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John Wesley Powell Center Working Group — Global Croplands

Select Bibliography:

Thenkabail P.S., Wu Z. An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data. Remote Sensing. 2012; 4(10):2890-2918.

Wu, Z., and Thenkabail, P.S. 2012. An automated cropland classification algorithm (ACCA) using Landsat and MODIS data combination for California. Photogrammetric Engineering and Remote Sensing. In review.

Thenkabail P.S., Knox J.W., Ozdogan, M., Gumma, M.K., Congalton, R.G., Wu, Z., Milesi, C., Finkral, A., Marshall, M., Mariotto, I., You, S. Giri, C. and Nagler, P. 2012. Assessing future risks to agricultural productivity, water resources and food security: how can remote sensing help?. Photogrammetric Engineering and Remote Sensing, August 2012 Special Issue on Global Croplands: Highlight Article. 78(8): 773-782.

WaterSMART — Mapping Irrigated and Rainfed Cropland Areas

Research on remote sensing derived estimates for global croplands extent (irrigated plus rainfed) featured in the journal Photogrammetric Engineering and Remote Sensing.
Research on remote sensing derived estimates for global
croplands extent (irrigated plus rainfed) featured in the journal
Photogrammetric Engineering and Remote Sensing.
The WaterSMART (Sustain and Manage America’s Resources for Tomorrow) Project is a U.S. Department of the Interior initiative on water conservation. It includes activities in Bureau of Reclamation, U.S. Geological Survey, and the Office of the Assistant Secretary for Water and Science. USGS is currently working on certain components of WaterSMART project with an overarching focus on development of a national framework for monitoring water use and water productivity on irrigated lands through advanced remote sensing and surface energy balance modeling. Within this overarching goal, creating geographic databases on croplands (irrigated and rainfed) using remote sensing and secondary datasets will help us understand the drought, water and food security scenario- locally and globally. The research will develop methods, approaches, and tools that will have broader implications for research pertaining to croplands-drought-water-food security for the US and the world. The specific focus of our research is to map irrigated and rainfed cropland areas using: A. existing high resolution orthophotography to establish locations of potentially irrigated lands (vector boundaries of these may be available and\or have to be digitized), B. multispectral data from Landsat; and C. Moderate-resolution Imaging Spectroradiometer (MODIS) to determine crop type, cropland watering method (e.g., irrigated or rainfed), cropping intensities, and cropland extent as well as evapotranspiration rates (consumptive use by crops). The work will involve uncertainty analysis (through assessment of accuracies and errors), field checking, and coordination with other project partners (a number of national entities). An important component of the project will be to develop an automated algorithm for cropland mapping and crop type assessment.

Principal Investigator: Prasad Thenkabail, pthenkabail@usgs.gov, Western Geographic Science Center, Menlo Park, CA

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