Remote sensing monitoring of rice growth under Cnaphalocrocis medinalis (Guenée) damage by integrating satellite and UAV remote sensing data

Abstract

Satellite remote sensing is commonly used for large-scale agricultural monitoring, but the low spatial resolution of its imagery does not allow it to present details of crop growth. Combining satellite remote sensing and unmanned aerial vehicle (UAV) remote sensing to complement each other’s advantages may be a way to realize large-scale and precision agricultural monitoring. Therefore, to explore the effect of the fusion method of UAV remote sensing data and satellite remote sensing data on improving satellite monitoring of the growth of damaged rice and to achieve large-scale accurate monitoring of the growth characteristics of damaged rice, in this study, the research object was rice infested by Cnaphalocrocis medinalis Guenee. We selected SuperDove satellite imagery on 14 August 2022 and UAV multispectral imagery on 15 August 2022 for vegetation index calculation and fused the two by a scale transformation method. We constructed LAI monitoring models using multiple linear regression (MLR), back-propagation neural network (BPNN) and support vector machine regression (SVR) to evaluate the effect of satellite data on LAI inversion for affected rice before and after fusion. The results showed that (1) the learning ability of the machine learning model represented by BPNN and SVR was better than that of the traditional regression model represented by MLR. The SVR model had the best inversion effect on the LAI. (2) The satellite data after fusion with UAV data significantly improved the inversion accuracy of the LAI of damaged rice. Compared with the original satellite data, with the combined data, R2 increased by approximately 0.3, RMSE and MAE decreased by approximately 0.1. Moreover, the spatial details of remote sensing images were clearer, and the spatial matching degree with the UAV image inversion results was higher. This study can provide theoretical and technical support for large-scale and high-precision monitoring of affected rice.