic mountain regions. The July temperature lapse rate was consistently linear, whereas the long-term maximum NDVI lapse rate was nonlinear. At lower elevations, the high-precipitation region had the highest NDVI, whereas the interior mountains region had the highest NDVI at higher elevations. The long-term maximum NDVI was negatively correlated with July precipitation for areas with July temperature below 12C. Above 12C, NDVI was positively correlated with July precipitation, with the greatest rate of NDVI increase with precipitation at the warmest July temperature class. The pattern of interannual peak NDVI with respect to July temperature was not as strong as the long-term pattern; however, the only interannual negative correlation between peak NDVI and July temperature was at lower elevations within the interior mountains. We concluded that among a regional climatic gradient of mountain areas, low growing season temperature and length were likely constraining vegetation productivity, and lower growing season moisture may be an important constraint at the warmest interior mountains region.

Research areas: Land Surface/Agriculture Indicators
Trends in streamflow, evapotranspiration, and groundwater storage across the Amazon Basin linked to changing precipitation and land cover (2020)

Study region
The study region is the Amazon River Basin, which controls globally important water and energy fluxes.
Study focus
In the face of a changing climate and landscape, it is critical that we understand how, where, and why surface water resources are changing. Specifically, we must consider holistic changes to the water cycle to understand how water resources are affected by climate change and landscape alterations. In this study, we investigate changes to all major components of the water balance across the entire Amazon Basin. We seek to understand: 1) how changes to land cover and precipitation affect streamflow, 2) how these factors affect evapotranspiration and groundwater storage water balance components, and 3) how changes to the water balance partitioning may in turn alter streamflows.
New hydrological insights
We find significant changes to streamflow of 9.5 mm/yr on average across the Amazon Basin. Streamflow alterations show a spatially variable pattern, with increasing discharge in the northern and western portions of the basin, and decreasing discharge in the southern and eastern basin. We also observe significant changes in evapotranspiration of 29 mm/yr and groundwater storage increases of 7.1 mm/yr. Together, these results indicate that studies of streamflow change in the Amazon should consider changes to the whole water budget, including understudied aspects of groundwater storage across the Basin.

Research areas: Land Surface/Agriculture Indicators
Strain Field Features and Three-Dimensional Crustal Deformations Constrained by Dense GRACE and GPS Measurements in NE Tibet (2022)

The continuing impact between the Eurasia Plate and India results in the thickening and shortening of the N-S Tibetan Plateau. There has been strong tectonic movement along the boundary of the zones of deformation of the NE corner of the Tibetan plateau (NET) since the new tectonic period, with its dynamic mechanisms remaining controversial. Here, we use observations of 39 Continuous Global Positioning System (CGPS) gauges and 451 Crustal Movement Observation Network of China (CMONOC) campaign-mode stations to detect the three-dimensional deformation of the crust in the NET. Improved processing procedures are implemented to strengthen the patterns of strain throughout the NET. The principal component analysis (PCA) technique is introduced to decompose the time series into spatial eigenvectors and principal components (PCs), and the first three PCs are used to estimate and rectify common mode errors (CMEs). In addition, GRACE observations are used to detect deformation changes that account for non-tidal oceanic mass loading, hydrological loading, and surface pressure. The rectified deformation of the crust indicates the anisotropic nature of both the subsidence and uplift, and that the highest uplift rate of the Longmen Shan fault uplift reaches 7.13  0.53 mm/yr. Finally, the horizontal velocity is further used to enumerate the strain rates throughout the NET. The results show that the shear band retained property in line with the strike-slip fault along the Altyn Tagh fault, the Qilian Shan faults, the Haiyuan fault, the West Qinling fault, the East Kunlun fault, and the Longmen Shan fault. In addition, the results further indicate that the whole NET shows a strong relationship with the mean principal rates of horizontal shortening strain. Extension and compression of the crust reasonably describe its sinking and uplifting.

Research areas: Land Surface/Agriculture Indicators
Retrieval of soil moisture by integrating Sentinel-1A and MODIS data over agricultural fields (2020)

Soil moisture is an essential variable in the land surface ecosystem, which plays an important role in agricultural drought monitoring, crop status monitoring, and crop yield prediction. High-resolution radar data can be combined with optical remote-sensing data to provide a new approach to estimate high-resolution soil moisture over vegetated areas. In this paper, the Sentinel-1A data and the Moderate Resolution Imaging Spectroradiometer (MODIS) data are combined to retrieve soil moisture over agricultural fields. The advanced integral equation model (AIEM) is utilized to calculate the scattering contribution of the bare soil surface. The water cloud model (WCM) is applied to model the backscattering coefficient of vegetated areas, which use two vegetation parameters to parameterize the scattering and attenuation properties of vegetation. Four different vegetation parameters extracted from MODIS products are combined to predict the scattering contribution of vegetation, including the leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), normalized difference vegetation index (NDVI), and the enhanced vegetation index (EVI). The effective roughness parameters are chosen to parameterize the AIEM. The Sentinel-1A and MODIS data in 2017 are used to calibrate the coupled model, and the datasets in 2018 are used for soil moisture estimation. The calibration results indicate that the Sentinel-1A backscattering coefficient can be accurately predicted by the coupled model with the Pearson correlation coefficient (R) ranging from 0.58 to 0.81 and a root mean square error (RMSE) ranging from 0.996 to 1.401 dB. The modeled results show that the retrieved soil moisture can capture the seasonal dynamics of soil moisture with R ranging from 0.74 to 0.81. With the different vegetation parameter combinations used for parameterizing the scattering contribution of the canopy, the importance of suitable vegetation parameters for describing the scattering and attenuation properties of vegetation is confirmed. The LAI is recommended to characterize the scattering properties. There is no obvious clue for selecting vegetation descriptors to characterize the attenuation properties of vegetation. These promising results confirm the feasibility and validity of the coupled model for soil moisture retrieval from the Sentinel-1A and MODIS data.

