基于时序定量遥感的冬小麦长势监测与估产研究

Remote sensing is an important approach for crop growth monitoring efficiently and subjectively, and is helpful for the agricultural productivity. In this paper, Longkang Farm in Anhui province, China, was selected as a case for the study. Remote sensing images with middle-high spatial resolution from different satellite-based sensors were collected and quantitively processed. Statistics models for the estimation of chlorophyll density and leaf area index were built based on vegetation indices. Time-series products of vegetation parameters were produced. We analyzed the temporal patterns of chlorophyll density and leaf area index and found that the high-yield wheat grew much better than the low-yield wheat during the winter. In addition, we built a yield prediction model based on the Normalized-Difference Vegetation Index (NDVI) for winter wheat. The results showed that, using accumulated NDVI at heading and milk stage, the yield can be accurately estimated. The winter wheat yield prediction map of Longkang farm was produced based on time-series satellite images. This study provided an efficient approach for crop growth monitoring.

Keywords： Time-series quantitive remote sensing ; Growth monitoring ; Yield prediction ; Vegetation indices