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Title:   Author  item
                                            MISHRA, V - University Of Alabamaitem
                                            CRUISE, J - University Of Alabamaitem
                                            MECIKALSKI, J - University Of Alabamaitem
                                            HAIN, C - University Of Marylanditem
                                            Anderson, Martha
  Submitted to: Remote Sensing
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 7/5/2013
                                    Publication  Date: 7/12/2013
                                    Publication URL: http://handle.nal.usda.gov/10113/59945Citation: Mishra, V., Cruise, J.F., Mecikalski, J., Hain, C., Anderson, M.C. 2013. A remote-sensing driven tool for estimating crop stress and yields. Remote Sensing. 5(7): 3331-3356.Interpretive Summary: Physically based crop models simulate crop growth, carbon uptake, reproductive stage and at-harvest yield given inputs relating to microclimate (solar radiation, rainfall, temperature, wind, vapor pressure), soil properties and management activities.   Soil moisture is a critical variable in these models, but is a difficult quantity to model with sufficient accuracy – particularly in data limited regions in the world.   Often the required precipitation and soil texture data are not available.  This paper explores the possibility of linking the Decision Support System for Agrotechnology Transfer (DSSAT) crop modeling system with remote sensing information about soil moisture conditions, inferred using the Atmosphere-Land Exchange Inverse (ALEXI) model developed by ARS scientists.  ALEXI provides routine information about crop water use and stress conditions that may be useful for updating soil moisture variables within the DSSAT modeling framework, allowing implementation in a gridded mode in areas where ground-based meteorological information is lacking.  This coupled remote sensing – crop modeling system was tested over irrigated and rainfed cornfields in northern Alabama, and was able to reproduce observed county-level yields for the years 2000-2009 with improved accuracy in comparison with model simulations using local measurements of rainfall.   This kind of remote-sensing driven crop modeling system could have significant utility in monitoring global food security, potentially providing early warning of reductions in yield due to developing crop stress conditions.
                                Technical Abstract: Biophysical crop simulation models are normally forced with precipitation data recorded with either gages 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 a state-of-the-art crop simulation model. The study area consisted of a mixture of rain-fed and irrigated cornfields. The results indicated that the model forced with the ALEXI moisture estimates produced yield simulations that better matched observed yields than did the model forced strictly with observed rainfall. The data appear to indicate that the ALEXI model did detect the soil moisture signal from the mixed rainfed/irrigated corn fields and this signal was of sufficient strength to produce adequate simulations of recorded yields over a 10 year period.


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