Evaluation of global land cover maps for cropland area estimation in the conterminous United States

作     者：Lu Liang Peng Gong 

作者机构：Department of Environmental SciencePolicy and ManagementUniversity of CaliforniaBerkeleyCAUSA Ministry of Education Key Laboratory for Earth System ModelingCenter for Earth System ScienceTsinghua UniversityBeijingChina 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2015年第8卷第2期

页      面：102-117页

核心收录：

学科分类：0708[理学-地球物理学] 09[农学] 0903[农学-农业资源与环境] 0835[工学-软件工程] 0704[理学-天文学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 090301[农学-土壤学] 

基　　金：This research was supported by USGS(grant number G12AC20085) 

主　　题：cropland area global land cover FROM-GLC area estimation NASS survey 

摘      要：Global land cover data could provide continuously updated cropland acreage and distribution information,which is essential to a wide range of applications over large geographical *** area estimates were evaluated in the conterminous USA from four recent global land cover products:MODIS land cover(MODISLC)at 500-m resolution in 2010,GlobCover at 300-m resolution in 2009,FROM-GLC and FROM-GLC-agg at 30-m resolution based on Landsat imagery circa 2010 against the US Department of Agriculture survey *** estimators derived from the 30-m resolution Cropland Data Layer were applied to MODIS and GlobCover land cover products,which greatly improved the estimation accuracy of MODISLC by enhancing the correlation and decreasing mean deviation(MDev)and RMSE,but were less effective on GlobCover *** found that,in the USA,the CDL adjusted MODISLC was more suitable for applications that concern about the aggregated county cropland acreage,while FROM-GLC-agg gave the least deviation from the survey at the state *** between land cover map estimates and survey estimates is significant,but stronger at the state level than at the county *** regions where most mismatches happen at the county level,MODIS tends to underestimate,whereas MERIS and Landsat images incline to *** uncertainties should be taken into consideration in relevant *** interannual and seasonal effects,R 2 of the FROM-GLC regression model increased from 0.1 to 0.4,and the slope is much closer to *** analysis shows that images acquired in growing season are most suitable for Landsat-based cropland mapping in the conterminous USA.