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Phenology Effects on Physically Based Estimation of Paddy Rice Canopy Traits from UAV Hyperspectral Imagery

文献类型： 外文期刊

作者： Wang, Li 1 ; Chen, Shuisen 1 ; Peng, Zhiping 2 ; Huang, Jichuan 2 ; Wang, Chongyang 1 ; Jiang, Hao 1 ; Zheng, Qiong 1 ; L 1 ;

作者机构： 1.Guangdong Acad Sci, Res Ctr Guangdong Prov Engn Technol Applicat Remo, Key Lab Guangdong Utilizat Remote Sensing & Geog, Guangzhou Inst Geog,Guangdong Open Lab Geospatial, Guangzhou 510070, Peoples R China

2.Guangdong Acad Agr Sci, Inst Agr Resources & Environm, Guangzhou 510640, Peoples R China

关键词： paddy rice; growth stages; phenology; soil background; radiative transfer models; PROSAIL; lookup tables; hyperspectral

期刊名称：REMOTE SENSING （ 影响因子：4.509； 五年影响因子：5.001 ）

ISSN：

年卷期： 2021 年 13 卷 9 期

页码：

收录情况： SCI

摘要： Radiation transform models such as PROSAIL are widely used for crop canopy reflectance simulation and biophysical parameter inversion. The PROSAIL model basically assumes that the canopy is turbid homogenous media with a bare soil background. However, the canopy structure changes when crop growth stages develop, which is more or less a departure from this assumption. In addition, a paddy rice field is inundated most of the time with flooded soil background. In this study, field-scale paddy rice leaf area index (LAI), leaf cholorphyll content (LCC), and canopy chlorophyll content (CCC) were retrieved from unmanned-aerial-vehicle-based hyperspectral images by the PROSAIL radiation transform model using a lookup table (LUT) strategy, with a special focus on the effects of growth-stage development and soil-background signature selection. Results show that involving flooded soil reflectance as background reflectance for PROSAIL could improve estimation accuracy. When using a LUT with the flooded soil reflectance signature (LUTflooded) the coefficients of determination (R-2) between observed and estimation variables are 0.70, 0.11, and 0.79 for LAI, LCC, and CCC, respectively, for the entire growing season (from tillering to heading growth stages), and the corresponding mean absolute errors (MAEs) are 21.87%, 16.27%, and 12.52%. For LAI and LCC, high model bias mainly occurred in tillering growth stages. There is an obvious overestimation of LAI and underestimation of LCC for in the tillering growth stage. The estimation accuracy of CCC is relatively consistent from tillering to heading growth stages.

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