Special Issue "Global Croplands"

Guest Editor
Dr. Prasad S. Thenkabail
Research Geographer 15, United States Geological Survey (USGS), 2255, N. Gemini Dr., Flagstaff, AZ 86001, USA
E-Mail:
Phone: +1 928 556 7221
Fax: +1 928 556 7169
Interests: remote sensing applications to: (a) agriculture, (b) water resources, (c) wetlands, (d) droughts, (e) land use\\land cover, (f) forestry, (g) natural resources management, and (h) environments; global mapping of croplands (irrigated and rainfed); hyperspectral remote sensing of vegetation; characterization of large river basins and deltas; wetland characterization and mapping; terrestrial Carbon storage and flux

Papers published in this Special Issue:

Thenkabail, P.
Global Croplands and their Importance for Water and Food Security in the Twenty-first Century: Towards an Ever Green Revolution that Combines a Second Green Revolution with a Blue Revolution.
Remote Sens. 2010, 2, 2305-2312; doi:10.3390/rs2092305.

Ozdogan, M.; Yang, Y.; Allez, G.; Cervantes, C.
Remote Sensing of Irrigated Agriculture: Opportunities and Challenges.
Remote Sens. 2010, 2, 2274-2304; doi:10.3390/rs2092274.

Laurila, H.; Karjalainen, M.; Kleemola, J.; Hyyppä, J.
Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing.
Remote Sens. 2010, 2, 2185-2239; doi:10.3390/rs2092185.

Demattê, J.; Fiorio, P.; Araújo, S.
Variation of Routine Soil Analysis When Compared with Hyperspectral Narrow Band Sensing Method.
Remote Sens. 2010, 2, 1998-2016; doi:10.3390/rs2081998.

Panda, S.; Hoogenboom, G.; Paz, J.
Remote Sensing and Geospatial Technological Applications for Site-specific Management of Fruit and Nut Crops: A Review.
Remote Sens. 2010, 2, 1973-1997; doi:10.3390/rs2081973.

Pittman, K.; Hansen, M.; Becker-Reshef, I.; Potapov, P.; Justice, C.
Estimating Global Cropland Extent with Multi-year MODIS Data.
Remote Sens. 2010, 2(7), 1844-1863; doi:10.3390/rs2071844.

Siebert, S.; Portmann, F.; Döll, P.
Global Patterns of Cropland Use Intensity.
Remote Sens. 2010, 2, 1625-1643; doi:10.3390/rs2071625.

Becker-Reshef, I.; Justice, C.; Sullivan, M.; Vermote, E.; Tucker, C.; Anyamba, A.; Small, J.; Pak, Ed; Masuoka, Ed; Schmaltz, J.; Hansen, M.; Pittman, K.; Birkett, C.; Williams, D.; Reynolds, C.; Doorn, B.
Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project.
Remote Sens. 2010, 2, 1589-1609; doi:10.3390/rs2061589.

Matejicek, L.; Kopackova, V.
Changes in Croplands as a Result of Large Scale Mining and the Associated Impact on Food Security Studied Using Time-Series Landsat Images.
Remote Sens. 2010, 2, 1463-1480; doi:10.3390/rs2061463.

Teixeira, A.
Determining Regional Actual Evapotranspiration of Irrigated Crops and Natural Vegetation in the São Francisco River Basin (Brazil) Using Remote Sensing and Penman-Monteith Equation.
Remote Sens. 2010, 2, 1287-1319; doi:10.3390/rs0251287.

Romaguera, M.; Hoekstra, A.; Su, Z.; Krol, M.; Salama, M.
Potential of Using Remote Sensing Techniques for Global Assessment of Water Footprint of Crops. Remote Sens. 2010, 2, 1177-1196; doi:10.3390/rs2041177.

Rudorff, B.; Aguiar, D.; Silva, W.; Sugawara, L.; Adami, M.; Moreira, M.
Studies on the Rapid Expansion of Sugarcane for Ethanol Production in São Paulo State (Brazil) Using Landsat Data. Remote Sens. 2010, 2, 1057-1076; doi:10.3390/rs2041057.

Conrad, C.; Fritsch, S.; Zeidler, J.; Rücker, G.; Dech, S.
Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data.
Remote Sens. 2010, 2, 1035-1056; doi:10.3390/rs2041035.

Zhang, X.; Seelan, S.; Seielstad, G.
Digital Northern Great Plains: A Web-Based System Delivering Near Real Time Remote Sensing Data for Precision Agriculture.
Remote Sens. 2010, 2, 861-873; doi:10.3390/rs2030861.

