Robust Multisensor Prediction of Drought-Induced Yield Anomalies of Soybeans in Argentina

MAAS, MARTIN D.; SALVIA, MERCEDES; SPENNEMANN, PABLO C.; FERNANDEZ-LONG, MARIA ELENA

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Lugar: New York; Año: 2022 vol. 19 p. 2504804 - 2504804

1545-598X

A multi-sensor method for the prediction of drought induced agricultural impact is put forth in this paper. The input data considered includes MODIS NDVI and LST, ESA-CCI Soil Moisture, and CHIRPS rain data, which is processed at the department level in a large and sparsely monitored cropland in Argentina. As ground truth, we have employed departmentscale crop losses estimated by an annual agricultural census. In particular, the period under consideration (2001-2019) includes five severe drought events where soybean production in the area was considerably affected. The proposed method is based on Lasso-regression of corresponding rank values of the satellite data to the relative yield anomalies. Importantly, the proposed methodology is robust to extreme drought events. In addition, an associated early warning classification method results in an overall accuracy no worse than 70% up to one month before the harvest, and 62% two months before the harvest. The proposed methodology offers a valuable method for the prediction of agricultural drought impact, and should be especially valuable in sparsely monitored regions of the world.