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In order to secure the necessary image acquisitions for global agricultural monitoring applications, we must first articulate Earth observation (EO) requirements for diverse agricultural landscapes and cropping systems. Crucial to this task is the identification of agricultural growing season timing at a meaningful spatial scale, so as to better define the necessary periods of image acquisition. To this end, 10 years of MODIS Terra Surface Reflectance imagery have been used to determine phenological transition dates including start of season, peak period, and end of season at 0.5° globally. This is the first set of global, satellite-derived, cropland-specific calendar dates for major field crops within a 0.5°, herein calledagricultural growing season calendarsPreliminary comparison against ground-based crop-specific calendars is performed, highlighting the utility of this approach for articulating growing season timing and its interannual and within-region variability. This research provides critical inputs for defining the EO requirements for the Global Agricultural Monitoring initiative (GEOGLAM), an effort by the Group on Earth Observations (GEO) to synergize existing national and regional observation systems for improved agricultural production and food security monitoring. [ABSTRACT FROM AUTHOR]

Global agricultural monitoring utilizes a variety of Earth observations (EO) data spanning different spectral, spatial, and temporal resolutions in order to gather information on crop area, type, condition, calendar, and yield, among other applications. Categorical requirements for space-based monitoring of major agricultural production areas have been articulated based on best practices established by the Group on Earth Observation's (GEO) Global Agricultural Monitoring Community (GEOGLAM) of Practice, in collaboration with the Committee on Earth Observation Satellites (CEOS). We present a method to transform generalized requirements for agricultural monitoring in the context of GEOGLAM into spatially explicit (0.05°) Earth observation (EO) requirements for multiple resolutions of data. This is accomplished through the synthesis of the necessary remote sensing-based datasets concerning where (crop mask, when (growing calendar, and how frequently imagery is required (considering cloud cover impact throughout the agricultural growing season. Beyond this provision of the framework and tools necessary to articulate these requirements, investigated in depth is the requirement for reasonably clear moderate spatial resolution (10-100 m) optical data within 8 days over global within-season croplands of all sizes, a data type prioritized by GEOGLAM and CEOS. Four definitions of "reasonably clear" are investigated: 70%, 80%, 90%, or 95% clear. The revisit frequency required (RFR) for a reasonably clear view varies greatly both geographically and throughout the growing season, as well as with the threshold of acceptable clarity. The global average RFR for a 70% clear view within 8 days is 3.9-4.8 days (depending on the month), 3.0-4.1 days for 80% clear, 2.2-3.3 days for 90% clear, and 1.7-2.6 days for 95% clear. While some areas/times of year require only a single revisit (RFR = 8 days) to meet their reasonably clear requirement, generally the RFR, regardless of clarity threshold, is below to greatly below the 8 day mark, highlighting the need for moderate resolution optical satellite systems or constellations with revisit capabilities more frequent than 8 days. This analysis is providing crucial input for data acquisition planning for agricultural monitoring in the context of GEOGLAM. [ABSTRACT FROM AUTHOR]

Agriculture is a highly dynamic process in space and time, with many applications requiring data with both a relatively high temporal resolution (at least every 8 days) and fine-to-moderate (FTM < 100 m) spatial resolution. The relatively infrequent revisit of FTM optical satellite observatories coupled with the impacts of cloud occultation have translated into a barrier for the derivation of agricultural information at the regional-to-global scale. Drawing upon the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) Initiative's general satellite Earth observation (EO) requirements for monitoring of major production areas, Whitcraft et al. (this issue) have described where, when, and how frequently satellite data acquisitions are required throughout the agricultural growing season at 0.05°, globally. The majority of areas and times of year require multiple revisits to probabilistically yield a view at least 70%, 80%, 90%, or 95% clear within eight days, something that no present single FTM optical observatory is capable of delivering. As such, there is a great potential to meet these moderate spatial resolution optical data requirements through a multi-space agency/multi-mission constellation approach. This research models the combined revisit capabilities of seven hypothetical constellations made from five satellite sensors--Landsat 7 Enhanced Thematic Mapper (Landsat 7 ETM+), Landsat 8 Operational Land Imager and Thermal Infrared Sensor (Landsat 8 OLI/TIRS), Resourcesat-2 Advanced Wide Field Sensor (Resourcesat-2 AWiFS), Sentinel-2A Multi-Spectral Instrument (MSI), and Sentinel-2B MSI--and compares these capabilities with the revisit frequency requirements for a reasonably cloud-free clear view within eight days throughout the agricultural growing season. Supplementing Landsat 7 and 8 with missions from different space agencies leads to an improved capacity to meet requirements, with Resourcesat-2 providing the largest incremental improvement in requirements met. The best performing constellation can meet 71%-91% of the requirements for a view at least 70% clear, and 45%-68% of requirements for a view at least 95% clear, varying by month. Still, gaps exist in persistently cloudy regions/periods, highlighting the need for data coordination and for consideration of active EO for agricultural monitoring. This research highlights opportunities, but not actual acquisition rates or data availability/access; systematic acquisitions over actively cropped agricultural areas as well as a policy which guarantees continuous access to high quality, interoperable data are essential in the effort to meet EO requirements for agricultural monitoring. [ABSTRACT FROM AUTHOR]

