A Spectral Agrometeorological Model for Estimating Soybean Grain Productivity in Mato Grosso, Brazil

Abstract

This study used spectral data integrated with the agrometeorological model by Doorenbos and Kassam to estimate soybean grain productivity in the state of Mato Grosso, Brazil. In the developed model, spectral data were used instead of meteorological data and biophysical parameters of the crop. For this purpose, the products of real and potential evapotranspiration (MOD16), normalized difference vegetation index -NDVI (MOD13Q1), and leaf area index (MOD15A2H) from the MODIS satellite were used, in addition to sunstroke data obtained by using the visible channel from the satellite GOES IMAGER. The results obtained showed that, with the proposed methodology, it was possible to follow the development of soybean cultivation throughout the cycle and to estimate production and productivity in the study area. Willmott's agreement index was 0.99 and 0.96 and Pearson's correlation coefficient was 0.99 and 0.84 for production and productivity, respectively.

Soybean Yield Prediction Using Remote Sensing in Southwestern Piauí State, Brazil

“…In addition, they found decrease in NDVI values after the beginning of physiological maturation (R6), which was not found in the present study due to the indeterminate growth habit of the Bonus cultivar. Sarmiento et al (2020) evaluated the capacity of a spectral agrometeorological model to predict soybean grain yield in the State of Mato Grosso, Brazil, and found variations in NDVI over the phenological stages, with higher variations in vegetative stages (V1 and R5), maximum NDVI in the stage R5 (0.89), and decreases at the beginning of the maturation stage (R8). They also found that NDVI analysis can identify and monitor phenological stages and whether the crop development is within the expected biomass production.…”

“…Trindade et al (2019) found best soybean grain yield prediction models using NDVI values of the stage R2. The use of NDVI images of reproduction stages of soybean crops is more capable of predicting the crop production potential, since there is a high correlation between soybean grain yield and the proper nutrient and water supply up to this stage (SARMIENTO et al, 2020).…”

“…The use of vegetation indexes assists in studies about spectral and temporal characteristics of the soybean crop during its phenological cycle, and factors that affect its growth, development, and yield, such as climate, relief, and soil physical and chemical attributes (TRINDADE et al, 2019). The Normalized Difference Vegetation Index (NDVI) is among the most used indexes for monitoring vegetation, which has been widely used in scientific researches (MERCANTE et al, 2010;GUSSO et al, 2013;TRINDADE et al, 2019;SARMIENTO et al, 2020).…”

“…Vegetation indexes enables field identifications of nutrient deficiencies (TRINDADE et al, 2019) and water stress (SCHIRMBECK et al, 2019); diagnoses of several biophysical parameters, including leaf area index (BERGER et al, 2019), soil cover percentage (MACEDO et al, 2020), biomass (RODIGHERI et al, 2020, and photosynthetic activity (RODRIGUES et al, 2013); and predictions of grain yield (MELO et al, 2008;BERTOLIN et al, 2017;TRINDADE et al, 2019).…”

“…Studies on prediction models for crop yields using vegetation indexes have been conducted since the decade of 1990, with proposition of models based on only one image, which showed the highest correlation between biomass and yield. Recently, researches have focused on the monitoring during the whole crop cycle (MELO et al, 2008;MERCANTE et al, 2010;GUSSO et al, 2013;SARMIENTO et al, 2020), searching for more accurate information to propose models that predict the soybean yield. Thus, the objective of this work was to evaluate the fit of multiple linear regression models for predicting soybean grain yield based on NDVI in a reference Farm in the Cerrado biome in the southwestern Piauí State, Brazil.…”