nasa-gesdisc/es-publications-researchareas huggingface dataset:
A review of remote sensing applications in agriculture for food (2020)

The global population is expected to reach 9.8 billion by 2050. There is an exponential growth of food production to meet the needs of the growing population. However, the limited land and water resources, climate change, and an increase in extreme events likely to pose a significant threat for achieving the sustainable agriculture goal. Given these challenges, food security is included in the United Nations' Sustainable Development Goals (SDGs). Since the advent of Sputnik, followed by the Explorer missions, satellite remote sensing is assisting us in collecting the data at global scales. In this work, we review how satellite remote sensing information is utilized to assess and manage agriculture, an important component of ecohydrology. Overall, three critical aspects of agriculture are considered: (a) crop growth and yield through empirical models, physics-based models, and data assimilation in crop models, (b) applications pertaining to irrigation, which include mapping irrigation areas and quantification of irrigation, and (c) crop losses due to pests, diseases, crop lodging, and weeds. The emphasis is on satellite sensors in optical, thermal, microwave, and fluorescence frequencies. We conclude the review with an outlook of challenges and recommendations. This paper is the first of a two-part review series. The second part reviews the role of satellite remote sensing in water security, wherein we discuss the aspects of water quality and quantity along with extremes (floods and droughts).

Research areas: Agriculture
Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season (2022)

The accurate monitoring of crop parameters is important for crop yield prediction and canopy parameter inversion from remote sensing. Process-based and semi-empirical crop models are the main approaches to modeling the temporal changes in crop parameters. However, the former requires too many input parameters and the latter has the problem of poor portability. In this study, new semi-empirical geometric and physical parameters of the maize canopy model (GPMCM) crop model adapted to northeast China were proposed based on a time-series field datasets collected from 11 sites in the Nongan and Changling Counties of Jilin Province, China, during DOY (day of year) 163 to DOY 278 in 2021. The allocation characteristics of and correlations between each maize canopy parameter were investigated for the whole growing season using the 22 algorithms of crop parameters, and the following conclusions were obtained. (1) The high correlation coefficient (R mean = 0.79) of LAI with other canopy parameters indicated that it was a good indicator for predicting other parameters. (2) Better performance was achieved by the regression method based on the two-stage simulation. The root-mean-squared error (RMSE) of geometric parameters including maize height, stem long radius, and short radius were 12.91 cm, 0.74 mm, and 0.73 mm, respectively, and the RMSE of the physical parameters including the FAGB, AGB, VWC, and RWC of the stems and leaves, ranged from 0.05 kg/m2 to 4.24 kg/m2 (2.0% to 12.9% for mean absolute percentage error (MAPE)). (3) The extension of the field-scale GPMCM to the 500 m MODIS-scale still provided a good accuracy (MAPE: 11% to 18.5%) and confirmed the feasibility of the large-scale application of the GPMCM. The proposed CPMCM can predict the temporal dynamics of maize geometric and physical parameters, and it is helpful to establish the forward and reverse models of remote sensing and improve the inversion accuracy of crop parameters.

Research areas: Agriculture
Towards crop yield estimation at a finer spatial resolution using machine learning methods over agricultural regions (2021)

Reliable yield estimation is crucial for food security and agricultural production especially in the intensively agricultural region. This study constructed a gridded yield estimation framework by driving machine learning models with remote sensing vegetation index and meteorological forcing. Among eight machine learning methods, support vector machine (SVM), k-nearest neighbor regression (KNN), and Gaussian process regression (GPR) models outperformed the others. Precipitation, temperature, and the fraction of photosynthetically active radiation are key factors for yield estimation. The yield estimation at county level and regional level were further conducted to explore the scale effect (estimation accuracy varies with spatial resolution). Different scales hold diverse spatial variability information. Finer scales that are more representative of spatial variability generally result in the better accuracy. This study demonstrates that a more accurate yield estimation can be achieved at a finer grid level, thus providing guidelines for agricultural planting structure.

