Crop Type and Yield Mapping Using Long-term Satellite Observations, Weather and Field Data
Abstract
Long-term spatially explicit information on crop production is essential for understanding food security in a changing climate. Land used to produce soybean is rapidly expanding in the agricultural frontiers of South America, replacing natural vegetation, pasture, and other cropland. In this research, we mapped annual soybean cover in South America between 2000 and 2020 by combining field data and satellite observations from Landsat, Sentinel-2 and MODIS. Validated against statistical field data, our soybean cover maps had an overall accuracy of >95%. We then developed a multi-temporal, multi-scale modeling workflow for mapping soybean yield in Brazil. Our method integrated the soybean cover maps, municipality-level crop yield statistics, Landsat and MODIS data, climate and weather records, and random forests regression. Compared to the reference data from official statistics, our annual soybean yield maps had an overall RMSE of 418 kg/ha and an r2 of 0.60. We show that the crop classification and yield models trained on historical data could be used to produce crop type and yield maps for future years, demonstrating the predictive capability of our approach for operational crop monitoring applications.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.B43B..01S