Journal ArticleDOI
Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium
Prasad S. Thenkabail,Chandrashekhar Biradar,Praveen Noojipady,Venkateswarlu Dheeravath,Yuanjie Li,Manohar Velpuri,Murali Krishna Gumma,Obi Reddy P. Gangalakunta,Hugh Turral,Xueliang Cai,Jagath Vithanage,Mitchell A. Schull,Rishiraj Dutta +12 more
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A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data.Abstract:
A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3‐band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time‐series for 1997–1999, (b) Systeme pour l'Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50 km monthly time series for 1961–2000, (d) Global 30 Arc‐Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite‐1 Synthetic Aperture Radar (JERS‐1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992–1993. A single mega‐file data‐cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re‐sampling different data types into a common 1 km resolutio...read more
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Journal ArticleDOI
Groundwater use for irrigation - a global inventory
Stefan Siebert,Jacob J. Burke,Jean-Marc Faures,Karen Frenken,Jippe Hoogeveen,Petra Döll,Felix T. Portmann +6 more
TL;DR: In this article, the authors present a new global inventory on the extent of areas irrigated with groundwater, surface water or non-conventional sources, and determine the related consumptive water uses.
Journal ArticleDOI
Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data
Peng Gong,Jie Wang,Le Yu,Yongchao Zhao,Yuanyuan Zhao,Lu Liang,Zhenguo Niu,Xiaomeng Huang,Haohuan Fu,Shuang Liu,Congcong Li,Xueyan Li,Wei Fu,Caixia Liu,Yue Xu,Xiaoyi Wang,Qu Cheng,Luanyun Hu,Wenbo Yao,Han Zhang,Peng Zhu,Ziying Zhao,Haiying Zhang,Yaomin Zheng,Luyan Ji,Yawen Zhang,Han Chen,An Yan,JianHong Guo,Liang Yu,Lei Wang,Xiaojun Liu,Tingting Shi,Menghua Zhu,Yanlei Chen,Guangwen Yang,Ping Tang,Bing Xu,Chandra Giri,Nicholas Clinton,Zhiliang Zhu,Jin Chen,Jun Chen +42 more
TL;DR: In this article, the first 30 m resolution global land cover maps using Landsat Thematic Mapper TM and enhanced thematic mapper plus ETM+ data were produced. And the authors used four classifiers that were freely available were employed, including the conventional maximum likelihood classifier MLC, J4.8 decision tree classifier, Random Forest RF classifier and support vector machine SVM classifier.
Journal ArticleDOI
Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs
TL;DR: Five main applications with regional to global focus were selected for this review, where remote sensing contributions are traditionally strong, and are put in the context of the global challenges the agricultural sector is facing.
Journal ArticleDOI
A review on the practice of big data analysis in agriculture
TL;DR: A review of current studies and research works in agriculture which employ the recent practice of big data analysis, showing that the availability of hardware and software, techniques and methods for big dataAnalysis, as well as the increasing openness ofbig data sources, shall encourage more academic research, public sector initiatives and business ventures in the agricultural sector.
Journal ArticleDOI
Automated cropland mapping of continental Africa using Google Earth Engine cloud computing
Jun Xiong,Prasad S. Thenkabail,Murali Krishna Gumma,P Teluguntla,Justin Poehnelt,Russell G. Congalton,Kamini Yadav,David Thau +7 more
TL;DR: In this article, an automated cropland mapping algorithm (ACMA) was used to capture extensive knowledge on the croplands of Africa available through ground-based training samples, very high (sub-meter to five-meter) resolution imagery (VHRI), and local knowledge captured during field visits and/or sourced from country reports and literature.
References
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Journal ArticleDOI
Status of land cover classification accuracy assessment
TL;DR: It is likely that it is unlikely that a single standardized method of accuracy assessment and reporting can be identified, but some possible directions for future research that may facilitate accuracy assessment are highlighted.
Book
Pattern recognition principles
Julius T. Tou,Rafael C. Gonzalez +1 more
TL;DR: The present work gives an account of basic principles and available techniques for the analysis and design of pattern processing and recognition systems.
Journal ArticleDOI
Characteristics of maximum-value composite images from temporal AVHRR data
TL;DR: In this paper, satellite data from the Advanced Very High Resolution Radiometer sensor have been processed over several days and combined to produce spatially continuous cloud-free imagery over large areas with sufficient temporal resolution to study green-vegetation dynamics.
Journal ArticleDOI
Development of a global land cover characteristics database and igbp discover from 1 km avhrr data
Thomas R. Loveland,Bradley C. Reed,Jesslyn F. Brown,Donald O. Ohlen,Zhiliang Zhu,Limin Yang,James W. Merchant +6 more
TL;DR: The IGBP DISCover global land cover product as mentioned in this paper is an integral component of the Global Land Cover database, which provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface and presents a detailed interpretation of the extent of human development.
BookDOI
Water for Food Water for Life : A Comprehensive Assessment of Water Management in Agriculture
TL;DR: Molden et al. as discussed by the authors presented a comprehensive assessment of water management in agriculture, focusing on water for food, water for life, and water for the future of agriculture.