A CNN-LSTM-based method for crop yield estimation at the county level

Abstract

The present invention relates to the technical field of remote sensing image information extraction, in particular to a method for estimating crop production at the county level based on CNN-LSTM, which includes the following steps: acquisition and processing of S1 data, superposition and filtering of S2 data, S3 acquisition of county Scale feature tensor data, S4 build and train CNN-LSTM model, S5 apply the CNN-LSTM model trained in S4 to estimate the yield of target crops. The method of the present invention is based on the remote sensing data reflecting the growth state of crops and the environmental data affecting the growth of crops, and extracts more features of county-level crops through the method of histogram statistics and converts them into tensors, so as to train the extracted CNN-LSTM model, Effectively improve the accuracy of small-scale crop yield estimation.

Description

technical field [0001] The invention relates to the technical field of remote sensing image information extraction, in particular to a CNN-LSTM-based crop yield estimation method at the county level. Background technique [0002] Crop yield is the most important indicator of agriculture and has many connections with human society. Yield forecasting is one of the most challenging tasks in precision agriculture and has important implications for yield mapping, crop market planning, crop insurance, and harvest management. [0003] Remote sensing technology has been widely used in crop yield estimation. Various relevant information can be extracted from remote sensing data to assist production estimation. In particular, various vegetation indices (VI), such as the normalized difference vegetation index (NDVI), have been widely used. Other indices such as Green Leaf Area Index (GLAI), Crop Water Stress Index (CWSI), Normalized Difference Water Index (NDWI), Green Vegetation In...

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