Milesi, C.; Samanta, A.; Hashimoto, H.; Kumar, K.; Ganguly, S.; Thenkabail, P.; Srivastava, A.; Nemani, R.; Myneni, R.
Decadal Variations in NDVI and Food Production in India.
Remote Sens. 2010, 2, 758-776; doi:10.3390/rs2030758.

Panda, S.; Ames, D.; Panigrahi, S.
Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques.
Remote Sens. 2010, 2, 673-696; doi:10.3390/rs2030673.

Hatfield, J.; Prueger, J.
Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices.
Remote Sens. 2010, 2, 562-578; doi:10.3390/rs2020562.

Appeaning Addo, K.
Urban and Peri-Urban Agriculture in Developing Countries Studied using Remote Sensing and In Situ Methods.
Remote Sens. 2010, 2, 497-513; doi:10.3390/rs2020497.

Thenkabail, P.; Hanjra, M.; Dheeravath, V.; Gumma, M.
A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches.
Remote Sens. 2010, 2, 211-261; doi:10.3390/rs2010211.

Nagler, P.; Morino, K.; Murray, R.; Osterberg, J.; Glenn, E.
An Empirical Algorithm for Estimating Agricultural and Riparian Evapotranspiration Using MODIS Enhanced Vegetation Index and Ground Measurements of ET. I. Description of Method.
Remote Sens. 2009, 1, 1273-1297; doi:10.3390/rs1041273.

Murray, R.; Nagler, P.; Morino, K.; Glenn, E.
An Empirical Algorithm for Estimating Agricultural and Riparian Evapotranspiration Using MODIS Enhanced Vegetation Index and Ground Measurements of ET. II. Application to the Lower Colorado River, U.S..
Remote Sens. 2009, 1, 1125-1138; doi:10.3390/rs1041125.

Cheesbrough, K.; Edmunds, J.; Tootle, G.; Kerr, G.; Pochop, L.
Estimated Wind River Range (Wyoming, USA) Glacier Melt Water Contributions to Agriculture.
Remote Sens. 2009, 1, 818-828; doi:10.3390/rs1040818.

Dear Colleagues,

With the era of green revolution fast fading, the world is looking at innovative approaches to curb potentially catastrophic effects of a looming long-term food crisis. Food security is tightly linked to croplands and their water use. More recently, other factors have come into play: conversion of croplands to bio-fuel lands and urban lands, loss of croplands to salinization and soil erosion, changing cropping patterns, production limits of existing crop varieties, and above all climate change. As a result, increases in grain production are becoming more difficult to achieve. Further, increasing cropland areas to grow more food is not an option given environmental and ecological impacts. So, we need to answer a central question: how do we grow more food from existing croplands and water resources and continue to feed the ballooning populations expected to reach 10 billion by 2050 from current 6 billion?

The greatest quantity of water used by humans is for producing food from croplands. For example, nearly 80% of all blue water (water in lakes, rivers, reservoirs, and ground water) used by humans is for growing food in irrigated croplands. Similarly, overwhelming proportion of the green water (water in soil moisture) used by humans is for producing food from rainfed croplands. However, water used by croplands is a complex phenomenon and depends on crop types, soil types, latitudelocation, type of irrigation, and a host of other issues. So, a proper understanding of these issues need us to inter-link croplands to water use, and food production considering a changing climate and keeping in view environmental sustainability, ecological integrity, and continued robust growth of economy.

In order to address above issues of great significance for humanity, we need to put collective knowledge of the best experts working in the area to facilitate solutions for generations to come.
Thereby, this special issue on “Global Croplands” by Journal “Remote Sensing” is an effort to bring together the collective knowledge base of the best experts involved in ensuring our food security for future generations. Given this, the overarching goal of this special volume will be to ensure that these diverse state-of-art knowledge base is available in one place for decision makers, experts, and other users in order to make use of the same and to advance our knowledge further to find smart solutions to overcome food crisis and produce in plenty for future generations. Thereby, I would like to seek articles from best multi-disciplinary experts addressing multitude of issues that are of relevance to ensure a food secure world for many generations to come. Specific topics may include:

Global cropland areas

• irrigated
• rainfed

• Remote sensing: At various spatial, spectral, radiometric, and temporal resolutions
• Non-remote sensing

• linking croplands to water use
• surface energy balance models
• other approaches like water balance
• water use assessments without use of thermal data

• Remote sensing approaches
• Non remote sensing approaches

• link to rainfed croplands and food production

• link to irrigated croplands and food production

• achievements, current stagnation, future growth possibilities

• opportunities
• Food security model

• linking economy to croplands, water use, and food security

• in mapping, modeling, and assessments

• croplands
• water
• remote sensing
• global: food security
• surface energy balance models
• water productivity
• spatial modeling
• agriculture
• economy
• irrigated croplands
• rainfed croplands
• climate change