Division (IPAD) is responsible for forecasting and assessing global crop production and agricultural yields. IPAD uses a combination of satellite-derived data and land surface and crop modeling for these assessments, particularly in regions that lack traditional ground sensing data. From these analyses, IPAD provides a timely and standardized estimate of the status of global crop production -an essential part of international food security and management in areas and times of agricultural drought or stress. Soil moisture is a critical variable in the IPAD crop forecasting system because crop growth cycles are very dependent upon the near surface soil moisture amounts, particularly for root zone. However, soil moisture is a difficult to sample globally and at present, uncertainty in these soil moisture estimates lead to errors in crop forecasting ability and accuracy.In this study, we have presented the results from a study evaluating a soil moisture data assimilation system designed to integrate satellite-derived soil moisture estimates into a water balance model for improved root-zone soil moisture estimates. Our analysis primarily involves the comparison of multiple soil moisture model estimates with and without the integrated satellite observations over the conterminous United States. From this analysis we can quantitatively evaluate the performance of the data assimilation system and the root-zone soil moisture estimates which will be delivered to IPAD, Our results indicate that the system provides improved root-zone soil moisture estimates over most of the US with some degradation in the northeast and semi-arid areas of the southwest, which may be the result of inappropriate model parameters or poor satellite-derived products over vegetation regions.https://ntrs.nasa.gov/search.jsp?R=20100031160 2018-05-11T07:30:50+00:00Z

Abstract: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 OPEN ACCESSRemote Sensing 2010, 2 1590 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.

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