气候变化领域集成服务门户(CCPortal): Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery

CCPortal

DOI	10.1016/j.jag.2019.04.019
	Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery
	Xie Q.; Dash J.; Huete A.; Jiang A.; Yin G.; Ding Y.; Peng D.; Hall C.C.; Brown L.; Shi Y.; Ye H.; Dong Y.; Huang W.
发表日期	2019
ISSN	15698432
起始页码	187
结束页码	195
卷号	80
英文摘要	The red-edge bands place the recently available multispectral Sentinel-2 imagery at an advantage over other multispectral sensors, and hypothetically offer improved crop biophysical variable retrieval accuracy. In this study, Sentinel-2 data was tested for its ability to estimate winter wheat leaf area index (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). Artificial neural network (ANN) and look-up table (LUT) (based on PROSAIL simulations) and vegetation index (VI) methods were applied to retrieve biophysical parameters, and compared with the biophysical processor module embedded in the Sentinel Application Platform (SNAP) software. Based on a set of in situ measurements (62 samples) and near-synchronous Sentinel-2 images, the inversion approaches were applied and validated. The results showed that: 1) Sentinel-2 red-edge bands improved the retrievals of chlorophyll / LAI compared to traditional VIs; 2) the red-edge VIs outperformed other approaches; and 3) the SNAP biophysical processor obtained comparable accuracies of LAI and CCC estimation compared to the ANN and LUT approaches, giving R2 values above 0.5 with relatively low RMSE (1.53 m2/m2 for LAI, and 148.58 μg/cm2 for CCC). We recommend VI retrieval approach for small region with ground measurements, whereas where ground data is not available, SNAP is applicable for versatile and rapid winter wheat parameter estimation (though results need to be evaluated alongside the provided quality indicators). Summarizing, the results demonstrate the suitability of Sentinel-2 data, especially its red-edge bands, for crop biophysical variables retrieval. Future studies will need to make comparisons across canopy types to better assess the capability of the SNAP biophysical processor. © 2019 Elsevier B.V.
英文关键词	Artificial neural network; Chlorophyll content; Leaf area index; Look-up table; Vegetation index
语种	英语
scopus关键词	artificial neural network; chlorophyll; leaf area index; remote sensing; satellite imagery; Sentinel; software; vegetation index; wheat
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156453
作者单位	University of Technology Sydney, Faculty of Science, Sydney, NSW 2007, Australia; University of Southampton, School of Geography and Environmental Science, Highfield, Southampton, SO171BJ, United Kingdom; Shandong Normal University, College of Geography and Environment, Jinan, Shandong 250358, China; Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China; School of Geographical Sciences, Northeast Normal University, Changchun, 130024, China; Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China
推荐引用方式 GB/T 7714	Xie Q.,Dash J.,Huete A.,et al. Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery[J],2019,80.
APA	Xie Q..,Dash J..,Huete A..,Jiang A..,Yin G..,...&Huang W..(2019).Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery.International Journal of Applied Earth Observation and Geoinformation,80.
MLA	Xie Q.,et al."Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery".International Journal of Applied Earth Observation and Geoinformation 80(2019).