Integration of Remote Sensing and Field Observations in Evaluating DSSAT Model for Estimating Maize and Soybean Growth and Yield in Maryland, USA

Crop models are useful for evaluating crop growth and yield at the field regional scales, but their applications accuracies restricted by input data availability quality. To overcome difficulties inherent to modeling, can be enhanced incorporation of remotely sensed observations into models. This approach has been recognized an important way monitor conditions predict scale. In recent years, satellite remote sensing provided high-temporal high-spatial-resolution that allow generating continuous time series biophysical parameters such as vegetation indices, leaf area index, phenology. The objectives this study were use along with inputs Decision Support System Agro-Technology (DSSAT) model estimate soybean maize yield. used phenology index (LAI) derived from Planet Fusion (daily, 3 m) imagery observation on stage, LAI collected United State Department Agriculture, Agricultural Research Service, Beltsville Center (BARC), Beltsville, Maryland. For maize, a total 17 treatments (site years) (ten calibration seven validation), while (maturity groups three four), 18 (nine nine validation). calibrated was tested against independent, multi-location multi-year set (2017–2020) BARC fields. accurately simulated days flowering maturity produced reasonable estimates most fields years. Model run independent locations years good results yields both soybean, indicated agreement (d) values ranging 0.65 0.93 normalized root-mean-squared error 1 20%, except group four. Overall, performances respect grain consistent other DSSAT evaluation studies. inclusion in crop-growth provide effective assessing conditions, even regions lacking ground data.