Since 2000, Russia experienced large-scale increases (149%) in wheat production and starting in 2018 accounted for almost 25% of all global wheat exports. This growth indicates significant land cover and land use change (LCLUC) and is driven primarily by winter wheat growth adding 9 million hectares of cropland area, a 117% increase. Here we show that 40% of southwestern Russia experienced changes in land cover and land use including a 29% growth in winter wheat cropland. Of this growth, 66% is attributed to winter wheat cropland expansion (planting in new areas) and 34% to intensification (increased planting rate). The observed growth in winter cropland was latitudinally dichotomous where northernmost regions experienced areal expansion and southernmost regions intensification. Based on rates of winter cropland use, we conclude that there remains significant capacity for winter crop intensification and provide probable trajectories of continued growth.

Ukraine plays an important role in global food security. Ukraine produces about half of the global sunflower oil production, and Ukraine-produced barley, corn, wheat and rapeseed are exported to Europe, China, India, North Africa and the Middle East countries. However, after the invasion of Russia in February 2022 and blocking the ports in Black Sea, the global food prices increased. Uncertainties over crop production over the Russia-occupied territories in Ukraine also impacted the food prices. In this paper, we developed approaches to detect and map crop planted areas over Ukraine using Synthetic Aperture Radar (SAR). This information is very important to assess the agricultural activities, especially on occupied territories, and potentially reduce global food market volatility.

In this study we present a model to forecast wheat yield based on the evolution of the Difference Vegetation Index (DVI) and the Growing Degree Days (GDD), presented in Franch et al. (2015), but adapted to Franch et al. (2019) model. Additionally, we explore how the Land Surface Temperature (LST) can be included into the model and if this parameter adds any value to the model when combined with the optical information. This study is applied to MODIS data at 1km resolution to monitor the national and state level yield of winter wheat in the United States and Ukraine from 2001 to 2019.

Crop yield information at field scale is important for farmers, crop insurance companies and agricultural communities in general. In this study, a wide range of ground-collected yield data was used to develop crop yield forecast models for the two internationally important crops: wheat and soybeans. A deep neural network (NN), a long short-term memory (LSTM), was trained for both crops individually. For each crop, the LSTM model was trained for two different scenarios including using Synthetic Aperture Radar (SAR)-only data as first scenario and using integration of SAR and optical satellite data as a second scenario. The root mean square error (RMSE) and coefficient of determination$(R^{2})$were estimated for each scenario. The results demonstrated that the accuracies improved from RMSE of 516.7 kg/ha and$R^{2}$of 0.79 (scenario 1) to RMSE of 433.77 kg/ha and$R^{2}$of 0.87 (scenario 2) for soybeans. For wheat, the accuracies improved from RMSE of 617.14 kg/ha and$R^{2}$of 0.83 (scenario 1) to RMSE of 423.04 kg/ha and$R^{2}$of 0.87 (scenario 2). These results show that using SAR data and their integration with optical satellite data is a promising approach for crop yield forecast at field scale.

Measures to slow the spread of COVID-19 are affecting the food supply chain in many ways including the availability of inputs, labor, transport, and cross-border trade. Earth Observation (EO) from satellites can provide timely and transparent evidence on the extent and impact these measures have on the agricultural activities and related food systems. EO capabilities required to address information needs related to global food supply, national harvesting statistics, national relief programs and labor-intensive crop production were made available over the trilateral COVID-19 Earth Observing Dashboard. The presented use cases make full use of the combined satellites fleet of NASA, ESA and JAXA as well as the expertise of the EO community.

In this study we present a methodology to develop a global winter wheat crop calendar based on the existing crop calendar products from FAO and GEOGLAM Crop Monitor in the framework of the WorldCereal project. It is based on integrating both datasets by building on the accuracy from Crop Monitor and the spatial resolution from the Food and Agriculture Organization of the United Nations (FAO). Additionally, given the global extent of WorldCereal and the gaps that both products present at global scale, we simulated the crop calendars in those areas not covered by any of the products. To do so, we integrated a Regression-Kriging model considering as training data the calendars derived from both products and based on the latitude, height and distance to the coast (DTC).

Timely detection of harvested fields is important for insurance companies. In this study, polarimetric Synthetic Aperture Radar (SAR) parameters derived from dual-polarization Sentinel-1 sensor have been tested for harvest monitoring over corn and soybean fields. Intensity at VH polarization, volume scattering derived from M-Chi decomposition and S0and S1elements of Scattering Matrix have been tested. Harvest maps derived from SAR have been compared with the results derived from Normalized Difference Vegetation Index (NDVI) which was extracted from Sentinel-2 imageries. Very promising and close results derived from both optical and SAR sensors. This study demonstrated that combination of SAR and optical sensors could be used to produce dense time-series of crop harvest maps.

Agriculture, while critically important to humanity in terms of jobs and sustenance, is also one of the largest contributors to climate change, the largest user of freshwater, and one of the primary drivers of land cover and land use change. Accordingly, the need for timely, accurate, and actionable information to understand these impacts and dynamics is growing. NASA Harvest-NASA's multisectoral food security and agriculture program - contends that Earth observations play a critical role in both the attainment of and the monitoring of progress toward the United Nations 2030 Agenda for Sustainable Development. In order to really deliver on what will be in 2030 six decades of investment in Earth observations with respect to agricultural productivity, sustainability, and land use, we posit herein several critical areas of necessary investigation, collaboration, partnership, and action for 2020–2030 and beyond.

