Predicting Soybean Yield at the Regional Scale Using Remote Sensing and Climatic Data

Abstract

Crop yield modeling at the regional level is one of the most important methods to ensure the profitability of the agro-industrial economy and the solving of the food security problem. Due to a lack of information about crop distribution over large agricultural areas, as well as the crop separation problem (based on remote sensing data) caused by the similarity of phenological cycles, a question arises regarding the relevance of using data obtained from the arable land mask of the region to predict the yield of individual crops. This study aimed to develop a regression model for soybean crop yield monitoring in municipalities and was conducted in the Khabarovsk Territory, located in the Russian Far East. Moderate Resolution Imaging Spectroradiometer (MODIS) data, an arable land mask, the meteorological characteristics obtained using the VEGA-Science web service, and crop yield data for 2010–2019 were used. The structure of crop distribution in the Khabarovsk District was reproduced in experimental fields, and Normalized Difference Vegetation Index (NDVI) seasonal variation approximating functions were constructed (both for total district sown area and different crops). It was found that the approximating function graph for the experimental fields corresponds to a similar graph for arable land. The maximum NDVI forecast error on the 30th week in 2019 using the approximation parameters according to 2014–2018 did not exceed 0.5%. The root-mean-square error (RMSE) was 0.054. The maximum value of the NDVI, as well as the indicators characterizing the temperature regime, soil moisture, and photosynthetically active radiation in the region during the period from the 1st to the 30th calendar weeks of the year, were previously considered as parameters of the regression model for predicting soybean yield. As a result of the experiments, the NDVI and the duration of the growing season were included in the regression model as independent variables. According to 2010–2018, the mean absolute percentage error (MAPE) of the regression model was 6.2%, and the soybean yield prediction absolute percentage error (APE) for 2019 was 6.3%, while RMSE was 0.13 t/ha. This approach was evaluated with a leave-one-year-out cross-validation procedure. When the calculated maximum NDVI value was used in the regression equation for early forecasting, MAPE in the 28th–30th weeks was less than 10%.

Soybean Crop Yield Prediction by Integration of Remote Sensing and Weather Observations

“…Multiple regression is a statistical method that uses multiple explanatory variables to predict the outcome (Stepanov et al, 2020). MLR models the linear relationship between explanatory variables and target variables.…”

Comparative analysis of orbital sensors in soybean yield estimation by the random forest algorithm

“…In this context, several agricultural studies have integrated data from orbital sensors and ML algorithms. Stepanov et al (2020) used data from the moderateresolution imaging spectro-radiometer (MODIS) sensor to monitor soybean crop yields in the Far East of Russia. In turn, Habibi et al (2021) analyzed the spatial variability of soybean plant density with images from the commercial PlanetScope sensor.…”

Application of LAI and NDVI to model soybean yield in the regions of the Russian Far East

“…Nowadays, soybean acreage in the Amur Region, Primorskiy Territory, Jewish Autonomous Region, and Khabarovsk Territory exceeds the area of all other crops combined [15]. In previous studies, soybean yield was estimated at the municipal level in the Far East based on remote sensing data, where the maximum NDVI, as well as various meteorological characteristics, were considered as independent predictors [16,17]. At the same time, models based on LAI have not previously been considered for assessing soybean yield in the Russian Far East.…”