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 yield modeling using remote sensing is an essential task in the south of the Russian Far East and makes it possible to plan sowing areas at the municipal level. This article presents a comparative assessment of the regression models’ accuracy, where the seasonal maxima of the LAI (Leaf Area Index) and NDVI (Normal Difference Vegetation Index), as well as the number of growing days (days with an average daily air temperature above 10°C), were considered as predictors. For four districts of the Amur Region and the Jewish Autonomous Region, MODIS (Moderate-resolution Imaging Spectroradiometer) data obtained from the arable land mask were used, using the Vega-Science web service, as well as soybean yield in 2010-2017. It was found that the maximum values of LAI and NDVI fall on weeks 31 to 33, which corresponds to the first half of August. In 2010-2017, the LAI-based models’ MAPE (Mean Absolute Percentage Error) was in the range 4.1 – 9.0%, and the RMSE (Root Mean Squared Error) was 0.06 to 0.13 t/ha. The corresponding errors of the regression model with NDVI were quite similar: MAPE 4.8 to 10.4%, RMSE 0.06 to 0.15 t/ha. This approach was evaluated with a ‘leave-one-year-out’ cross-validation procedure. There were no significant differences in the forecast error (APE) when using LAI and NDVI; at the same time, it was found that the quality of the regression model in the Tambovskiy and Oktyabrskiy districts is higher than in the Leninskiy and Mikhailovskiy districts. The median APE for Tambovskiy district was 7.2% for LAI and 8.8% for NDVI, for Oktyabrskiy the corresponding figures were 7.5% and 6.1%, for Leninskiy – 14.2% and 13.7%, and for Mikhailovskiy – 10.8% and 12.3%, respectively.
Crop yields are strictly dependent from natural and climatic conditions of the growing region, in addition specific weather conditions in the southern part of the Far East necessitates the analysis of a large number of factors when building a predictive regression model. The article presents regression models for assessing the average productivity of the main crops in Chernigovsky district of Primorsky region: soybean, spring wheat, barley and oat. Between 2012 and 2018 the sown area of these crops ranged from 78 to 86 % of the total sown area in the Chernigovsky district. We used the indicators obtained from Earth remote sensing data (the maximum weekly NDVI per year, calculated from the mask of arable land in the Chernigovsky district) and meteorological characteristics (from 2008 to 2018): hydrothermal Selyaninov coefficient, the duration of the growing season, temperature and humidity of the upper soil layer, photosynthetically active radiation and the Budyko radiation index. Climatic characteristics of arable land, representing reanalysis data and combining ground based and remote observations, were obtained using the Vega–Science web–service. Also, we used data about sown area and gross crop in the Chernigovsky region from 2008 to 2018. It was found that average annual oat yield has the biggest coefficient of variation (31.5 %). The corresponding indicator for the remaining crops is in range from 16 to 18 %. The accuracy analysis of the obtained models showed that the average error of the model in period from 2008 to 2017 was 4.1 % for barley, 5.1 % for oat and spring wheat, and 5.4 % for soybean.
One of the most important tasks in practical agricultural activity is the identification of agricultural crops, both those growing in individual fields at the moment and those that grew in these fields earlier. To reduce the complexity of the identification process in recent years, data from remote sensing of the Earth (remote sensing), including the values of vegetation indices calculated during the growing season, have been used. At the same time, processing optical satellite images and obtaining reliable index values is often difficult, which is due to cloud cover during the shooting. To solve this problem, the article suggests using the seasonal course curve of the radar vegetation index with double polarization (DpRVI) as the main indicator characterizing agricultural crops. In the period 2017-2020, 48 radar images of the Khabarovsk Municipal District of the Khabarovsk Territory from the Sentinel-1 satellite were received and processed to identify crops in the experimental fields of the Far Eastern Research Institute of Agriculture (FEARI) (resolution 22 m, shooting interval - 12 days). Soybeans and oats were the main identified crops. Pixels of fields not occupied by these crops (forage grasses, abandoned fields) were also added. The series of values of DpRVI were obtained both for individual pixels and fields, and approximated series for three classes. The approximation was carried out using the Gaussian function, the double logistic function, the square and cubic polynomials. It is established that the optimal approximation algorithm is the use of a double logistic function (the average error was 4.6%). On average, the approximation error of the vegetation index for soybeans did not exceed 5%, for perennial grasses – 8.5%, and for oats - 11%. For experimental fields with a total area of 303 hectares with a known crop rotation, the classification was carried out by the weighted method of k nearest neighbors (the training sample was formed according to the data of 2017-2019, the test sample -2020). As a result, 90% of the fields were correctly identified, and the overall pixel classification accuracy was 73%, which made it possible to identify the discrepancy between the actual boundaries of the fields declared to identify abandoned and swampy areas. Thus, it is established that the DpRVI index can be used to identify agricultural crops in the south of the Far East and serve as the basis for the automatic classification of arable land.
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