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Remote Sens. 2013, 5(7), 3331-3356; doi:10.3390/rs5073331
Article

A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields

1
,
1,* , 2
,
3
 and
4
1 Earth Systems Science Center, National Space Science and Technology Center, University of Alabama in Huntsville, 320 Sparkman Drive, Huntsville, AL 35805, USA 2 Atmospheric Science Department, University of Alabama in Huntsville, 320 Sparkman Drive, Huntsville, AL 35805, USA 3 Earth System Science Interdisciplinary Center, University of Maryland-College Park, College Park, MD 20740, USA 4 Hydrology and Remote Sensing Laboratory, USDA Agricultural Research Service, Beltsville, MD 20705, USA
* Author to whom correspondence should be addressed.
Received: 18 May 2013 / Revised: 28 June 2013 / Accepted: 5 July 2013 / Published: 12 July 2013
(This article belongs to the Special Issue Advances in Remote Sensing of Crop Water Use Estimation)
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Abstract

Biophysical crop simulation models are normally forced with precipitation data recorded with either gauges or ground-based radar. However, ground-based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would be to employ satellite-based observations of either precipitation or soil moisture. Satellite observations of precipitation are currently not considered capable of forcing the models with sufficient accuracy for crop yield predictions. However, deduction of soil moisture from space-based platforms is in a more advanced state than are precipitation estimates so that these data may be capable of forcing the models with better accuracy. In this study, a mature two-source energy balance model, the Atmosphere Land Exchange Inverse (ALEXI) model, was used to deduce root zone soil moisture for an area of North Alabama, USA. The soil moisture estimates were used in turn to force the state-of-the-art Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation model. The study area consisted of a mixture of rainfed and irrigated cornfields. The results indicate that the model forced with the ALEXI moisture estimates produced yield simulations that compared favorably with observed yields and with the rainfed model. The data appear to indicate that the ALEXI model did detect the soil moisture signal from the mixed rainfed/irrigation corn fields and this signal was of sufficient strength to produce adequate simulations of recorded yields over a 10 year period.
Keywords: crop modeling; remote sensing; soil moisture; ALEXI; DSSAT; maximum entropy crop modeling; remote sensing; soil moisture; ALEXI; DSSAT; maximum entropy
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Mishra, V.; Cruise, J.F.; Mecikalski, J.R.; Hain, C.R.; Anderson, M.C. A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields. Remote Sens. 2013, 5, 3331-3356.

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