Research areas: Agriculture
Local-scale cereal yield forecasting in Italy: Lessons from different statistical models and spatial aggregations (2020)

Statistical, data-driven methods are considered good alternatives to process-based models for the sub-national monitoring of cereal crop yields, since they can flexibly handle large datasets and can be calibrated simultaneously to different areas. Here, we assess the influence of several characteristics on the ability of these methods to forecast cereal yields at the local scale. We look at two diverse agro-climatic Italian regions and analyze the most relevant types of cereal crops produced (wheat, barley, maize and rice). Models of different complexity levels are built for all species by considering six meteorological and remote sensing indicators as candidate predictive variables. Yield data at three different spatial aggregation scales were retrieved from a comprehensive, farm-level dataset over the period 20012015. Overall, our results suggest the better predictability of summer crops compared to winter crops, irrespective of the model considered, reflecting a more intricate relationship among winter cereals, their physiology and weather patterns. At higher spatial resolutions, more sophisticated modelling techniques resting on feature selection from multiple indicators outperformed more parsimonious linear models. These gains, however, vanished as data were further aggregated spatially, with the predictive ability of all competing models converging at the agricultural district and province levels. Feature-selection models tended to elicit more satellite-based than meteorological indicators, with a preference for temperature indicators in summer crops, whereas variables describing the water content of the soil/plant were more often selected in winter crops. The selected features were, in general, equally distributed along the plant growing cycle.

Research areas: Agriculture
Determining paddy field spatiotemporal distribution and temperature influence using remote sensing in Songnen Plain, Northeastern China (2020)

This study investigates changes in the spatial distributions of paddy fields in the Songnen Plain (Chinas largest commodity grain-producing area) using the Satellite Pour lObservation de la TerreVegetation (SPOT VEGETATION) time series product data to simulate the phenological characteristics of paddy rice growth. According to the analysis, the characteristics of paddy fields are different from other vegetation cover around late May (15th 10-day). This difference can be used to improve accuracy in identifying paddy fields. Historical maps (19992013) are extracted successfully in this work. Based on the results, a trend map for changes in paddy field areas is generated using a 10-km grid. Based on the verification of paddy field extractions in 2019 using the Project for On-Board Autonomy-Vegetation (PROBA VEGETATION), the trend map is strongly indicative for predicting future spatial distribution changes of paddy fields. These results can be used to provide a reliable theoretical basis for environmental research related to the growth of paddy rice. This study also verifies that paddy rice cultivation is one of the main causes of rising temperatures in the study area.

Research areas: Agriculture
Nitrogen cycling in pastoral livestock systems in Sub‐Saharan Africa: knowns and unknowns (2021)

Pastoral systems are the dominant livestock production system in arid and semiarid regions of sub‑Saharan Africa (SSA). They are often the only form of agriculture that can be practiced due to unfavorable climate and soil fertility levels that prevent crop cultivation. Pastoralism can have negative impacts on the environment, including land degradation, greenhouse gas emissions and other gases to the atmosphere, soil erosion, water pollution and biodiversity loss. Here, we review the current knowledge on nitrogen (N) cycling, storage, and loss pathways, with an emphasis on identification of N emission hotspots. Our review reports a large uncertainty in the amount of N lost as ammonia from excreta and manure storage, as well as N losses via nitrate and DON leaching. We also found that another major N loss pathway (18%), soil N2 emissions, has not yet been measured. In order to summarize the available information, we use a virtual pastoral farm, with characteristics and management practices obtained from a real farm, Kapiti Research Station in Kenya. For outlining N flows at this virtual farm, we used published data, data from global studies, satellite imagery and geographic information system (GIS) tools. Our results show that N inputs in pastoral systems are dominated by atmospheric N deposition (∼80%), while inputs due to biological nitrogen fixation seems to play a smaller role. A major N loss pathway is nitrogen leaching (nitrate &gt; DON) from pastures (33%). Cattle enclosures (bomas), where animals are kept during night, represent N emissions hotspots, representing 16% of the total N losses from the system. N losses via ammonia volatilization and N2O were four and three orders of magnitude higher from bomas than from the pasture, respectively. Based on our results, we further identify future research requirements and highlight the urgent need for experimental data collection to quantify nitrogen losses from manure in animal congregation areas. Such information is needed to improve our understanding on N cycling in pastoral systems in semiarid regions and to provide practical recommendations for managers that can help with decision‑making on management strategies in pastoral systems in semiarid savannas.