Research areas: Land Surface/Agriculture Indicators
Modis Snowline Elevation Changes During Snowmelt Runoff Events in Europe (2018)

This study evaluates MODIS snow cover characteristics for large number of snowmelt runoff events in 145 catchments from 9 countries in Europe. The analysis is based on open discharge daily time series from the Global Runoff Data Center database and daily MODIS snow cover data. Runoff events are identified by a base flow separation approach. The MODIS snow cover characteristics are derived from Terra 500 m observations (MOD10A1 dataset, V005) in the period 2000-2015 and include snow cover area, cloud coverage, regional snowline elevation (RSLE) and its changes during the snowmelt runoff events. The snowmelt events are identified by using estimated RSLE changes during a runoff event. The results indicate that in the majority of catchments there are between 3 and 6 snowmelt runoff events per year. The mean duration between the start and peak of snowmelt runoff events is about 3 days and the proportion of snowmelt events in all runoff events tends to increase with the maximum elevation of catchments. Clouds limit the estimation of snow cover area and RSLE, particularly for dates of runoff peaks. In most of the catchments, the median of cloud coverage during runoff peaks is larger than 80%. The mean minimum RSLE, which represents the conditions at the beginning of snowmelt events, is situated approximately at the mean catchment elevation. It means that snowmelt events do not start only during maximum snow cover conditions, but also after this maximum. The mean RSLE during snowmelt peaks is on average 170 m lower than at the start of the snowmelt events, but there is a large regional variability.

Research areas: Cryospheric Indicators, Land Surface/Agriculture Indicators
Uncertainty in data for hydrological ecosystem services modelling: Potential implications for estimating services and beneficiaries for the CAZ Madagascar (2018)

This study assesses the differences in modelled total runoff and modelled runoff delivered to people between different rainfall and population datasets in the Ankeniheny Zhamena Corridor (CAZ) of Eastern Madagascar. Runoff is estimated using the WaterWorld hydrological model driven by six rainfall datasets, and population is derived from five population datasets. Model results for runoff under different rainfall datasets lead to variability in runoff (coefficient of variation) up to 99% for single months and 60% in the dry season. These differences are much larger than differences in estimated runoff between baseline and complete deforestation scenario for each rainfall dataset. Population estimates for the CAZ range from 1.2 to 2 million between the population datasets. Differences in runoff under different rainfall datasets lead to an average of 356,000 people estimated to receive 90% more runoff and nearly 750,000 people estimated to receive 50% more or less runoff relative to a baseline rainfall dataset. Therefore, the choice of rainfall data in hydrological ecosystem services modelling has a large influence on estimates of ecosystem service flows highlighting the need for modellers to justify their data choices and report on uncertainties in results, particularly in light of potential policy decisions based on modelled outcomes.

Research areas: Land Surface/Agriculture Indicators
Regional assessment of soil microbial functional diversity of Douglas‐fir forests (2014)

Extracellular enzyme activity (EEA) provides a functional fingerprint of soil microbial communities and can be an indicator of change following disturbance. This study examined the EEA of nine managed, Douglasfir [Pseudotsuga menziesii (Mirb.) Franco] dominated forests in the Pacific Northwest of the United States. It assessed the variability of EEA both within and among sites and correlated EEA with environmental variables covered by the broad geographical range of the study sites. This characterization was linked with measures of community size, C and N cycling ability, and other soil properties, such as texture and nutrient status, to provide insights into extracellular enzyme production and microbial function. Principal components analysis and multiple response permutation procedure results indicated that EEA is highly variable among sites but much less variable within sites. It also revealed that the functional fingerprint of the soil tended to be more similar for soils within the same soil order. Additionally, the results implied that phosphatase and the Ccycling enzymes glucosidase and cellobiohydrolase are constitutive in these systems, whereas the Nscavenging enzymes Nacetylglucosaminidase and leucine aminopepdidase are facultative. Furthermore, the Ncycling EEA suggests an N limitation in these sites despite N fertilization. This characterization will be critical in assessing microbial community changes following timber harvest.

