Estimating soybean yield using time series of anomalies in vegetation indices from MODIS

MIGUEL NOLASCO; GUSTAVO OVANDO; SILVINA SAYAGO; IVANA MAGARIO; MÓNICA BOCCO

Buenos Aires

Congreso; CAI 2021 CONGRESO ARGENTINO DE AGROINFORMATICA (JAIIO); 2021

Sadio

An accurate estimation of soybean yield while the plants are still inthe field is highly necessary for industry applications and decision-making policies related to planning. Remote sensing is a powerful tool, due to its spatiotemporal coverage, for developing empirical models to predict and evaluatecrop yields at regional and national scales.In Argentina, soybean (Glycine max (L.) Merr.) is the most important crop, particularly in Córdoba province the 89% of the sown area and 88% of the production is concentrated in eleven departments. The objectives of this work were toevaluate the performance of three vegetation indices: Normalized DifferenceVegetation Index (NDVI), Enhanced Vegetation Index (EVI) and NormalizedDifferential Water Index (NDWI), from Moderate Resolution Imaging Spectroradiometer (MODIS), to explain the anomalies in the soybean yield at department-level in Córdoba, and to develop regressions models for estimate this variable using anomalies of these indices and average crop yield, considering timeseries of historical records and different sources of data.The results showed that the anomalies of the three vegetation indices fit, withvery good precision, the anomalies of soybean yield (Pearson correlation coefficient values from 0.71 to 0.85). The evolution of the NDVI anomalies of midseason crop development stage, for all periods considered, showed a similarpattern to yield anomalies, particularly differentiating years where droughts orhighest soybean yields occurred, independently of data sources used. The regression models estimated soybean yield with NDVI anomalies, obtained priorto harvest, with % RMSE values between 8% and 17%.These simple and versatile models show that using free MODIS data, we canproduce reasonable real-time estimates of soybean yield at department-levelwithout previous crop classification.