Research areas: Agriculture
Energy development and hunter success for mule deer and pronghorn in Wyoming (2017)

Infrastructure associated with energy development influences hunter access and introduces disturbance activities to landscapes that can influence habitat selection and behavior of ungulates. Consequently, habitat loss and hunter access concerns must be addressed by wildlife managers as they consider management of populations of western big game species including mule deer (Odocoileus hemionus) and pronghorn (Antilocapra americana). We evaluated whether increased energy development, as quantified through change in well pad density, has affected hunter success of mule deer and pronghorn. Ungulates tend to avoid energy development; therefore, we also evaluated whether hunting statistics can be used to identify potential effects of energy development on mule deer and pronghorn. We included data from 22 of 39 mule deer and 34 of 46 pronghorn Herd Units across Wyoming, USA, from 1980 to 2012. On average, well pad densities across mule deer Herd Units increased from 0.01 km2 in 1980 to 0.06 km2 in 2012, and well pads in pronghorn Herd Units increased from 0.01 km2 to 0.12 km2 during the same period. Our results indicated that hunter success for mule deer in Wyoming was positively associated with hunter effort, whereas pronghorn hunter success was negatively associated with hunter effort. Hunter success for both species was unaffected by well pad density. We identified a change in mule deer and pronghorn harvest success associated with hunter numbers and effort; however, harvest statistics were not informative in identifying impacts from energy development on mule deer or pronghorn populations.

Research areas: Ecosystems
A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt (2021)

Crop yield maps estimated from satellite data increasingly are used to understand drivers of yield trends and variability, yet satellite-derived regional maps are rarely compared with location-specific yields due to the difficulty of acquiring sub-field ground truth data at scale. In commercial agricultural systems, combine harvesters with onboard yield monitors collect real-time yield data during harvest with high spatial resolution, generating a large ground dataset. Here, we leveraged a yield monitor dataset of over one million maize field observations across the United States Corn Belt from 2008 to 2018 to evaluate the Scalable Crop Yield Mapper (SCYM). SCYM uses region-specific crop model simulations and climate data to interpret vegetation indices from satellite observations, thus enabling efficient sub-field yield estimation across large regions and multiple years without reliance on ground data calibration. We used the ground dataset to compare alternative SCYM model implementations, define minimum required satellite observation criteria, and evaluate the sensitivity of satellite-based yield estimates to on-the-ground variation in management, soil, and annual weather. We found that smoothing annual time series data with harmonic regression increased 30 m pixel-level accuracy from r2 = 0.31 to 0.40 and reduced dependency on specific satellite observation timing, enabling robust yield estimation on 97% of annual maize area using only Landsat data. Agreement improved as the assessment was aggregated to field-level (r2 = 0.45) and county-level (r2 = 0.69) scales, demonstrating the need for fine-resolution ground truth data to better assess sub-field level accuracy in high resolution products. We found that SCYM and ground data showed a similar yield response to management and environmental variation, particularly capturing linear and nonlinear responses to sowing density, soil water holding capacity, and growing season precipitation. However, sensitivity to factors like soil quality and planting date was muted for SCYM estimates compared to ground-based yields. Random forest models were able to match SCYM performance when trained on at least 1000 ground observations, but performed poorly when tested on years and locations not represented in the training data. Our results demonstrate that satellite yield maps can provide much-needed information on multidecadal yield trends and inform yield gap analyses.

Research areas: Agriculture
Climate drives variability and joint variability of global crop yields (2019)

In this study, long-term national-based yields of maize, rice, sorghum and soybean (MRSS) from 1961 to 2013 are decomposed using Robust Principal Component Analysis (RPCA). After removing outliers, the first three principal components (PC) of the persistent yield anomalies are scrutinized to assess their association with climate and to identify co-varying countries and crops. Sea surface temperature anomalies (SSTa), atmospheric and oceanic indices, air temperature anomalies (ATa) and Palmer Drought Severity Index (PDSI) are used to study the association between the PCs and climate. Results show that large-scale climate, especially El Nino-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) are strongly correlated with crop yield variability. Extensive maize harvesting regions in Europe and North America, rice in South America, Oceania and east of Asia, sorghum in west and southeast of Asia, North America and Caribbean and soybean in North and South America, Oceania and south of Asia experienced the influence of local climate variability in this period. Sorghum yield variability across the globe exhibits significant correlations with many atmospheric and oceanic indices. Results indicate that not only do the same crops in many countries co-vary significantly, but different crops, in particular maize, in different PCs also co-vary with other crops. Identifying the association between climate and crop yield variability and recognizing similar and dissimilar countries in terms of yield fluctuations can be informative for the identified nations with regard to the periodic and predictable nature of many large-scale climatic patterns.

