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Special Issue "Global Croplands"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (25 February 2010) | Viewed by 345268

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Special Issue Editor

Dr. Prasad S. Thenkabail
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Guest Editor

Senior Scientist (ST), U. S. Geological Survey (USGS), USGS Western Geographic Science Center (WGSC), 2255, N. Gemini Dr., Flagstaff, AZ 86001, USA
Interests: hyperspectral remote sensing, remote sensing expertise in a number of areas including: (a) global croplands, (b) agriculture, (c) water resources, (d) wetlands, (e) droughts, (f) land use/land cover, (g) forestry, (h) natural resources management, (i) environments, (j) vegetation, and (k) characterization of large river basins and deltas
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Special Issue Information

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

Methods of mapping croplands

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

Water use

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

Water productivity mapping

Remote sensing approaches
Non remote sensing approaches

Green water

link to rainfed croplands and food production

Blue water

link to irrigated croplands and food production

Green revolution

achievements, current stagnation, future growth possibilities

Blue revolution

opportunities
Food security model

Economy

linking economy to croplands, water use, and food security

Accuracies and errors

in mapping, modeling, and assessments

Other topics relevant to above introduction are welcome.

Prasad S. Thenkabail, Ph. D.
Guest Editor

Related New Book

See https://www.mdpi.com/about/announcements/49

Keywords

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

Published Papers (22 papers)

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Editorial

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Open AccessEditorial

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

Prasad S. Thenkabail

Remote Sens. 2010, 2(9), 2305-2312; https://doi.org/10.3390/rs2092305 - 27 Sep 2010

Cited by 57 | Viewed by 10244

Abstract

In an increasingly food insecure world, there is a critical need for us to have a comprehensive understanding of global croplands. The reality that the “green revolution” has ended is beginning to be felt around the World. Whereas, global population continues to increase at a rate of about 100 million per year and is expected to reach around 10 billion by 2050, cropland areas are not increasing and have stagnated around 1.5 billion hectares globally. Indeed, cropland areas have even begun to decrease in some countries with important food contribution (e.g., USA) due to increasing demand of fertile arable lands for alternative uses such as bio-fuels, encroachment from urbanization, and industrialization. [...] Full article

(This article belongs to the Special Issue Global Croplands)

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Research

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Open AccessArticle

Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing

and

Remote Sens. 2010, 2(9), 2185-2239; https://doi.org/10.3390/rs2092185 - 16 Sep 2010

Cited by 17 | Viewed by 11328

Abstract

During 1996–2006, the Ministry of Agriculture and Forestry in Finland (MAFF), MTT Agrifood Research and the Finnish Geodetic Institute performed a joint remote sensing satellite research project. It evaluated the applicability of optical satellite (Landsat, SPOT) data for cereal yield estimations in the annual crop inventory program. Four Optical Vegetation Indices models (I: Infrared polynomial, II: NDVI, III: GEMI, IV: PAR_ND/FAPAR) were validated to estimate cereal baseline yield levels (y_b) using solely optical harmonized satellite data (Optical Minimum Dataset). The optimized Model II (NDVI) y_b level was 4,240 kg/ha (R² 0.73, RMSE 297 kg/ha) for wheat and 4390 kg/ha (R² 0.61, RMSE 449 kg/ha) for barley and Model I y_b was 3,480 kg/ha for oats (R² 0.76, RMSE 258 kg/ha). Optical VGI yield estimates were validated with CropWatN crop model yield estimates using SPOT and NOAA data (mean R² 0.71, RMSE 436 kg/ha) and with composite SAR/ASAR and NDVI models (mean R² 0.61, RMSE 402 kg/ha) using both reflectance and backscattering data. CropWatN and Composite SAR/ASAR & NDVI model mean yields were 4,754/4,170 kg/ha for wheat, 4,192/3,848 kg/ha for barley and 4,992/2,935 kg/ha for oats. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Variation of Routine Soil Analysis When Compared with Hyperspectral Narrow Band Sensing Method

José A. M. Demattê

Peterson R. Fiorio

and

Suzana R. Araújo

Remote Sens. 2010, 2(8), 1998-2016; https://doi.org/10.3390/rs2081998 - 24 Aug 2010

