Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index

Cong Zhou; Yan Gong; Shenghui Fang; Kaili Yang; Yi Peng; Xianting Wu; Renshan Zhu

doi:10.3389/fpls.2022.957870

Combining spectral and wavelet texture features for unmanned aerial vehicles remote estimation of rice leaf area index

Front Plant Sci. 2022 Aug 4:13:957870. doi: 10.3389/fpls.2022.957870. eCollection 2022.

Authors

Cong Zhou¹, Yan Gong^{1

2}, Shenghui Fang^{1

2}, Kaili Yang¹, Yi Peng^{1

2}, Xianting Wu^{2

3}, Renshan Zhu^{2

3}

Affiliations

¹ School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China.
² Lab for Remote Sensing of Crop Phenotyping, Wuhan University, Wuhan, China.
³ College of Life Sciences, Wuhan University, Wuhan, China.

Abstract

Estimating the crop leaf area index (LAI) accurately is very critical in agricultural remote sensing, especially in monitoring crop growth and yield prediction. The development of unmanned aerial vehicles (UAVs) has been significant in recent years and has been extensively applied in agricultural remote sensing (RS). The vegetation index (VI), which reflects spectral information, is a commonly used RS method for estimating LAI. Texture features can reflect the differences in the canopy structure of rice at different growth stages. In this research, a method was developed to improve the accuracy of rice LAI estimation during the whole growing season by combining texture information based on wavelet transform and spectral information derived from the VI. During the whole growth period, we obtained UAV images of two study areas using a 12-band Mini-MCA system and performed corresponding ground measurements. Several VI values were calculated, and the texture analysis was carried out. New indices were constructed by mathematically combining the wavelet texture and spectral information. Compared with the corresponding VIs, the new indices reduced the saturation effect and were less sensitive to the emergence of panicles. The determination coefficient (R²) increased for most VIs used in this study throughout the whole growth period. The results indicated that the estimation accuracy of LAI by combining spectral information and texture information was higher than that of VIs. The method proposed in this study used the spectral and wavelet texture features extracted from UAV images to establish a model of the whole growth period of rice, which was easy to operate and had great potential for large-scale auxiliary rice breeding and field management research.

Keywords: leaf area index (LAI); remote sensing (RS); rice; texture; unmanned aerial vehicle (UAV); vegetation index (VI); wavelet.