Evaluating the Utility of Remotely Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring

Abstract

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

Satellite-Based Run-Off Model for Monitoring Drought in Peninsular Malaysia

“…Recent studies have linked rainfall deficits, soil thermal stress and vegetation growth and mortality to drought [2,3,[5][6][7]9,11,12,15,20,25,[27][28][29][30][31][32]36,[50][51][52], highlighting the need for operational drought monitoring systems that can show drought intensity, location and extent [16]. In response, this study devised an operational procedure and algorithm, a run-off model, from TMPA rainfall and MODIS evapotranspiration products for spatiotemporal drought assessment (Figures 3 and 4).…”

An approach to quantifying the efficiency of a Bayesian filter

“…Many efforts have been made to estimate root-zone soil moisture from radiometer observations using data assimilation [e.g., Bolten et al, 2010;Crow and Wood, 2003;Galantowicz et al, 1999;Li and Islam, 1999;Margulis et al, 2002] including several observing system simulation experiments which test the feasibility of such approaches using synthetic observations Dunne and Entekhabi, 2005;Flores et al, 2012;Reichle et al, 2001Reichle et al, , 2002bReichle et al, , 2008. The OSSE described in this section assimilates synthetic observations of soil moisture in the surface soil layer (5 cm) into a three-layer soil moisture model with total depth of 30 cm.…”

Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

“…For example, land-atmosphere models [Delworth and Manabe, 1989, Entekhabi et al, 1996, Ferranti and Viterbo, 2006, precipitation forecasting models Suarez, 2003, Seuffert et al, 2002], regional and global climate models [Dirmeyer, 1999, Mahfouf et al, 1987, Seuffert et al, 2002, and hydrologic models at all scales [Houser et al, 1998, Lakshmi, 1998, Wood, 1997 would benefit from reliable soil moisture information. Similarly, soil moisture is important for flood forecasting [Beck et al, 2009, Dunne andBlack, 1970], drought monitoring and wildfire prediction [Bartsch et al, 2009, Bolten et al, 2010, crop growth and forest regrowth after wildfires [de Wit andvan Diepen, 2007, Kasischke et al, 2007], and malaria outbreak modeling [Montosi et al, 2012]. Soil moisture is an important variable in soil mechanical stability [Horn and Fleige, 2003], which is relevant in trafficability [Flores et al, 2014] and vehicle impact assessment and land rehabilitation [Shoop et al, 2005, Vero et al, 2014.…”