In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. Timely information on global crop production is indispensable for combating the growing stress on the world's crop production and for securing both short-term and long-term stable and reliable supply of food. Global agriculture monitoring systems are critical to providing this kind of intelligence and global earth observations are an essential component of an effective global agricultural monitoring system as they offer timely, objective, global information on croplands distribution, crop development and conditions as the growing season progresses. The Global Agriculture Monitoring Project (GLAM), a joint NASA, USDA, UMD and SDSU initiative, has built a global agricultural monitoring system that provides the USDA Foreign Agricultural Service (FAS) with timely, easily accessible, scientifically-validated remotely-sensed data and derived products as well as data analysis tools, for crop-condition monitoring and production assessment. This system is an integral component of the USDA's FAS Decision Support System (DSS) for agriculture. It has significantly improved the FAS crop analysts' ability to monitor crop conditions, and to quantitatively forecast crop yields through the provision of timely, high-quality global earth observations data in a format customized for FAS alongside a suite of data analysis tools. FAS crop analysts use these satellite data in a 'convergence of evidence' approach with meteorological data, field reports, crop models, attaché reports and local reports. The USDA FAS is currently the only operational provider of timely, objective crop production forecasts at the global scale. These forecasts are routinely used by the other US Federal government agencies as well as by commodity trading companies, farmers, relief agencies and foreign governments. This paper discusses the operational components and new developments of the GLAM monitoring system as well as the future role of earth observations in global agricultural monitoring. [ABSTRACT FROM AUTHOR]

• Crop Monitor provides consensus crop assessments for countries at risk. • The goal is to reduce ambiguity in crop assessments for food security decisions. • Achieved through international coordination sharing of data, methods and expertise • EO play key role in early warning especially in countries at risk. • Early warning of reduced production is key component of SDG2 Zero Hunger. [ABSTRACT FROM AUTHOR]

Remotely sensed Earth observations (EO) have their history firmly rooted in agricultural monitoring, and more recently with applications in food production, food security, and sustainable agriculture. Still, after more than 45 years of observing the Earth's land surface, usage of EO data by operational monitoring entities concerned with global agriculture is uneven. One reason for this is a gap in continuous communication and collaboration between those who undertake research and development of methods for cropland assessment and monitoring, and those who have the mandate to report on agricultural indicators at a national, regional, and global scales. The recent international policy focus on the United Nations 2030 Agenda for Sustainable Development via its Sustainable Development Goals (SDGs) is giving increased attention to measurements and indicators for monitoring and measuring progress for meeting these goals. Satellite EO provide a source of measurements beyond traditional census data collection and statistical reporting. In this vein, this overview paper describes the current and potential uses of EO data and tools that can support the SDGs, particularly highlighting the activities of the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) Initiative. GEOGLAM is composed of agricultural ministries, intergovernmental organizations, research entities, universities, space agencies, and members of industry concerned with agricultural monitoring. This GEOGLAM community has a broad portfolio of activities which provide information on the state and changes in agricultural production and land use that can be considered as contributions to both supporting the attainment of several of the 17 SDGs and many of their 169 Targets, as well as monitoring their achievement via the Global Indicator Framework. GEOGLAM contributes in particular to Goal 2: Zero Hunger, but also has less immediately apparent contributions in the realms of water (Goal 6), responsible consumption and production (Goal 12), climate action (Goal 13), life on land (Goal 15), and global partnerships for sustainable development (Goal 17). We further characterize the applicability and use of EO data products and tools as they correspond with the United Nations Interagency Expert Group on Sustainable Development Goals (IAEG-SDGs) Global Indicator Framework. This inventory will be complemented by a discussion of the intersection of other policy mandates with the SDGs in the agriculture and food security contexts, and will conclude with a discussion of approaches to improving awareness of EO value and bridging the gap between policy and EO communities, to the societal benefit of all with no one left behind. • GEOGLAM activities & EO data are mapped to UN SDGs with many opportunities identified. • UN guidance states, data must be created for multiple uses; we ID examples and means. • EO can/does support SDGs; adoption hindered by poor cross-community collaboration. • Levering overlapping mandates supports 2030 Agenda & reduces implementation burden. [ABSTRACT FROM AUTHOR]

