Comparison of multi-source remote sensing data for integrated monitoring winter wheat growth

Abstract-Accurate and timely regional crop growth monitoring greatly benefits for the establishment and adjustment of agricultural planning and policies. In this study, above ground fresh biomass (AFB), leaf area index (LAI), soil and plant analyzer development(SPAD)?plant height (PH) and plant water content (PWC) were selected as the key indicators of winter wheat growth and used to integrated monitor winter wheat growth based on the entropy weight method. Sentinel-1A?Sentinel-2A and PlanetScope were used as the data source to analyze the correlation between different remote sensing index and growth integrated monitoring indicator (GIMI), and then to screen appropriate index as the input variables of the model. Based on different remote sensing index of multi-source remote sensing data, the monitoring models of winter wheat growth were constructed and evaluated. The optimal data source was used to monitor the distribution characteristics of winter wheat growth in the study area. The results showed that the Sentinel-2A data source has strongergrowth monitoring ability than the other two data sources, and the model produced the highest R -squared value (0.649) and the lower RMSE value (0.104). The overall growth of winter wheat in the study area was stable and concentrated, showing the spatial distribution characteristics of "the middle bad and the northsouth well". The research results can provide scientific basis for field management of winter wheat and serve the strategic needs of developing intelligent agriculture and building an agricultural power in China.