Applying crops yield index in Heilongjiang Province of China

Real-time and accurate assessment of crop growth and yield is of great significance for grain macro-control measures, crop insurance, and agricultural production planning. However, it is lacking in the comparability for the crops in different growth stages at the same period during traditional monitoring crop growth using normalized difference vegetation index (NDVI). The high/low NDVI value cannot directly represent the high/low yield. In this study, a spatiotemporal comparable statistical index (crop yield index) was designed to dynamically represent the changes in crop growth and yield. The yield, meteorological, remote sensing and crop distribution data were first collected from the Heilongjiang Province of China. The yield estimation zoning was carried out for three crops (maize, rice, and soybeans) by spatial "K" luster analysis by tree edge removal (SKATER). The optimal variables of yield estimation were screened for each yield estimation zone using the random forest importance evaluation and Leave-One-Out. Secondly, the yield estimation models were built and validated for each yield estimation zoning. After that, the yield of three crops was estimated monthly during the growing season of 2022 (June-September), according to the yield estimation models. Taking the historical five years as the base period, the maximum indexation was used to index the predicted yield to obtain the crop yield index. Finally, the monthly analysis was implemented to determine the changes in the yield index that related to the historical reference period and the previous month during the growing season of 2022. The results show that: 1) The whole province was divided into 3, 3, and 4 yield estimation zones for rice, soybean, and maize, respectively. 2) The importance of modeling indicators was ranked in the descending order of the trend yield, remote sensing vegetation, and climate indicators. 3) The importance of each candidate modeling variable was varied in the different yield estimation zones. The different optimal modeling variables were also selected for each yield estimation zone. In the yield estimation models using the optimal modeling variables, the yield prediction accuracy of rice was the highest, with a mean absolute relative precision (MARP) of 95.20%, followed by maize (MARP=93.81%), and finally soybean (MARP=92.73%). 4) The yield indices of three crops from June to September were compared with the calculated using a historical five-year period. There were "flat" state differences in the yield indices of the three crops in most counties (i.e., the difference in the yield index was within 0.05). 5) The monthly yield index differences (the current month's yield index minus the previous month's yield index) of the three crops in most counties are between −0.01 and 0.01. The crop yield index shared excellent spatiotemporal comparability. On the temporal dimension, the crop yield index can be expected to compare the increase or decrease of a certain statistical unit (such as a county, city, or province) that is related to the historical average yield at a specific evaluation time point, or to compare the yield changes of two adjacent evaluation time points. On the spatial dimension, the crop yield index can be compared with the different statistical units at a specific evaluation time point. This crop yield index can provide promising application prospects in growth monitoring and yield forecasting.