文献类型： 外文期刊

作者： Li, Zhenhai ¹ ; Zhao, Yu ¹ ; Taylor, James ³ ; Gaulton, Rachel ⁴ ; Jin, Xiuliang ⁵ ; Song, Xiaoyu ¹ ; Li, Zhenhong ⁶ ; Meng, Yang ¹ ; Chen, Pengfei ⁸ ; Feng, Haikuan ¹ ; Wang, Chao ⁹ ; Guo, Wei ¹⁰ ; Xu, Xingang ¹ ; Chen, Liping ¹ ; Yang, Guijun ¹ ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

2.Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Peoples R China

3.Univ Montpellier, Inst Agro Montpellier, INRAE, UMR ITAP, F-34000 Montpellier, France

4.Newcastle Univ, Sch Nat & Environm Sci, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England

5.Chinese Acad Agr Sci, Inst Crop Sci, Minist Agr & Rural Affairs, Key Lab Crop Physiol & Ecol, Beijing 100081, Peoples R China

6.Changan Univ, Coll Geol Engn & Geomat, Xian 710054, Peoples R China

7.Newcastle Univ, Sch Engn, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England

8.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China

9.Shanxi Agr Univ, Agron Coll, Taigu 030801, Peoples R China

10.Henan Agr Univ, Coll Informat & Management Sci, Zhengzhou 450002, Peoples R China

关键词： Crop biomass algorithm; Phenological scale; Proximal reflectance; UAV hyperspectral; Winter wheat

期刊名称：REMOTE SENSING OF ENVIRONMENT （ 影响因子：13.85； 五年影响因子：13.662 ）

ISSN： 0034-4257

年卷期： 2022 年 273 卷

页码：

收录情况： SCI

摘要： Timely monitoring of above-ground biomass (AGB) is essential for indicating the crop growth status and pre-dicting grain yield and carbon dynamics. Non-destructive remote sensing techniques with a large spatial coverage have become a promising method for crop biomass monitoring. However, most existing crop biomass models have only been tested at a single growth stage or only at a small number of growth stages at a single location. This has limited the ability of these AGB models to be transferred spatially, to other fields or regions, to predict AGB at any growth stage during the season, or to be potentially used with data from other sensing systems. Here, a new crop biomass algorithm (CBA-Wheat) was developed to estimate AGB over the entire growing season. It uses information on the crop growth stage, based on phenological scale observations (Zadoks scale or ZS), the day of the year or thermal indices (growing degree days), to correct AGB estimations from remotely sensed vegetation indices. The model transferability was evaluated across multiple regional test sites and different data sources (UAV and hand-held spectroscopic data). Results showed that the coefficient values [slope (k) and intercept (b)] of ordinary least squares regression (OLSR) of AGB with vegetation indices had a strong relationship with ZS. These k and b relationships were used to correct the OLSR model parameters based on the observed phenological stage (ZS value). The two-band enhanced vegetation index (EVI2) was the best vegetation index for predicting AGB with the new CBA-WheatZS model, with R-2 and RMSE values of 0.83 and 2.07 t/ha for an experimental trial site, 0.78 and 2.05 t/ha for multiple independent regional test sites, and 0.69 and 1.87 t/ha when transferred to EVI2 derived from UAV. Model performance was lower with the day of the year and thermal index corrections; however, the use of relative growing degree-days (RGS; CBA-WheatRGS), instead of ZS information, to adjust the model parameters showed a high consistency with the CBA-WheatZS model, and a good potential for estimation of AGB at regional scales without the need for local phenological observations. The CBA-WheatRGS had validated R-2 and RMSE values of 0.82 and 2.01 t/ha for the experimental trial site, 0.76 and 2.39 t/ha for multiple independent regional test sites, and 0.66 and 2.14 t/ha for UAV hyperspectral imagery. These results demonstrated a good potential to estimate biomass from remotely sensed imagery at varying spatio-temporal scales in winter wheat.