文献类型： 外文期刊

作者： Hu, Qiong ¹ ; Yang, Jingya ² ; Xu, Baodong ² ; Huang, Jianxi ⁴ ; Memon, Muhammad Sohail ⁶ ; Yin, Gaofei ⁸ ; Zeng, Yelu ¹⁰ ;

作者机构： 1.Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Sch Urban & Environm Sci, Wuhan 430079, Peoples R China

2.Huazhong Agr Univ, Macro Agr Res Inst, Coll Resources & Environm, Wuhan 430070, Peoples R China

3.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

4.China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China

5.Minist Agr & Rural Affairs, Key Lab Remote Sensing Agri Hazards, Beijing 100083, Peoples R China

6.Sindh Agr Univ, Fac Agr Engn, Tandojam 70060, Pakistan

7.Nanjing Agr Univ, Coll Engn, Nanjing 210031, Peoples R China

8.Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Chengdu 610031, Peoples R China

9.CREAF, Cerdanyola Del Valles 08193, Catalonia, Spain

10.Carnegie Inst Sci, Dept Global Ecol, Stanford, CA 94305 USA

11.Sichuan Acad Agr Sci, Inst Remote Sensing Applicat, Chengdu 610066, Peoples R China

关键词： leaf area index (LAI); fraction of absorbed photosynthetically active radiation (FAPAR); fractional vegetation cover (FVC); Sentinel-2; Evaluation; Uncertainty

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN：

年卷期： 2020 年 12 卷 6 期

页码：

收录情况： SCI

摘要： Global biophysical products at decametric resolution derived from Sentinel-2 imagery have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring. Evaluating uncertainties of different Sentinel-2 biophysical products over various regions and vegetation types is pivotal in the application of land surface models. In this study, we quantified the performance of Sentinel-2-derived Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and Fractional Vegetation Cover (FVC) estimates using global ground observations with consistent measurement criteria. Our results show that the accuracy of vegetation and non-vegetated classification based on Sentinel-2 surface reflectance products is greater than 95%, which indicates the vegetation identification is favorable for the practical application of biophysical estimates, as several LAI, FAPAR, and FVC retrievals were derived for non-vegetated pixels. The rate of best retrievals is similar between LAI and FAPAR estimates, both accounting for 87% of all vegetation pixels, while it is almost 100% for FVC estimates. Additionally, the Sentinel-2 FAPAR and FVC estimates agree well with ground-measurements-derived (GMD) reference maps, whereas a large discrepancy is observed for Sentinel-2 LAI estimates by comparing with both GMD effective LAI (LAI(e)) and actual LAI (LAI) reference maps. Furthermore, the uncertainties of Sentinel-2 LAI, FAPAR and FVC estimates are 1.09 m(2)/m(2), 1.14 m(2)/m(2), 0.13 and 0.17 through comparisons to ground LAI(e), LAI, FAPAR, and FVC measurements, respectively. Given the temporal difference between Sentinel-2 observations and ground measurements, Sentinel-2 LAI estimates are more consistent with LAI(e) than LAI values. The robustness of evaluation results can be further improved as long as more multi-temporal ground measurements across different regions are obtained. Overall, this study provides fundamental information about the performance of Sentinel-2 LAI, FAPAR, and FVC estimates, which imbues our confidence in the broad applications of these decametric products.