Research areas: Land Surface/Agriculture Indicators
Urban greenness and survival in lung cancer patients: A registry-based (2021)

Background. Previous studies have reported inverse associations between urban greenness and mortality. However, findings regarding contemporaneous and cumulative average greenness exposure are inconsistent. A potential explanation is the weak effects of greenness among the general population. This study assessed the associations between contemporaneous and cumulative average greenness exposure and survival in lung cancer patients. <P />Methods. An open cohort consisting of lung cancer patients enrolled from 1 January 2001 was studied. The cohort was followed up until 31 December 2018. We evaluated contemporaneous and cumulative greenness exposure using the normalized difference vegetation index (NDVI) within a 250 m buffer of each patient's geocoded residential address. The association between NDVI and mortality was evaluated using both time-fixed and time-varying Cox models. Their difference was compared either. <P />Results. In time-fixed Cox models, a 0.1-unit increment in contemporaneous and cumulative average NDVI was associated with 17% (95% confidence interval [CI], 15-18%) and 7% (95% CI, 5-8%) reductions in mortality rate, respectively. Time-varying Cox models consistently revealed significant associations, but their magnitudes were lower, at 2% (95% CI, 1-3%) for both contemporaneous and cumulative average NDVI. Compared to the time-fixed models, the time-varying ones exhibited better fit for the proportional hazard assumption in Cox regressions with more conservative results. <P />Conclusion. Urban greenness is positively associated with an increased chance of survival in lung cancer patients. Compared to time-fixed Cox models, the results of time-varying Cox models were more conservative. This study confirms the beneficial health effects of urban greenness, which may facilitate the improvement of public health by urban green space planning.

Research areas: Public Health, Habitat Conversion/Fragmentation
Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in … (2022)

Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and processing multi-source data to generate risk predictors and consuming significant time and computational resources to train and validate models locally. In this context, this study proposed a framework for dengue risk prediction by integrating big geospatial data cloud computing based on Google Earth Engine (GEE) platform and artificial intelligence modeling on the Google Colab platform. It enables defining the epidemiological calendar, delineating the predominant area of dengue transmission in cities, generating the data of risk predictors, and defining multi-date ahead prediction scenarios. We implemented the experiments based on weekly dengue cases during 20132020 in the Federal District and Fortaleza, Brazil to evaluate the performance of the proposed framework. Four predictors were considered, including total rainfall (Rsum), mean temperature (Tmean), mean relative humidity (RHmean), and mean normalized difference vegetation index (NDVImean). Three models (i.e., random forest (RF), long-short term memory (LSTM), and LSTM with attention mechanism (LSTM-ATT)), and two modeling scenarios (i.e., modeling with or without dengue cases) were set to implement 1- to 4-week ahead predictions. A total of 24 models were built, and the results showed in general that LSTM and LSTM-ATT models outperformed RF models; modeling could benefit from using historical dengue cases as one of the predictors, and it makes the predicted curve fluctuation more stable compared with that only using climate and environmental factors; attention mechanism could further improve the performance of LSTM models. This study provides implications for future dengue risk prediction in terms of the effectiveness of GEE-based big geospatial data processing for risk predictor generation and Google Colab-based risk modeling and presents the benefits of using historical dengue data as one of the input features and the attention mechanism for LSTM modeling.

Research areas: Public Health
Health risks of extended exposure to low-level UV radiation–An analysis of ground-based and satellite-derived data (2022)

This study aims to indicate the importance of revising current health recommendations concerning the duration of exposure and individual sensitivity of the skin to solar ultraviolet (UV) radiation. For this purpose, a 16-year data series (20052020) of erythemal radiant exposure (Her) and UV index (UVI) for Serbia was analyzed. The UV-related risk was estimated for lighter skin (skin phototypes I-IV) under prolonged exposure on days when maximum UVI was below the recommended protection threshold (UVIlow days, for UVI < 3). Risk assessment was performed for seasonal exposure using satellite-derived data (OMUVBd product) previously validated by ground-based measurements in Novi Sad. The assessment of harmful effects included an analysis of the relation between the daily maximum UVI and the corresponding daily Her, the occurrence of UVIlow days, the exceedance of minimal erythema dose (MED), and the minimum duration of exposure to induce erythema (tMED) for all lighter skin phototypes. It was found that the share of UVIlow days in the total number of days in Serbia increases with the latitude, with the highest percentage in winter (up to 69.454%) and the lowest in summer (up to 3.468%). The results show that the daily Her frequently exceeded the harmful threshold for lighter skin phototypes I-IV (on average by 91.521, 84.923, 70.556, and 56.515%, respectively) on UVIlow days. It was found that prolonged exposure on days with a maximum of UVI = 2 poses a significant risk of erythema for all lighter skin phototypes, even for a duration of 3 h in the middle of the day, as well as medium risk for UVI = 1, and an absence of risk for UVI = 0. The results suggest that health recommendations should be revised, especially in the mid-latitudes, where the share of UVIlow days is large, and in areas where the population is predominantly lighter-skinned.

Research areas: Public Health