Journal Article

Comparison of UAV RGB Imagery and Hyperspectral Remote-Sensing Data for Monitoring Winter Wheat Growth

Li, Zhenhai, Zhao, Chunjiang, Yang, Guijun, Feng, Haikuan, Tao, Huilin

2022

Overview

Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unmanned aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into the new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results are as follows: (1) The RGB-imagery indices red reflectance (r), the excess-red index (EXR), the vegetation atmospherically resistant index (VARI), and the modified green-red vegetation index (MGRVI), as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index (MSR), the simple ratio vegetation index (SR), and the normalized difference vegetation index (NDVI), are more strongly correlated with the CGI than a single growth-monitoring indicator. (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stages in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.

• Publisher: MDPI AG
• Subject: aboveground biomass / Accuracy / Agricultural management / Biomass / Cameras / Cereal crops / Chlorophyll / comprehensive growth index / Corn / Crop growth / Crops / Image acquisition / Image resolution / Indicators / Leaf area / Leaf area index / Leaves / Moisture content / Monitoring / multiple linear regression / Neural networks / Nitrogen / normalized difference vegetation index / Normalized difference vegetative index / partial least squares / Physiology / plant height / plant nitrogen content / random forest / reflectance / Regression analysis / Remote sensing / Support vector machines / Unmanned aerial vehicles / Vegetation / vegetation indices / Water content / Wheat / Winter wheat