气候变化领域集成服务门户(CCPortal): Mapping crops within the growing season across the United States

CCPortal

DOI	10.1016/j.rse.2020.112048
	Mapping crops within the growing season across the United States
	Konduri V.S.; Kumar J.; Hargrove W.W.; Hoffman F.M.; Ganguly A.R.
发表日期	2020
ISSN	00344257
卷号	251
英文摘要	Timely and accurate knowledge about the geospatial distribution of crops at regional to continental scales is crucial for forecasting crop production and estimating crop water use. The United States (US) is one of the leading food-producing countries, but lacks a nationwide high resolution crop-specific land cover map available publicly during the current growing season. The goal of this study was to map crops across the Continental US (CONUS) before the harvest, and to estimate the earliest date of classification by which crops can be mapped with sufficient accuracy (90% of full-season accuracy). The study employed a scalable cluster-then-label model that was trained on multiple years of MODIS NDVI using ground truth data in the form of US Department of Agriculture (USDA) Cropland Data Layer (CDL) products. The first step in the crop classification was to perform Multivariate Spatio-Temporal Clustering (MSTC) of annual MODIS-derived NDVI trajectories to create phenologically similar regions, or phenoregions. The second step was to assign crop labels to phenoregions based on spatial concordance between phenoregions and crop classes from CDL using Mapcurves. Assigning crop labels to phenoregions was performed within ecoregions to reduce classification errors due to spatial variability in phenology caused by variations in climate, agricultural practices, and growing conditions. The crop classifier was trained and validated on the years 2008–2014, then tested independently on 2015–2018. Ecoregion-level crop classification performed better than state-level and CONUS-level classification. Pixel-wise accuracy of classification for eight major crops by area was around 70% across the major corn-, soybeans- and winter wheat-producing areas, whereas regions characterized by high crop diversity had slightly lower accuracy. Classification accuracy for dominant crops like corn, soybeans, winter wheat, fallow/idle cropland and other hay/non alfalfa improved with time as they grew, reaching 90% of year-end accuracy by the end of August over each of the four unseen years in the test period. For corn and soybeans, the earliest dates of classification were found to be much earlier in the central regions of the Corn Belt (parts of Iowa, Illinois and Indiana) than in peripheral areas. The ability to map growing crops may permit near real-time monitoring of the health status and vigor of agricultural crops nationally. © 2020 The Author(s)
英文关键词	Cropland data layer; Mapcurves; MODIS; Multivariate spatio-temporal clustering; NDVI; Near real-time crop mapping; Phenoregions
语种	英语
scopus关键词	Agricultural robots; Cultivation; Radiometers; Accuracy of classifications; Agricultural practices; Classification accuracy; Classification errors; Near-real-time monitoring; Spatial variability; Spatio-temporal clustering; Us department of agricultures; Crops; agricultural land; alfalfa; crop plant; crop production; growing season; land cover; MODIS; NDVI; phenology; spatial variation; spatiotemporal analysis; water use; United States; Glycine max; Medicago sativa; Triticum aestivum; Zea mays
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/179112
作者单位	Sustainability and Data Sciences Laboratory, Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, United States; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Eastern Forest Environmental Threat Assessment Center (EFETAC), USDA Forest Service, Asheville, NC, United States; Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, United States
推荐引用方式 GB/T 7714	Konduri V.S.,Kumar J.,Hargrove W.W.,et al. Mapping crops within the growing season across the United States[J],2020,251.
APA	Konduri V.S.,Kumar J.,Hargrove W.W.,Hoffman F.M.,&Ganguly A.R..(2020).Mapping crops within the growing season across the United States.Remote Sensing of Environment,251.
MLA	Konduri V.S.,et al."Mapping crops within the growing season across the United States".Remote Sensing of Environment 251(2020).