The challenge of global food security in the face of population growth, conflict, and climate change requires a comprehensive understanding of cropped areas, irrigation practices, and the distribution of major commodity crops like maize and wheat. However, such understanding should preferably be updated at seasonal intervals for each agricultural system rather than relying on a single annual assessment. Here we present the European Space Agency-funded WorldCereal system, a global, seasonal, and reproducible crop and irrigation mapping system that addresses existing limitations in current global-scale crop and irrigation mapping. WorldCereal generates a range of global products, including temporary crop extent, seasonal maize and cereal maps, seasonal irrigation maps, seasonal active cropland maps, and model confidence layers providing insights into expected product quality. The WorldCereal product suite for the year 2021 presented here serves as a global demonstration of the dynamic open-source WorldCereal system. Validation of the products was done based on best available reference data per product. A global statistical validation for the temporary crop extent product resulted in user's and producer's accuracies of 88.5 % and 92.1 %, respectively. For crop type, a verification was performed against a newly collected street view dataset (overall agreement 82.5 %) and a limited number of publicly available in situ datasets (reaching minimum agreement of 80 %). Finally, global irrigated-area estimates were derived from available maps and statistical datasets, revealing the conservative nature of the WorldCereal irrigation product. The WorldCereal system provides a vital tool for policymakers, international organizations, and researchers to better understand global crop and irrigation patterns and to inform decision-making related to food security and sustainable agriculture. Our findings highlight the need for continued community efforts such as additional reference data collection to support further development and to push the boundaries for global agricultural mapping from space. The global products are available at 10.5281/zenodo.7875104 (Van Tricht et al., 2023). [ABSTRACT FROM AUTHOR]

The challenge of global food security in the face of population growth, conflict and climate change requires a comprehensive understanding of cropped areas, irrigation practices and the distribution of major commodity crops like maize and wheat. However, such understanding should preferably be updated at seasonal intervals for each agricultural system rather than relying on a single annual assessment. Here we present the European Space Agency funded WorldCereal system, a global, seasonal, and reproducible crop and irrigation mapping system that addresses existing limitations in current global-scale crop and irrigation mapping. WorldCereal generates a range of global products, including temporary crop extent, seasonal maize and cereals maps, seasonal irrigation maps, seasonal active cropland maps, and confidence layers providing insights into expected product quality. The WorldCereal product suite for the year 2021 presented here serves as a global demonstration of the dynamic open-source WorldCereal system. The presented products are fully validated, e.g., global user's and producer's accuracies for the annual temporary crop product are 88.5% and 92.1%, respectively. The WorldCereal system provides a vital tool for policymakers, international organizations, and researchers to better understand global crop and irrigation patterns and inform decision-making related to food security and sustainable agriculture. Our findings highlight the need for continued community efforts such as additional reference data collection to support further development and push the boundaries for global agricultural mapping from space. The global products are available at https://doi.org/10.5281/zenodo.7875104 (Van Tricht et al., 2023). [ABSTRACT FROM AUTHOR]

Wheat is the staple food of Punjab province of Pakistan, which contributes more than 75% of the total national production. Accurate and timely forecasting of wheat yield is a cornerstone for monitoring food security and planning for agricultural markets, but the efficiency of the current system for near real-time forecasting should be improved. In this research paper, we developed a model to forecast wheat yield before harvest for each of eight individual districts and for Punjab province as a whole. The model uses weather and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived normalized difference vegetation index (NDVI) data for 2001–2014 (14 years) to calculate Random Forest (RF) statistical models using 15 independent variables. Temperature, rainfall, sunshine hours, growing degree days, and MODIS-derived NDVI for each of the eight districts of Punjab province were used to forecast yield for the year 2014. The same independent variables were used to forecast wheat yield of the whole Punjab from 2001 to 2014 by excluding the respective year from training set. Sunshine hour data were not available for all districts and therefore we tested using temperature data and average latitude-based solar radiation as surrogates. The root mean square errors (RMSEs) of the forecast results of the whole of Punjab province were 147.7 kg ha−1and 148.7 kg ha−1with a mean error of less than 5% using average and generic RFs, respectively. Forecasts for individual districts showedR2of 0.95 with RMSE of 175.6 kg ha−1and 5.86% mean error. [ABSTRACT FROM AUTHOR]

Purpose: As stated in the United Nations Global Assessment Report 2022 Concept Note, decision-makers everywhere need data and statistics that are accurate, timely, sufficiently disaggregated, relevant, accessible and easy to use. The purpose of this paper is to demonstrate scalable and replicable methods to advance and integrate the use of earth observation (EO), specifically ongoing efforts within the Group on Earth Observations (GEO) Work Programme and the Committee on Earth Observation Satellites (CEOS) Work Plan, to support risk-informed decision-making, based on documented national and subnational needs and requirements. Design/methodology/approach: Promotion of open data sharing and geospatial technology solutions at national and subnational scales encourages the accelerated implementation of successful EO applications. These solutions may also be linked to specific Sendai Framework for Disaster Risk Reduction (DRR) 2015–2030 Global Targets that provide trusted answers to risk-oriented decision frameworks, as well as critical synergies between the Sendai Framework and the 2030 Agenda for Sustainable Development. This paper provides examples of these efforts in the form of platforms and knowledge hubs that leverage latest developments in analysis ready data and support evidence-based DRR measures. Findings: The climate crisis is forcing countries to face unprecedented frequency and severity of disasters. At the same time, there are growing demands to respond to policy at the national and international level. EOs offer insights and intelligence for evidence-based policy development and decision-making to support key aspects of the Sendai Framework. The GEO DRR Working Group and CEOS Working Group Disasters are ideally placed to help national government agencies, particularly national Sendai focal points to learn more about EOs and understand their role in supporting DRR. Originality/value: The unique perspective of EOs provide unrealized value to decision-makers addressing DRR. This paper highlights tangible methods and practices that leverage free and open source EO insights that can benefit all DRR practitioners. [ABSTRACT FROM AUTHOR]

