气候变化领域集成服务门户(CCPortal): Cropland mapping with L-band UAVSAR and development of NISAR products

CCPortal

DOI	10.1016/j.rse.2020.112180
	Cropland mapping with L-band UAVSAR and development of NISAR products
	Huang X.; Reba M.; Coffin A.; Runkle B.R.K.; Huang Y.; Chapman B.; Ziniti B.; Skakun S.; Kraatz S.; Siqueira P.; Torbick N.
发表日期	2021
ISSN	00344257
卷号	253
英文摘要	Planned satellite launches will provide open access and operational L-band radar data streams at space-time resolutions not previously available. To further prepare, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) platform was used to observe cropland sites across the southern United States to support the development of L-band (24 cm) prototype science products. Time series flights flew over four independent areas during the growing season of 2019 while crop ground measurements were collected. Major crops include corn, cotton, pasture, peanut, rice, and soybean. A suite of cropland classification experiments applied a set of machine learning (ML) algorithms (random forest, feedforward fully connected neural network, support vector machine), the recently developed Multi-temporal Binary Tree Classification (MBTC), and a phenology (Coefficient of Variation; CoV) approach to synergistically assess performance, scattering mechanisms, and limitations. Specific objectives of this research application included 1.) evaluation of L-band mapping performance across multiple independent agricultural production areas with field scale training data, and 2.) assessment of the CoV approach for the generation of prototype NISAR Level 2 science products. Collectively, SAR terms with sensitivity to volume scattering performed well and consistently across CoV mapping experiments achieving accuracy greater than 80% for cropland vs not cropland. Dynamic phenology classes, such as herbaceous wetlands, had some confusion with CoV agriculture requiring further regionalized training optimization. Volume scattering and cross-pol terms were most useful across the different ML techniques with overall accuracy and Kappa consistently over 90% and 0.85, respectively, for crop type by late growth stages for L-band observations. As expected, time series information was more valuable compared to any single ML technique, site, or crop schema. Ultimately, as more SAR platforms launch, the user community should leverage physical contributions of different wavelengths and polarizations along with growing open access time series for efficient and meaningful agricultural products. © 2020 Elsevier Inc.
英文关键词	Crop type and area mapping; Machine learning; NISAR; UAVSAR
语种	英语
scopus关键词	Agricultural products; Agricultural robots; Antennas; Binary alloys; Binary trees; Biology; Cobalt alloys; Crops; Data streams; Decision trees; Mapping; Support vector machines; Synthetic aperture radar; Time series; Agricultural productions; Coefficient of variation; Fully connected neural network; Research applications; Scattering mechanisms; Time series informations; Training optimizations; Uninhabited aerial vehicle; Space-based radar; accuracy assessment; agricultural land; agricultural production; cotton; growing season; legume; machine learning; maize; mapping method; pasture; radar; rice; satellite altimetry; scattering; soybean; synthetic aperture radar; unmanned vehicle; United States; Arachis hypogaea; Glycine max; Gossypium hirsutum
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/179030
作者单位	Applied Geosolutions, Durham, NH, United States; USDA Agricultural Research Service, Jonesboro, AR, United States; USDA Agricultural Research Service, Tifton, GA, United States; Department of Biological & Agricultural Engineering, University of Arkansas, Fayetteville, AR, United States; USDA Agricultural Research Service, Stoneville, MS, United States; University of Maryland, College Park, MD, United States; NASA Jet Propulsion Laboratory, Pasadena, CA, United States; University of Massachusetts, Amherst, MA, United States
推荐引用方式 GB/T 7714	Huang X.,Reba M.,Coffin A.,et al. Cropland mapping with L-band UAVSAR and development of NISAR products[J],2021,253.
APA	Huang X..,Reba M..,Coffin A..,Runkle B.R.K..,Huang Y..,...&Torbick N..(2021).Cropland mapping with L-band UAVSAR and development of NISAR products.Remote Sensing of Environment,253.
MLA	Huang X.,et al."Cropland mapping with L-band UAVSAR and development of NISAR products".Remote Sensing of Environment 253(2021).