Research and Practice of Remote Sensing Aided Sampling Yield of Grain Crops Based on Counting Plants and Kernels

摘要

In China, the traditional method of determining actual grain yield is expensive, labor-intensive and inefficient. Using satellite imaging data, this work designed a remote-sensing-aided grain yield sampling and measuring method by studying the corn produced in Shunyi, Beijing, in 2015, based on counting plants and kernels. The aim of the research is to transform the traditional practice of field surveying, harvesting and weighing, into counting plants and kernels, culminating in building a regression model based on the relationship of the calculated sample yield and the corresponding normalized vegetation difference index (NDVI) for realizing the spatial distribution of the crop yield. The results show that the yield monitoring results based on counting plants and kernels and the validation error of NDVI regression model are all within a reasonable range. The scheme designed in this research is practically feasible, because it is simple and easy to operate; therefore, it is worthy of promotion and further work.