Existing crop mapping models, rely heavily on reference (calibration) data obtained from remote sensing observations. However, the transferability of such models in space and time, without the need for additional extensive datasets remains a significant challenge. There is still a large gap in developing generalized classification models capable of mapping specific or multiple crops with minimal calibration data. In this study, we present a generalized automatic approach for sunflower mapping at 20-m spatial resolution, using the C-band Sentinel-1 (S1) synthetic aperture radar (SAR) data driven by previously developed phenological metrics. These metrics characterize the directional behavior of the sunflower head, capturing distinct backscattering responses in SAR data acquired from ascending and descending orbits. Specifically, we utilize SAR-derived backscatter values in VH and VV polarization, as well as their ratio VH/VV, as input features to a random forest classifier that was calibrated for the year 2022 in Ukraine. This model is further directly applied to selected sites for multiple years in Ukraine (generalization in time) and other major sunflower producing countries (generalization in space): Ukraine for 2018–2020, and Hungary, France, Russia and USA for 2018. Our results reveal that the model based on features acquired from descending orbits outperforms its ascending orbit counterpart because of the directional behavior of sunflower: user's accuracy (UA) of 96%, producer's accuracy (PA) of 97% and F-score of 97% (descending) compared to UA of 90%, PA of 89% and F-score of 90% (ascending). When generalized to other years and countries, our model achieves an F-score exceeding 77% for all cases, with the highest F-scores (>91%) observed in Ukraine and Russia sites and the lowest (77%) for the US site. We further utilize the produced maps (pixel-based) for the selected regions and years to estimate sunflower planted areas using a statistical sampling-based approach. Our estimates yield the relative root mean square error (RMSE) as 19.7% of the mean area, when compared to the reference data from official statistics and reference maps. These findings demonstrate the robustness of our proposed approach across space and time in generating accurate sunflower maps, its ability to mitigate cloud cover issues through spaceborne SAR data acquisitions, and its potential for obtaining estimates of sunflower planted areas. This research emphasizes the importance of developing interpretable and domain-specific machine learning models that can be readily extended to multiple geographical regions with little to no labelled datasets. • SAR-based generalized model for sunflower mapping. • Model applicable across space and time. • Directional behavior of sunflower impacts classification accuracy. • Model applicable to Ukraine, Russia, Hungry, France and USA. • Model yields average PA = 0.87 ± 0.08 and UA = 0.86 ± 0.08. [ABSTRACT FROM AUTHOR]

Cloud cover impedes optical satellite remote sensing instruments from obtaining clear views of the Earth's surface. Meanwhile, agriculture is a highly dynamic process, with significant changes in crop biomass and condition often occurring within roughly a week. The Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) Initiative represents international efforts to improve the satellite-based monitoring of agricultural processes at multiple temporal and spatial scales. Within this context, it is necessary to understand how cloud cover impacts the probability of securing reasonably clear views of croplands using passive optical Earth observations as the agricultural growing season progresses. To this end, we employ 10–13 years of twice daily 0.05° MODIS Terra (AM) and Aqua (PM) surface reflectance quality assessment cloud flags to investigate diurnal, geographical, and seasonal (early, mid, late, and non-agricultural growing season) characteristics of cloud cover presence frequency and pervasiveness (amount) over global agricultural areas. To provide insight into the ability of hypothetical missions with two modeled revisit frequencies ( f = 2, 4 days) to return reasonably clear views at a rate sufficient to track changes in crop biomass and condition, we show the percentage of 8 day compositing periods throughout the agricultural growing season for which a given clarity requirement (at least 70%, 80%, 90%, or 100% cloud-free) could be met. This research shows that the early and mid-agricultural growing season, which are important periods for crop type area identification and crop yield forecasting, are characterized by both frequent and pervasive cloud extent. Many important agricultural areas during this and other portions of the agricultural growing season are so persistently and pervasively occluded by clouds that less than half of their 8 day composites would be even 70% clear, suggesting that in these areas/time periods, optical, polar-orbiting imaging is not likely to be a viable option for operational monitoring and alternatives (e.g. microwave synthetic aperture radar, SAR) ought to be considered. Further, for most agricultural areas of the world, regardless of seasonality, morning acquisitions are more likely to return reasonably clear views, an important consideration in the planning of future optical, polar-orbiting Earth observing missions with agricultural monitoring science objectives. These results are an important contribution toward the articulation of Earth observation data requirements for global agricultural monitoring. [ABSTRACT FROM AUTHOR]

