Estimating Chlorophyll Content of Rice Based on UAV-Based Hyperspectral Imagery and Continuous Wavelet Transform

Chlorophyll is an essential pigment for photosynthesis of crops, which indicates the growth status of crops. Accurate and robust information on the spatial dynamics of chlorophyll content is of critical importance for crop growth status assessment and corresponding response activities. However, previous studies mainly focus on the methodologies based on in situ hyperspectral data, which are not applicative for regional chlorophyll content mapping. In this content, Unmanned Aerial Vehicle (UAV) based hyperspectral imagery, with high spatial and spectral resolution, may provide the spatial distribution of chlorophyll content accurately over crop fields. In this study, an empirical model between wavelet features, derived from UAV based hyperspectral imagery by continuous wavelet transform (CWT), and chlorophyll content (measured by a portable soil-plant analysis development meter) is proposed by support vector regression (SVR). The results suggest that the UAV based hyperspectral imagery combined with CWT demonstrates good performance in rice chlorophyll content estimation with R and RMSE of 0.81 and 3.51, respectively. Moreover, the wavelet coefficients corresponding to two bands at red (640nm, 628nm) and one band at green (548nm) are the most effective wavelet features to estimate chlorophyll content of rice.