Journal
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
Volume 33, Issue 7, Pages 1283-1295Publisher
SPRINGER
DOI: 10.1007/s00477-019-01700-3
Keywords
Maize yields; Data assimilation; DSSAT; Remotely sensed data
Categories
Funding
- United States Department of Agriculture (USDA) [2015-68007-23210]
- National Natural Science Foundation of China [51709074]
- Fundamental Research Funds for the Central Universities of China [2018B10414]
- National Key Research and Development Program of China [2016YFC0402706]
- Special Fund of the State Key Laboratory of Hydrology-Water Resources [20145027312]
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We used the Decision Support System for Agro-technology Transfer-Cropping System Model (DSSAT) and data assimilation scheme (DSSAT-DA) to estimate maize (i.e., corn) yield and to evaluate the sensitivity of maize yield to hydroclimatic variables (i.e., precipitation, air temperatures, solar radiation, soil water). The remotely sensed soil moisture products, which includes Advanced Microwave Scanning Radiometer and the Soil Moisture and Ocean Salinity, were assimilated to DSSAT model by using the Ensemble Kalman Filtering approach. It was observed that both DSSAT and DSSAT-DA models can able to capture the annual trend of maize yield, although they overestimate the observed maize yield. The DSSAT-DA scheme assimilated with remotely sensed products slightly improves the model performance. The antecedent hydroclimatic information can influence the subsequent maize yield. The maize yield is sensitive to the soil water availability and precipitation amount, especially at the antecedent 1 month time to sowing and the subsequent second and third month's growing period.
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