Policy makers, government planners and agricultural market participants in Pakistan require accurate and timely information about wheat yield and production. Punjab Province is by far the most important wheat producing region in the country. The manual collection of field data and data processing for crop forecasting by the provincial government requires significant amounts of time before official reports can be released. Several studies have shown that wheat yield can be effectively forecast using satellite remote sensing data. In this study, we developed a methodology for estimating wheat yield and area for Punjab Province from freely available Landsat and MODIS satellite imagery approximately six weeks before harvest. Wheat yield was derived by regressing reported yield values against time series of four different peak-season MODIS-derived vegetation indices. We also tested deriving wheat area from the same MODIS time series using a regression-tree approach. Among the four evaluated indices, WDRVI provided more consistent and accurate yield forecasts compared to NDVI, EVI2 and saturation-adjusted normalized difference vegetation index (SANDVI). The lowest RMSE values at the district level for forecast versus reported yield were found when using six or more years of training data. Forecast yield for the 2007/2008 to 2012/2013 growing seasons were within 0.2% and 11.5% of final reported values. Absolute deviations of wheat area and production forecasts from reported values were slightly greater compared to using the previous year's or the three- or six-year moving average values, implying that 250-m MODIS data does not provide sufficient spatial resolution for providing improved wheat area and production forecasts. [ABSTRACT FROM AUTHOR]

Remote sensing derived datasets (e.g. Leaf Area Index (LAI)) are increasingly being used in process based cropping system models to improve the prediction skill of the simulations when implementing operationally at regional scale. However, challenges such as inadequate quality of the available remote sensing data products and high reliance of models on climate variables and their uncertainties still exist. To address these challenges, we developed Geo-CropSim, a spatial modeling framework to use high quality remote sensing products in the Environmental Policy Integrated Climate (EPIC) agroecosystem model to regulate simulated processes and improve predictions of crop yield and evapotranspiration. Geo-CropSim comprises three main features 1) pixel level model initialization using crop emergence dates; 2) ability of the EPIC model to read in the PROSAIL (i.e. combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model) inversion-based crop type LAI; and 3) a stress adjustment function to regulate simulated stress using LAI anomalies. To understand its performance, we implemented it over the State of Nebraska to estimate corn (Zea mays L.) and soybean (Glycine max [Merr.]) yields and evapotranspiration (ET) for 2012 (drought year) and 2015 (normal year) at 500-m resolution. Results showed that emergence dates and seasonal LAI captured spatial and temporal differences in crop progression (e.g. delayed planting in 2015) and growth (e.g. declined LAI in 2012) driven by regional differences in crop management and weather conditions very well. These differences were reflected in Geo-CropSim yield estimates, and showed improved spatial and temporal details compared over those from EPIC simulations obtained without using remote sensing derived emergence and LAI. Results revealed that Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) of Geo-CropSim yield estimates, computed based on USDA-NASS reported yields, were 18.85% and 1.22 Mg ha−1 for corn, and 17.90% and 0.46 Mg ha−1 for soybeans, respectively, which are substantially lower than those of original EPIC estimates (MAPE = 33.74% and RMSE = 2.18 Mg ha−1 for corn; and MAPE = 40.71% and RMSE = 0.98 Mg ha−1 for soybeans). Further, Geo-CropSim was able to capture ET and transpiration dynamics reasonably well (e.g. 10–12 % lower values for soybeans compared to corn values), and showed good agreement with flux measurements (i.e. R2 values of 0.63 and 0.72, RMSE values of 29.88 and 33.41 mm, and MAPE values of 5.0% and 6.8% for corn and soybean, respectively). Overall, this study demonstrated that Geo-CropSim has considerable potential to serve as a reliable operational tool to assess crop yields and water use under various cropping systems and to help in regional yield monitoring and water resource management. [ABSTRACT FROM AUTHOR]

This study examines the suitability of 250 m MODIS (MODerate Resolution Imaging Spectroradiometer) data for mapping global cropland extent. A set of 39 multi-year MODIS metrics incorporating four MODIS land bands, NDVI (Normalized Difference Vegetation Index) and thermal data was employed to depict cropland phenology over the study period. Sub-pixel training datasets were used to generate a set of global classification tree models using a bagging methodology, resulting in a global per-pixel cropland probability layer. This product was subsequently thresholded to create a discrete cropland/non-cropland indicator map using data from the USDA-FAS (Foreign Agricultural Service) Production, Supply and Distribution (PSD) database describing per-country acreage of production field crops. Five global land cover products, four of which attempted to map croplands in the context of multiclass land cover classifications, were subsequently used to perform regional evaluations of the global MODIS cropland extent map. The global probability layer was further examined with reference to four principle global food crops: corn, soybeans, wheat and rice. Overall results indicate that the MODIS layer best depicts regions of intensive broadleaf crop production (corn and soybean), both in correspondence with existing maps and in associated high probability matching thresholds. Probability thresholds for wheat-growing regions were lower, while areas of rice production had the lowest associated confidence. Regions absent of agricultural intensification, such as Africa, are poorly characterized regardless of crop type. The results reflect the value of MODIS as a generic global cropland indicator for intensive agriculture production regions, but with little sensitivity in areas of low agricultural intensification. Variability in mapping accuracies between areas dominated by different crop types also points to the desirability of a crop-specific approach rather than attempting to map croplands in aggregate. [ABSTRACT FROM AUTHOR]