The GEO Global Agricultural Monitoring (GEOGLAM) Initiative launched in 2011 is focused on improving the use of Earth Observations for Agricultural Monitoring. With the growing availability of satellite data and advances in high performance computing, there are new opportunities for agricultural monitoring, for example providing up-to-date information on cropland extent, crop condition and improved crop production forecasts. The Initiative is strengthening agricultural monitoring systems at the global and national level, coordinating activities to develop robust methods and provide policy relevant information on agricultural production to support markets and food security early warning.

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. In Becker-Reshef et al. (2010) and Franch et al. (2015) we developed an empirical generalized model for forecasting winter wheat yield. In this study we present a new model based on the extrapolation of the pure wheat signal (100% of wheat within the pixel) from MODIS data at 1 km resolution and using the Difference Vegetation Index (DVI). The model has been applied to monitor the national and state level yield of winter wheat in the United States from 2001 to 2016.

An accurate global map of current cropland is needed to tackle the agricultural challenges of the next decades. Several remote sensing products have provided with global cropland maps. To create the GEOGLAM best available global crop-specific maps, we integrated the SPAM 2005 with regional maps developed by different countries and international organisms. The comparison with other available global cropland products showed a clear linear agreement between products and a tendency towards overestimation of GEOGLAM best available global crop-specific maps. In addition, a sensitivity analysis was performed using the forecast production model for winter wheat developed in previous studies. It showed how at constant values of adjusted NDVI small variations of cultivated area (5000 ha) produced a 17% rate of variation in the estimated production values.

Wheat is one of the most important cereal crops in the world. Timely and accurate forecast of wheat yield and production at global scale is vital in implementing food security policy. Becker-Reshef et al. (2010) developed a generalized empirical model for forecasting winter wheat production using remote sensing data and official statistics. This model was implemented using static wheat maps. In this paper, we analyze the impact of incorporating yearly wheat masks into the forecasting model. We propose a new approach of producing in season winter wheat maps exploiting satellite data and official statistics on crop area only. Validation on independent data showed that the proposed approach reached 6% to 23% of omission error and 10% to 16% of commission error when mapping winter wheat 2-3 months before harvest. In general, we found a limited impact of using yearly winter wheat masks over a static mask for the study regions.

The frequent occurrences of drought in the Great Plains of United States have led to significant crop loss. Timely monitoring of drought-induced agricultural impacts, especially at a large scale from remote sensing, is of great importance for ensuring food security. Following the authors' prior drought impact work in Kansas, based on MODIS data, this study investigated the impacts of drought on winter wheat in Oklahoma (OK) and Nebraska (NE) during the main growing season at 8-day intervals, both at the state and agricultural statistics district level, and then explored the spatial variability of drought impacts. Despite some variability, drought shows generally increasing impacts during winter wheat main growing season for both states, reaching the peak around Mid-Late April in OK and Mid-Late May in NE during their corresponding vegetative peaks. As compared to NE, winter wheat has higher planting density in OK and drought shows more significant impacts in OK.

Soil moisture is a good indicator of agricultural drought. The Advanced Scatterometer Surface Soil Moisture (ASCAT SSM) product provides daily estimates of surface soil moisture and shows high potential for drought monitoring at global scale. However, its regional application still needs further investigation. In this paper, the effectiveness of ASCAT SSM product for drought monitoring in 3 major winter wheat producing states of the US (Oklahoma, Kansas and Nebraska) was evaluated using intensive in situ measurements from North America Soil Moisture Database (NASMD). The results indicate ASCAT SSM product is generally effective for characterizing drought conditions across different stations, with better representation of soil moisture at 5cm than 10cm depth. ASCAT SSM product shows varying capability for drought monitoring across the entire year, with a better capability observed around March-July and October-November. Also, better drought monitoring capability is found for the ASCAT descending SSM than for the ascending SSM.

Despite growing agricultural production in the past decades, drought remains the leading factor of crop loss, threatening food security. Satellite observation has proved a practical and dynamic tool for drought monitoring. However, the drought impacts on agriculture at large scales still remain poorly examined, especially at a finer spatial and temporal resolution from remote sensing. This study investigates the impacts of drought on crop yield in Kansas wheat growing regions using MODIS data. The results show that drought has time-varying impacts during the growing season with significant impacts observed between Mid-April and Early-June. During this key alert period, we generally find high agricultural impacts with the most severe drought effects during the grain filling stage around Mid-May. This research provides a first step towards prototyping an agricultural drought monitoring system, which can alert crop analysts of agricultural drought vulnerable areas/periods and provide tools for assessing crop outlooks.

Wheat is the principal winter crop in Pakistan during the winter (Rabi) season and an important food staple. Several months after harvest the local crop reporting administration in Punjab Province releases wheat yield and production statistics. The statistical estimates are based on field data collected manually from a fixed list of villages. Early season wheat production forecasts allow to better plan for wheat transactions on the world market, maintain adequate stocks, inform policy making, set support prices and increase market efficiency. This study describes a satellite-based methodology for early season wheat production forecasting. Wheat area is derived from a MODIS satellite image time series. Yield is estimated by regressing historic yields against MODIS-NDVI. The results for yield were within 6.7% and for production within 19.6% of final reported values for the 2011/12 Rabi season.