气候变化领域集成服务门户(CCPortal): Evaluating NISAR's cropland mapping algorithm over the conterminous United States using Sentinel-1 data

CCPortal

DOI	10.1016/j.rse.2021.112472
	Evaluating NISAR's cropland mapping algorithm over the conterminous United States using Sentinel-1 data
	Rose S.; Kraatz S.; Kellndorfer J.; Cosh M.H.; Torbick N.; Huang X.; Siqueira P.
发表日期	2021
ISSN	00344257
卷号	260
英文摘要	Accurate knowledge of the distribution, breadth and change in agricultural activity is important to food security and the related trade and policy mechanisms. Routine observations afforded by spaceborne Synthetic Aperture Radar (SAR) allows for high-fidelity monitoring of agricultural parameters at the field scale. Here we evaluate the approach to be used for generating NASA's upcoming NASA ISRO SAR (NISAR) mission's L-band cropland product using Sentinel-1C-band data. This study uses all ascending Sentinel-1A/B data collected over the conterminous United States in 2017 to compute the coefficient of variation (CV) at 150 m × 150 m resolution and evaluates the overall accuracy (OA) of CV-based crop/non-crop classifications at 100 one-by-one degree tiles. We calculate accuracies using two approaches: (a) using a literature-recommended constant CV threshold of 0.5 (CVthr_0.5) and (b) determining optimal CV thresholds for every tile using Youden's J statistic (YJS), CVthr_YJS. These accuracy comparisons are important for determining (1) the viability of using a computationally inexpensive and straightforward approach for cropland classification over large spatial scales/diverse land covers (i.e., can accuracies ≥80% be routinely achieved?), (2) how closely OA0.5 compares to the performance ceiling (OAYJS). This information will help determine whether approach (a) is appropriate and how much potential room of improvement there could be in modifying it. Results for OA0.5 and OAYJS are 81.5% and 86.8%, respectively. A breakdown by census geographic region, showed that OA0.5 (OAYJS) exceeded 80% (90%) in the South and Midwest, but was only 76.1% (73.5%) in the West. The improvement in OAYJS mainly stems from tiles with >40% crop prevalence having about 10% greater OA values. To better examine the potential of the approach for land cover classification, results of approach (b) were also stratified by crop. Approach (b) accurately detected most non-crop classes as non-crop (>80%), but with low OAYJS values for grasslands/pasture, especially in the West. CV values for crop were distinct from non-crop indicating that the approach is suitable for crop/non-crop classifications. Because results CV values have substantial overlap within crop/non-crop classes, indicating the approach is poorly suited for land cover classifications. We also detected a strong geographic dependence of CVthr_YJS: values ranged from about 0.2 at the coasts and gradually increase to about 0.6 in the Central United States, most often falling close to 0.3 and 0.5. © 2021 Elsevier Inc.
英文关键词	Agriculture; Coefficient of variation; Crop classification; SAR; Sentinel-1; Time series
语种	英语
scopus关键词	Conformal mapping; Crops; Food supply; NASA; Radar imaging; Time series; Coefficient of variation values; Coefficients of variations; Crop class; Crop classification; Geographics; Land cover classification; Mapping algorithms; Overall accuracies; Sentinel-1; Times series; Synthetic aperture radar
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/178824
作者单位	Department of Electrical and Computer Engineering, University of Massachusetts, 113 Knowles Engineering Building, 151 Holdsworth Way, Amherst, MA 01003, United States; Earth Big Data LLC, Woods Hole, MA 02543, United States; USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, United States; Applied Geosolutions, Durham, NH 03824, United States
推荐引用方式 GB/T 7714	Rose S.,Kraatz S.,Kellndorfer J.,et al. Evaluating NISAR's cropland mapping algorithm over the conterminous United States using Sentinel-1 data[J],2021,260.
APA	Rose S..,Kraatz S..,Kellndorfer J..,Cosh M.H..,Torbick N..,...&Siqueira P..(2021).Evaluating NISAR's cropland mapping algorithm over the conterminous United States using Sentinel-1 data.Remote Sensing of Environment,260.
MLA	Rose S.,et al."Evaluating NISAR's cropland mapping algorithm over the conterminous United States using Sentinel-1 data".Remote Sensing of Environment 260(2021).