The inability of a farmer to plant an insured crop by the policy's final planting date can pose financial challenges for the grower and cause reduced production for a widely impacted region. Prevented planting is primarily caused by excess moisture or rainfall such as the catastrophic flooding and widespread conditions that prevented active field work in the midwestern region of United States in 2019. While the Farm Service Agency reports the number of such "prevent plant" acres each year at the county scale, field-scale maps of prevent plant fields—which would enable analyses related to assessing and mitigating the impact of climate on agriculture—are not currently available. The aim of this study is to demonstrate a method for mapping likely prevent plant fields based on flood mapping and historical cropland maps. We focused on a study region in eastern South Dakota and created flood maps using Landsat 8 and Sentinel 1 images from 2018 and 2019. We used automatic threshold-based change detection using NDVI and NDWI to accentuate changes likely caused by flooding. The NDVI change detection map showed vegetation loss in the eastern parts of the study area while NDWI values showed increased water content, both indicating possible flooding events. The VH polarization of Sentinel 1 was also particularly useful in identifying potential flooded areas as the VH values for 2019 were substantially lower than those of 2018, especially in the northern part of the study area, likely indicating standing water or reduced biomass. We combined the flood maps from Landsat 8 and Sentinel 1 to form a complete flood likelihood map over the entire study area. We intersected this flood map with a map of fallow pixels extracted from the Cropland Data Layer to produce a map of predicted prevent plant acres across several counties in South Dakota. The predicted figures were within 10% error of Farm Service Agency reports, with low errors in the most affected counties in the state such as Beadle, Hanson, and Hand. [ABSTRACT FROM AUTHOR]

This paper presents a simple and efficient image processing method for estimating the number of coconut trees in the Tonga region using very high spatial resolution data (30 cm) in the blue, green, red and near infrared spectral bands acquired by the WorldView-3 sensor. The method is based on the detection of tree shadows and the further analysis to reject false detection using geometrical properties of the derived segments. The algorithm is evaluated by comparing coconut tree counts derived by an expert through photo-interpretation over 57 randomly distributed (4% sampling rate) segments of 200 m × 200 m over the Vaini region of the Tongatapu island. The number of detected trees agreed within 5% versus validation data. The proposed method was also evaluated over the whole Tonga archipelago by comparing satellite-derived estimates to the 2015 agricultural census data—the total tree counts for both Tonga and Tongatapu agreed within 3%. [ABSTRACT FROM AUTHOR]

Spatially explicit information on crop distribution is essential for market information, food security, and agricultural sustainability. However, high-resolution crop maps are unavailable for most countries of the world. In this study, we developed an operational workflow and produced the first openly-available 10-m resolution maize and soybean map over China. We also derived area estimates for maize and soybean extent for 2019 using a stratified, two-stage, cluster sampling design and ground data collected for the entire country. We developed a multi-scale, multi-temporal procedure for mapping, in which field data were used as training to map maize and soybean over the first-stage sample of 10 km × 10 km equal-area blocks with PlanetScope and Sentinel-2 data. Then, the classified blocks were used as training to map maize and soybean for the country with wall-to-wall Sentinel-2 data using a random forests approach. We used all available Sentinel-2 surface reflectance data acquired between April and October 2019, applied quality assurance, including cloud and shadow masking, and created monthly image composites as inputs for the random forests analysis. We derived maize and soybean area estimates using the field sample data and a regression estimator. Utilizing the probability output layer of the random forests models, we found and applied empirical probability thresholds that matched map-based crop area estimates with sample-based area estimates. Maize area in China in 2019 was estimated to be 330,609 ± 34,109 km2 (± value is the standard error), and soybean area was estimated to be 78,107 ± 12,969 km2. Validated using the field sample data as reference, our crop map had an overall accuracy of 91.8 ± 1.2%. The user's and producer's accuracies for the maize class were 93.9 ± 2.5% and 79.2 ± 3.6%, and for the soybean class were 63.6 ± 12.1% and 61.9 ± 11.8%. Our map-based maize and soybean area estimates had close agreement with government reports at the provincial and prefectural levels, with r2 of 0.90 and 0.92 for maize, and 0.93 and 0.94 for soybean, respectively. Our workflow can generate internally consistent results for crop area estimation and crop mapping simultaneously. As Sentinel-2 data are being acquired consistently and very-high-resolution commercial satellite data are increasingly available, our established workflow may be applied in an operational setting for annual crop mapping in China and other countries. • First openly available 10-m crop map of China at 92% overall accuracy. • Field observations collected over sample locations across the country. • Map-based crop area consistent with sample-based area estimates at national level. • Workflow can be operationalized for annual crop mapping. [ABSTRACT FROM AUTHOR]