Research areas: Agriculture
Efficiency of assimilating leaf area index into a soybean model to assess within-field yield variability (2022)

Methods for accurately estimating within-field yield are essential to improve site-specific crop management and resource use efficiencies, which would be a major step toward sustainable intensification of agricultural systems. We set out to assess the accuracy of within-field soybean yields predicted by two data assimilation methods and to assess these methods assimilation efficiency (AE). Yields were estimated by assimilating remotely sensed leaf area index (LAI) data from Sentinel-2 into a soybean crop growth model on a pixel basis. The LAI data was integrated into the model by Ensemble Kalman Filtering (EnKF) or by recalibrating with the Subplex algorithm (recalibration-based). An open-loop setting which only integrates information on the soil layers was used as a baseline scenario for quantifying the AE. We assessed both data assimilation techniques on eight fields (3067 pixels) in the Corn Belt region (Nebraska, Kansas and Kentucky) in the United States. The data set encompassed substantial variation in crop growth conditions: three growing seasons (2018, 2019 and 2020), rainfed and irrigated fields, and early and late planting dates. Ground truth yield acquired from combine monitors was used to validate the yield estimations. Agreement between predicted and observed yield at pixel level was two times higher for both data assimilation methods compared to the open-loop. The root mean square error (RMSE) was 476 kg.ha1 (RRMSE of 10 %) in the recalibration-based method and 573 kg.ha1 (RRMSE of 12 %) in the EnKF-based method. For both data assimilation methods, assimilating the LAI improved predictions for 68 % of the pixels. For a further 12 % of pixels, there was no accuracy improvement. For the remaining 20 %, AE was positive for one of the two assimilation methods. The high proportion of pixels with positive AE indicates the potential for overcoming the limitations in applying crop models at high spatial resolution by integrating a crop growth indicator. Assimilating an in-season indicator of crop growth (LAI) into a soybean model made it possible to adjust the simulation pathway, thereby greatly improving the accuracy of the yield estimations at the pixel level. This study elucidates the practical applications of data assimilation strategies for fine-scale within-field crop yield mapping.

Research areas: Agriculture
Global manure nitrogen production and application in cropland during 1860–2014: a 5 arcmin gridded global dataset for Earth system modeling (2017)

Abstract. Given the important role of nitrogen input from livestock systems in terrestrial nutrient cycles and the atmospheric chemical composition, it is vital to have a robust estimation of the magnitude and spatiotemporal variation in manure nitrogen production and its application to cropland across the globe. In this study, we used the dataset from the Global Livestock Impact Mapping System (GLIMS) in conjunction with country-specific annual livestock populations to reconstruct the manure nitrogen production during 18602014. The estimated manure nitrogen production increased from 21.4 Tg N yr1 in 1860 to 131.0 Tg N yr1 in 2014 with a significant annual increasing trend (0.7 Tg N yr1, p < 0.01). Changes in manure nitrogen production exhibited high spatial variability and concentrated in several hotspots (e.g., Western Europe, India, northeastern China, and southeastern Australia) across the globe over the study period. In the 1860s, the northern midlatitude region was the largest manure producer, accounting for  52 % of the global total, while low-latitude regions became the largest share ( 48 %) in the most recent 5 years (20102014). Among all the continents, Asia accounted for over one-fourth of the global manure production during 18602014. Cattle dominated the manure nitrogen production and contributed  44 % of the total manure nitrogen production in 2014, followed by goats, sheep, swine, and chickens. The manure nitrogen application to cropland accounts for less than one-fifth of the total manure nitrogen production over the study period. The 5 arcmin gridded global dataset of manure nitrogen production generated from this study could be used as an input for global or regional land surface and ecosystem models to evaluate the impacts of manure nitrogen on key biogeochemical processes and water quality. To ensure food security and environmental sustainability, it is necessary to implement proper manure management practices on cropland across the globe. Datasets are available at https://doi.org/10.1594/PANGAEA.871980 (Zhang et al., 2017).

Research areas: Agriculture
Agroecological measures and circular economy strategies to ensure sufficient nitrogen for sustainable farming (2021)

Sustainable food systems face trade-offs between demands of low environmental pressures per unit area and requirements of increasing production. Organic farming has lower yields than conventional agriculture and requires the introduction of nitrogen (N) fixing legumes in crop rotations. Here we perform an integrated assessment of the feasibility of future food systems in terms of land and N availability and the potential for reducing greenhouse gas (GHG) emissions. Results show that switching to 100% organic farming without additional measures results in N deficiency. Dietary change towards a reduced share of animal products can aggravate N limitations, which can be overcome through the implementation of a combination of agroecological, circular economy and decarbonization strategies. These measures help to recycle and transfer N from grassland. A vegan diet from fully decarbonized conventional production performs similarly as the optimized organic scenario. Sustainable food systems hence require measures beyond the agricultural sector.

Research areas: Agriculture