Integration of remote sensing and field observations in evaluating DSSAT model for estimating maize and soybean growth and yield in Maryland, USA

Authors: Akumaga, Uvirkaa; Gao, Feng; Anderson, Martha; Dulaney, Wayne; HOUBORG, R. - Planet Labs Inc; Russ, Andrew - Andy; HIVELY, W.D - Us Geological Survey (USGS)

Submitted to: Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/16/2023
Publication Date: 6/1/2023
Citation: Akumaga, U., Gao, F.N., Anderson, M.C., Dulaney, W.P., Houborg, R., Russ, A.L., Hively, W. 2023. Integration of remote sensing and field observations in evaluating DSSAT model for estimating maize and soybean growth and yield in Maryland, USA. Agronomy. 13:1540. https://doi.org/10.3390/agronomy13061540.
DOI: https://doi.org/10.3390/agronomy13061540

Interpretive Summary: Crop models can be used to predict crop yield by simulating the growth and development of a crop. However, these models are limited by the availability and quality of input data. Remote sensing provides a means of constraining these uncertainties. This study integrates remote sensing and field observations into the Decision Support System for Agro-Technology (DSSAT) model to estimate soybean and maize growth and yield. Results based on the fields in the USDA-ARS Beltsville Agricultural Research Center (BARC) show that the calibrated model accurately simulated days to flowering and maturity and produced reasonable yield estimates for most fields and years. This study demonstrates that remotely sensed crop information can augment field observations for crop modeling, especially for the data-poor region where no field records are available for modeling purposes.

Technical Abstract: Crop models are very useful in evaluating crop growth and yield at the field and regional scales, but their applications and accuracies are restricted by input data availability and quality. To overcome difficulties inherent to crop modeling, input data can be enhanced by the incorporation of remotely sensed and field observations into crop growth models. This approach has been recognized to be an important way to monitor crop growth conditions and to predict yield at field and regional scale. In recent years, satellite remote sensing has provided high temporal and spatial resolution data that allow for generating continuous time series of biophysical parameters such as vegetation indices, leaf area index, and phenology. The objectives of this study were to integrate remote sensing and field observations into the Decision Support System for Agro-Technology (DSSAT) model to estimate soybean and maize growth and yield. The study used Planet Fusion (daily, 3m) derived phenology and LAI and field observations data from field experiments carried out at the USDA-ARS Beltsville Agricultural Research Center (BARC), Beltsville, Maryland. For maize, a total of 17 treatments were used (10 treatments for model calibration and seven treatments for validation), while for soybean (Maturity groups 3 and 4), a total of 18 treatments were used (nine for calibration and nine for validation). The calibrated model was tested against an independent, multi-locations and multi-year set of phenology and yield data (2017-2020). The model accurately simulated maize and soybean measured days to flowering and maturity and produced reasonable yield estimates for most fields and years. The test run model for independent multi-locations and years produced good results for phenology and yields for both maize and soybean as indicated by the index of agreement (d), ranging from 0.65 to 0.93 and the normalized root mean squared errors ranging from 1 to 20% except for soybean maturity group 4. Overall, the model performance with respect to phenology and grain yield for maize and soybean were good and consistent with other evaluation studies of the model. Integrating remote sensing and field observations with crop growth models provides an effective approach for assessing crop conditions and yield in data-poor regions.