气候变化领域集成服务门户(CCPortal): Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data

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DOI	10.1016/j.rse.2020.111954
	Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data
	Mandal D.; Kumar V.; Ratha D.; Dey S.; Bhattacharya A.; Lopez-Sanchez J.M.; McNairn H.; Rao Y.S.
发表日期	2020
ISSN	00344257
卷号	247
英文摘要	Sentinel-1 Synthetic Aperture Radar (SAR) data have provided an unprecedented opportunity for crop monitoring due to its high revisit frequency and wide spatial coverage. The dual-pol (VV-VH) Sentinel-1 SAR data are being utilized for the European Common Agricultural Policy (CAP) as well as for other national projects, which are providing Sentinel derived information to support crop monitoring networks. Among the Earth observation products identified for agriculture monitoring, indicators of vegetation status are deemed critical by end-user communities. In literature, several experiments usually utilize the backscatter intensities to characterize crops. In this study, we have jointly utilized the scattering information in terms of the degree of polarization and the eigenvalue spectrum to derive a new vegetation index from dual-pol (DpRVI) SAR data. We assess the utility of this index as an indicator of plant growth dynamics for canola, soybean, and wheat, over a test site in Canada. A temporal analysis of DpRVI with crop biophysical variables (viz., Plant Area Index (PAI), Vegetation Water Content (VWC), and dry biomass (DB)) at different phenological stages confirms its trend with plant growth dynamics. For each crop type, the DpRVI is compared with the cross and co-pol ratio (σVH0/σVV0) and dual-pol Radar Vegetation Index (RVI = 4σVH0/(σVV0 + σVH0)), Polarimetric Radar Vegetation Index (PRVI), and the Dual Polarization SAR Vegetation Index (DPSVI). Statistical analysis with biophysical variables shows that the DpRVI outperformed the other four vegetation indices, yielding significant correlations for all three crops. Correlations between DpRVI and biophysical variables are highest for canola, with coefficients of determination (R2) of 0.79 (PAI), 0.82 (VWC), and 0.75 (DB). DpRVI had a moderate correlation (R2≳ 0.6) with the biophysical parameters of wheat and soybean. Good retrieval accuracies of crop biophysical parameters are also observed for all three crops. © 2020 Elsevier Inc.
英文关键词	Canola; Degree of polarization; DpRVI; PAI; RVI; Vegetation water content
语种	英语
scopus关键词	Agricultural robots; Backscattering; Biophysics; Crops; Eigenvalues and eigenfunctions; Forestry; Polarimeters; Polarization; Vegetation; Agriculture monitoring; Bio-physical variables; Biophysical parameters; Common agricultural policy; Degree of polarization; Earth observation products; Scattering information; Vegetation water contents (VWC); Synthetic aperture radar; accuracy assessment; backscatter; biomass; biomonitoring; canola; Common Agricultural Policy; correlation; eigenvalue; growth; phenology; radar altimetry; satellite data; scattering; Sentinel; soybean; synthetic aperture radar; temporal analysis; vegetation index; water content; wheat; Canada; Brassica napus var. napus; Glycine max; Triticum aestivum
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179231
作者单位	Microwave Remote Sensing Lab, Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India; Department of Water Resources, Delft University of Technology, Delft, Netherlands; Institute for Computer Research, University of Alicante, Alicante, Spain; Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, Canada
推荐引用方式 GB/T 7714	Mandal D.,Kumar V.,Ratha D.,et al. Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data[J],2020,247.
APA	Mandal D..,Kumar V..,Ratha D..,Dey S..,Bhattacharya A..,...&Rao Y.S..(2020).Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data.Remote Sensing of Environment,247.
MLA	Mandal D.,et al."Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data".Remote Sensing of Environment 247(2020).