Uncertainties in estimates of cropland area in China: a comparison between an AVHRR-derived dataset and a Landsat TM-derived dataset


Autoria(s): Xiao, XM; Liu, JY(刘纪远); Zhuang, DF; Frolking, S; Boles, S; Xu, B; Liu, ML; Salas, W; Moore, B; Li, CS
Data(s)

2003

Resumo

The large uncertainties in estimates of cropland area in China may have significant implications for major cross-cutting themes of global environmental change-food production and trade, water resources, and the carbon and nitrogen cycles. Many earlier studies have indicated significant under-reporting of cropland area in China from official agricultural census statistics datasets. Space-borne remote sensing analyses provide an alternative and independent approach for estimating cropland area in China. In this study, we report estimates of cropland area from the National Land Cover Dataset (NLCD-96) at the 1:100,000 scale, which was generated by a multi-year National Land Cover Project in China through visual interpretation and digitization of Landsat TM images acquired mostly in 1995 and 1996. We compared the NLCD-96 dataset to another land cover dataset at I-km spatial resolution (the IGBP DIScover dataset version 2.0), which was generated from monthly Advanced Very High Resolution Radiometer (AVHRR)-derived Normalized Difference Vegetation Index (NDVI) from April, 1992 to March, 1993. The data comparison highlighted the limitation and uncertainty of cropland area estimates from the DIScover dataset. (C) 2003 Elsevier Science B.V. All rights reserved.

Identificador

http://ir.igsnrr.ac.cn/handle/311030/3197

http://www.irgrid.ac.cn/handle/1471x/144491

Fonte

Xiao, XM; Liu, JY(刘纪远); Zhuang, DF; Frolking, S; Boles, S; Xu, B; Liu, ML; Salas, W; Moore, B; Li, CS.Uncertainties in estimates of cropland area in China: a comparison between an AVHRR-derived dataset and a Landsat TM-derived dataset,GLOBAL AND PLANETARY CHANGE,2003,37(3-4):297-306

Palavras-Chave #Geography #Physical; Geosciences #Multidisciplinary #cropland area #China #Landsat TM #AVHRR #uncertainty
Tipo

期刊论文