Abstract Global and regional scale agricultural monitoring systems aim to provide up-to-date information regarding food production to different actors and decision makers in support of global and national food security. To help reduce price volatility of the kind experienced between 2007 and 2011, a global system of agricultural monitoring systems is needed to ensure the coordinated flow of information in a timely manner for early warning purposes. A number of systems now exist that fill this role. This paper provides an overview of the eight main global and regional scale agricultural monitoring systems currently in operation and compares them based on the input data and models used, the outputs produced and other characteristics such as the role of the analyst, their interaction with other systems and the geographical scale at which they operate. Despite improvements in access to high resolution satellite imagery over the last decade and the use of numerous remote-sensing based products by the different systems, there are still fundamental gaps. Based on a questionnaire, discussions with the system experts and the literature, we present the main gaps in the data and in the methods. Finally, we propose some recommendations for addressing these gaps through ongoing improvements in remote sensing, harnessing new and innovative data streams and the continued sharing of more and more data. Highlights • Eight global and regional agricultural monitoring systems are compared • Gaps in data are described, where cropland maps, crop calendars and meteorological data are viewed as the most critical • Gaps in methods include the need for better predictions of yield and crop production and how to operationalize research methods • New sources of remote sensing data, greater data sharing and new sources of information, e.g. from mobile phones, will all help to improve agricultural monitoring [ABSTRACT FROM AUTHOR]

Knowledge on geographical location and distribution of crops at global, national and regional scales is an extremely valuable source of information for many applications. Traditional approaches to crop mapping using remote sensing data rely heavily on reference or ground truth data in order to train/calibrate classification models. As a rule, such models are only applicable to a single vegetation season and should be recalibrated to be applicable for other seasons. This paper addresses the problem of early season large-area winter crop mapping using Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) time-series and growing degree days (GDD) information derived from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product. The model is based on the assumption that winter crops have developed biomass during early spring while other crops (spring and summer) have no biomass. As winter crop development is temporally and spatially non-uniform due to the presence of different agro-climatic zones, we use GDD to account for such discrepancies. A Gaussian mixture model (GMM) is applied to discriminate winter crops from other crops (spring and summer). The proposed method has the following advantages: low input data requirements, robustness, applicability to global scale application and can provide winter crop maps 1.5–2 months before harvest. The model is applied to two study regions, the State of Kansas in the US and Ukraine, and for multiple seasons (2001–2014). Validation using the US Department of Agriculture (USDA) Crop Data Layer (CDL) for Kansas and ground measurements for Ukraine shows that accuracies of > 90% can be achieved in mapping winter crops 1.5–2 months before harvest. Results also show good correspondence to official statistics with average coefficients of determination R 2 > 0.85. [ABSTRACT FROM AUTHOR]

The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980s to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR surface reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geolocation, improvement of cloud masking, and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream leaf area index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by Becker-Reshef et al. (2010) and Franch et al. (2015) are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980s, the results have errors equivalent to those derived from MODIS. [ABSTRACT FROM AUTHOR]

To predict regional-scale winter wheat yield, we developed a crop model and data assimilation framework that assimilated leaf area index (LAI) derived from Landsat TM and MODIS data into the WOFOST crop growth model. We measured LAI during seven phenological phases in two agricultural cities in China’s Hebei Province. To reduce cloud contamination, we applied Savitzky–Golay (S–G) filtering to the MODIS LAI products to obtain a filtered LAI. We then regressed field-measured LAI on Landsat TM vegetation indices to derive multi-temporal TM LAIs. We developed a nonlinear method to adjust LAI by accounting for the scale mismatch between the remotely sensed data and the model’s state variables. The TM LAI and scale-adjusted LAI datasets were assimilated into the WOFOST model to allow evaluation of the yield estimation accuracy. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors during key phenological stages. We used the shuffled complex evolution–University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters. Finally, we simulated winter wheat yield in a 1-km grid for cells with at least 50% of their area occupied by winter wheat using the optimized WOFOST, and aggregated the results at a regional scale. The scale adjustment substantially improved the accuracy of regional wheat yield predictions ( R 2 = 0.48; RMSE = 151.92 kg ha −1 ) compared with the unassimilated results ( R 2 = 0.23; RMSE = 373.6 kg ha −1 ) and the TM LAI results ( R 2 = 0.27; RMSE = 191.6 kg ha −1 ). Thus, the assimilation performance depends strongly on the LAI retrieval accuracy and the scaling correction. Our research provides a scheme to employ remotely sensed data, ground-measured data, and a crop growth model to improve regional crop yield estimates. [ABSTRACT FROM AUTHOR]

A new 1 km global IIASA- IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo-Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA- IFPRI cropland product has been validated using very high-resolution satellite imagery via Geo-Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo-Wiki. The first ever global field size map was produced at the same resolution as the IIASA- IFPRI cropland map based on interpolation of field size data collected via a Geo-Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the website. [ABSTRACT FROM AUTHOR]

