Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India

被引:124
作者
Biggs, T. W. [1 ]
Thenkabail, P. S.
Gumma, M. K.
Scott, C. A.
Parthasaradhi, G. R.
Turral, H. N.
机构
[1] Int Water Management Inst, S Asia Reg Off, Patancheru 502324, Andhra Pradesh, India
[2] INTERA Inc, Niwot, CO 80544 USA
[3] Int Water Management Inst, Colombo, Sri Lanka
[4] Univ Arizona, Dept Geog & Reg Dev, Tucson, AZ 85721 USA
关键词
D O I
10.1080/01431160600851801
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Diverse irrigated areas were mapped in the Krishna River Basin (258,912 km(2)), southern India, using an irrigated fraction approach and multiple ancillary data sources. Unsupervised classification of a monthly time series of net difference vegetation index (NDVI) images from the Moderate Resolution Imaging Spectrometer (MODIS) over January-December 2002 generated 40 classes. Nine generalized classes included five irrigated classes with distinct NDVI time signatures: continuous irrigation, double-cropped, irrigated with low biomass, minor irrigation, and groundwater irrigation. Areas irrigated by surface water began greening 45 days after groundwater-irrigated areas, which allowed separation of surface and groundwater irrigation in the classification. The fraction of each class area irrigated was determined using three different methods: ground truth data, a linear regression model calibrated to agricultural census data, and visual interpretation of Landsat TM imagery. Irrigated fractions determined by the three methods varied least for the double-cropped irrigated class (0.62-0.79) and rangeland (0.00-0.02), and most for the minor irrigated class (0.06-0.43). Small irrigated patches (< 0.1 km(2)) accounted for more irrigated area than all major surface water irrigated areas combined. The irrigated fractions of the minor and groundwater-irrigated classes differed widely by method, suggesting that mapping patchy and small irrigated areas remains challenging, but comparison of multiple data sources improves confidence in the classification and highlights areas requiring more intensive fieldwork.
引用
收藏
页码:4245 / 4266
页数:22
相关论文
共 39 条
[1]   Forest classification of Southeast Asia using NOAA AVHRR data [J].
Achard, F ;
Estreguil, C .
REMOTE SENSING OF ENVIRONMENT, 1995, 54 (03) :198-208
[2]  
[Anonymous], 2000, GLOBAL GROUNDWATER S
[3]   Agricultural land-use change in Brazilian Amazonia between 1980 and 1995: Evidence from integrated satellite and census data [J].
Cardille, JA ;
Foley, JA .
REMOTE SENSING OF ENVIRONMENT, 2003, 87 (04) :551-562
[4]   Land cover mapping of large areas from satellites: status and research priorities [J].
Cihlar, J .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2000, 21 (6-7) :1093-1114
[5]   Classification by progressive generalization: A new automated methodology for remote sensing multichannel data [J].
Cihlar, J ;
Xia, QH ;
Chen, J ;
Beaubien, J ;
Fung, K ;
Latifovic, R .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 1998, 19 (14) :2685-2704
[6]   Global land cover classifications at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers [J].
De Fries, RS ;
Hansen, M ;
Townshend, JRG ;
Sohlberg, R .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 1998, 19 (16) :3141-3168
[7]   Global continuous fields of vegetation characteristics: a linear mixture model applied to multi-year 8 km AVHRR data [J].
Defries, RS ;
Hansen, MC ;
Townshend, JRG .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2000, 21 (6-7) :1389-1414
[8]   NDVI-DERIVED LAND-COVER CLASSIFICATIONS AT A GLOBAL-SCALE [J].
DEFRIES, RS ;
TOWNSHEND, JRG .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 1994, 15 (17) :3567-3586
[9]  
DROOGERS P, 2002, 36 INT WT MAN I
[10]  
FAO/UNEP, 1999, TERM INT RES PLANN M