文献类型： 外文期刊

作者： Li, Wenjuan ¹ ; Weiss, Marie ² ; Jay, Sylvain ² ; Wei, Shanshan ³ ; Zhao, Na ⁴ ; Comar, Alexis ⁵ ; Lopez-Lozano, Raul ² ; De Solan, Benoit ⁶ ; Yu, Qiangyi ¹ ; Wu, Wenbin ¹ ; Baret, Frederic ² ;

作者机构： 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China

2.Avignon Univ, UMR EMMAH 1114, INRAE, F-84000 Avignon, France

3.Natl Univ Singapore, Ctr Remote Imaging Sensing & Proc, Singapore 119076, Singapore

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

5.HIPHEN SAS, 120 Rue Jean Dausset,Site Agroparc, F-84140 Avignon, France

6.Inst vegetal, Arvalis, 228,route aerodrome,CS 40509, F-84914 Avignon 9, France

关键词： Green Area Index (GAI); Leaf chlorophyll content (LCC); Canopy chlorophyll content (CCC); Daily measurements; Wheat; Near-surface system

期刊名称：REMOTE SENSING OF ENVIRONMENT （ 影响因子：13.5； 五年影响因子：14.2 ）

ISSN： 0034-4257

年卷期： 2024 年 300 卷

页码：

收录情况： SCI

摘要： Green area index (GAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) are key variables that are closely related to crop growth. Concurrent and continuous monitoring of GAI, LCC and CCC is critical to keep consistency among variables and make decisions for field precision managements. Previous studies have developed several instruments and algorithms to monitor continuous GAI, while the autonomous monitoring of three variables simultaneously has been lacking. This study presents a novel algorithm to retrieve daily GAI, LCC and CCC from continuous directional observations acquired by a fixed and economic affordable multi-band spectrometer (6 bands covering red, red-edge and near infrared domains) and a photosynthetically active radiation (PAR) sensor in the field. It is composed of three main steps, corresponding to three crucial questions when retrieving variables under natural environments using multi-band spectrometer installed on a near-surface platform: diffuse fraction in each spectral band, radiometric calibration and diurnal sun variation of daily acquisitions. First, we estimated diffuse fraction in each spectral band from the relationship with PAR diffuse fraction based on simulations of the 6S atmospheric radiative transfer model. Second, we computed the relative value of each band to the reference of mean of measurements on all six bands from near-surface measurements, in place of absolute radiometric calibration to limit the influence of changing illumination conditions. In the third step, we combined PROSAIL canopy radiative transfer model and kernel-driven models to retrieved GAI, LCC and CCC from artificial neural network using above spectral diffuse fraction and diurnal multi-angle relative observations. The algorithm was evaluated over 43 IoTA (Internet of things for Agriculture) systems that were installed in 29 wheat fields in France from March to May 2019. Results showed that our method provides good estimates of GAI with root mean square error (RMSE) of 0.54, relative RMSE (RRMSE) of 26.95%, R2 of 0.86, LCC (RMSE = 12.06 mu g/cm2, RRMSE = 33.34%, R2 = 0.52) and CCC (RMSE = 0.23 g/m2, RRMSE = 24.58%, R2 = 0.93). This study shows great potentials for concurrent estimates of GAI, LCC and CCC from continuous ground measurements. It will be useful over other vegetations or other near-surface platforms for simultaneous estimations of biophysical variables.