World agriculture is facing a great challenge since it is necessary to find a sustainable way to increase food production. Current trends in advancing the agriculture sector are based on leveraging remote sensing technology and the use of biostimulants. However, the efficient implementation of both of these on a commercial scale for the purposes of productivity improvement remains a challenge. Thus, by proposing a crop monitoring strategy based on remote sensing data, this paper aims to verify and anticipate the impact of applying a Glycinebetaine biostimulant (GB) on the final yield. The study was carried out in a rice-producing area in Eastern Spain (Valencia) in 2021. GB was applied by drone 33 days after sowing (tillering phase). Phenology was monitored and crop production parameters were determined. Regarding satellite data, Sentinel-2 cloud-free images were obtained from sowing to harvest, using the bands at 10 m. Planet data were used to evaluate the results from Sentinel-2. The results show that GB applied 33 days after sowing improves both crop productive parameters and commercial yield (13.06% increase). The design of the proposed monitoring strategy was based on the dynamics and correlations between the visible (green and red) and NIR bands. The analysis showed differences when comparing the GB and control areas, and permitted the determination of the moment in which the effect of GB on yield (tillering and maturity) may be greater. In addition, an index was constructed to verify the crop monitoring strategy, its mathematical expression being: NCMI = (NIR − (red + green))/(NIR + red + green). Compared with the other VIs (NDVI, GNDVI and EVI2), the NCMI presents a greater sensitivity to changes in the green, red and NIR bands, a lower saturation phenomenon than NDVI and a better monitoring of rice phenology and management than GNDVI and EVI2. These results were evaluated with Planet images, obtaining similar results. In conclusion, in this study, we confirm the improvement in rice crop productivity by improving sustainable plant nutrition with the use of biostimulants and by increasing the components that define crop yield (productive tillers, spikelets and grains). Additionally, crop monitoring using remote sensing technology permits the anticipation and understanding of the productive behavior and the evolution of the phenological stages of the crop, in accordance with crop management. [ABSTRACT FROM AUTHOR]

Rice is considered one of the most important crops in the world. According to the Food and Agriculture Organization of the United Nations (FAO), rice production has increased significantly (156%) during the last 50 years, with a limited increase in cultivated area (24%). With the recent advances in remote sensing technologies, it is now possible to monitor rice crop production for a better understanding of its management at field scale to ultimately improve rice yields. In this work, we monitor within-field rice production of the two main rice varieties grown in Valencia (Spain) JSendra and Bomba during the 2020 season. The sowing date of both varieties was May 22–25, while the harvesting date was September 15–17 for Bomba and October 5–8 for JSendra. Rice yield data was collected over 66.03 ha (52 fields) by harvesting machines equipped with onboard sensors that determine the dry grain yield within irregular polygons of 3–7 m width. This dataset was split in two, selecting 70% of fields for training and 30% for validation purposes. Sentinel-2 surface reflectance spectral data acquired from May until September 2020 was considered over the test area at the two different spatial resolutions of 10 and 20 m. These two datasets were combined assessing the best combination of spectral reflectance bands (SR) or vegetation indices (VIs) as well as the best timing to infer final within-field yields. The results show that SR improves the performance of models with VIs. Furthermore, the correlation of each spectral band and VIs with the final yield changes with the dates and varieties. Considering the training data, the best correlation with the yields is obtained on July 4, with R2 for JSendra of 0.72 at 10 m and 0.76 at 20 m resolution, while the R2 for Bomba is 0.87 at 10 m and 0.92 at 20 m resolution. Based on the validation dataset, the proposed models provide within-field yield modelling Mean Absolute Error (MAE) of 0.254 t⋅ha−1 (Mean Absolute Percentage Error, MAPE, of 3.73%) for JSendra at 10 m (0.240 t⋅ha−1; 3.48% at 20 m) and 0.218 t⋅ha−1 (MAPE 5.82%) for Bomba (0.223 t⋅ha−1; 5.78% at 20 m) on July 4, that is three months before harvest. At parcel level the model's MAE is 0.176 t⋅ha−1 (MAPE 2.61%) for JSendra and 0.142 t⋅ha−1 (MAPE 4.51%) for Bomba. These results confirm the close correlation between the rice yield and the spectral information from satellite imagery. Additionally, these models provide a timeliness overview of underperforming areas within the field three months before the harvest where farmers can improve their management practices. Furthermore, it highlights the importance of optimum agronomic management of rice plants during the first weeks of rice cultivation (40–50 days after sowing) to achieve high yields. [ABSTRACT FROM AUTHOR]

On 10 August 2020, a series of intense and fast-moving windstorms known as a derecho caused widespread damage across Iowa's (the top US corn-producing state) agricultural regions. This severe weather event bent and flattened crops over approximately one-third of the state. Immediate evaluation of the disaster's impact on agricultural lands, including maps of crop damage, was critical to enabling a rapid response by government agencies, insurance companies, and the agricultural supply chain. Given the very large area impacted by the disaster, satellite imagery stands out as the most efficient means of estimating the disaster impact. In this study, we used time-series of Sentinel-1 data to detect the impacted fields. We developed an in-season crop type map using Harmonized Landsat and Sentinel-2 data to assess the impact on important commodity crops. We intersected a SAR-based damage map with an in-season crop type map to create damaged area maps for corn and soybean fields. In total, we identified 2.59 million acres as damaged by the derecho, consisting of 1.99 million acres of corn and 0.6 million acres of soybean fields. Also, we categorized the impacted fields to three classes of mild impacts, medium impacts and high impacts. In total, 1.087 million acres of corn and 0.206 million acres of soybean were categorized as high impacted fields. [ABSTRACT FROM AUTHOR]