Cited by 13 | Viewed by 8675

Abstract

The objectives of this research were to: (i) develop hyperspectral narrow-band models to determine soil variables such as organic matter content (OM), sum of cations (SC = Ca + Mg + K), aluminum saturation (m%), cations saturation (V%), cations exchangeable capacity (CEC), silt, sand and clay content using visible-near infrared (Vis-NIR) diffuse reflectance spectra; (ii) compare the variations of the chemical and the spectroradiometric soil analysis (Vis-NIR). The study area is located in São Paulo State, Brazil. The soils were sampled over an area of 473 ha divided into grids (100 × 100 m) with a total of 948 soil samples georeferenced. The laboratory RS data were obtained using an IRIS (Infrared Intelligent Spectroradiometer) sensor (400–2,500 nm) with a 2-nm spectral resolution between 450 and 1,000 nm and 4-nm between 1,000 and 2,500 nm. Satellite reflectance values were sampledfrom corrected Landsat Thematic Mapper (TM) images. Each pixel in the image was evaluated as its vegetation index, color compositions and soil line concepts regarding certain locationsof the field in the image. Chemical and physical analysis (organic matter content, sand, silt, clay, sum of cations, cations saturation, aluminum saturation and cations exchange capacity) were performed in the laboratory. Statisticalanalysis and multiple regression equations for soil attributepredictions using radiometric data were developed. Laboratorydata used 22 bands and 13 “Reflectance Inflexion Differences,RID” from different wavelength intervals of the optical spectrum.However, for TM-Landsat six bands were used in analysis (1, 2, 3, 4, 5, and 7).Estimationsof some tropical soil attributes were possible using laboratoryspectral analysis. Laboratory spectral reflectance (SR) presented high correlations withtraditional laboratory analyses for the soilattributes such as clay (R² = 0.84, RMSE = 3.75) and sand (R² = 0.85, RMSE = 3.74). The most sensitive narrow-bands in modeling (using 474 observations) these attributes were B8 (1,350–1,417 nm), B10 (1,417–1,449 nm), B11 (1,449–1,793 nm), B15 (1,927–2,102 nm), B16 (2,101–2,139 nm), and B17 (2,139–2,206 nm); B7 (975–1,350 nm), B10, B11, B16, B19 (2,206–2,258 nm) and B21 (2,258–2,389 nm) for clay and sand, respectively. The bands selected to model sand and clay, by orbital data, were 3, 5 and 7 of TM-Landsat-5 and 2, 5 and 7 sand and clay, respectively. The use of soilanalysis methodology by ground remote sensingconstitutes an alternative to traditional routine laboratoryanalysis. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Estimating Global Cropland Extent with Multi-year MODIS Data

and

Christopher O. Justice

Remote Sens. 2010, 2(7), 1844-1863; https://doi.org/10.3390/rs2071844 - 21 Jul 2010

Cited by 216 | Viewed by 17725

Abstract

This study examines the suitability of 250 m MODIS (MODerate Resolution Imaging Spectroradiometer) data for mapping global cropland extent. A set of 39 multi-year MODIS metrics incorporating four MODIS land bands, NDVI (Normalized Difference Vegetation Index) and thermal data was employed to depict cropland phenology over the study period. Sub-pixel training datasets were used to generate a set of global classification tree models using a bagging methodology, resulting in a global per-pixel cropland probability layer. This product was subsequently thresholded to create a discrete cropland/non-cropland indicator map using data from the USDA-FAS (Foreign Agricultural Service) Production, Supply and Distribution (PSD) database describing per-country acreage of production field crops. Five global land cover products, four of which attempted to map croplands in the context of multiclass land cover classifications, were subsequently used to perform regional evaluations of the global MODIS cropland extent map. The global probability layer was further examined with reference to four principle global food crops: corn, soybeans, wheat and rice. Overall results indicate that the MODIS layer best depicts regions of intensive broadleaf crop production (corn and soybean), both in correspondence with existing maps and in associated high probability matching thresholds. Probability thresholds for wheat-growing regions were lower, while areas of rice production had the lowest associated confidence. Regions absent of agricultural intensification, such as Africa, are poorly characterized regardless of crop type. The results reflect the value of MODIS as a generic global cropland indicator for intensive agriculture production regions, but with little sensitivity in areas of low agricultural intensification. Variability in mapping accuracies between areas dominated by different crop types also points to the desirability of a crop-specific approach rather than attempting to map croplands in aggregate. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Global Patterns of Cropland Use Intensity

Stefan Siebert

Felix T. Portmann

and

Petra Döll

Remote Sens. 2010, 2(7), 1625-1643; https://doi.org/10.3390/rs2071625 - 24 Jun 2010

Cited by 121 | Viewed by 16938

Abstract

This study presents a global scale analysis of cropping intensity, crop duration and fallow land extent computed by using the global dataset on monthly irrigated and rainfed crop areas MIRCA2000. MIRCA2000 was mainly derived from census data and crop calendars from literature. Global cropland extent was 16 million km² around the year 2000 of which 4.4 million km² (28%) was fallow, resulting in an average cropping intensity of 0.82 for total cropland extent and of 1.13 when excluding fallow land. The lowest cropping intensities related to total cropland extent were found for Southern Africa (0.45), Central America (0.49) and Middle Africa (0.54), while highest cropping intensities were computed for Eastern Asia (1.04) and Southern Asia (1.0). In remote or arid regions where shifting cultivation is practiced, fallow periods last 3–10 years or even longer. In contrast, crops are harvested two or more times per year in highly populated, often irrigated tropical or subtropical lowlands where multi-cropping systems are common. This indicates that intensification of agricultural land use is a strategy that may be able to significantly improve global food security. There exist large uncertainties regarding extent of cropland, harvested crop area and therefore cropping intensity at larger scales. Satellite imagery and remote sensing techniques provide opportunities for decreasing these uncertainties and to improve the MIRCA2000 inventory. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project

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Remote Sens. 2010, 2(6), 1589-1609; https://doi.org/10.3390/rs2061589 - 18 Jun 2010

Cited by 212 | Viewed by 19327

Abstract

In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. Timely information on global crop production is indispensable for combating the growing stress on the world’s crop production and for securing both short-term and long-term stable and reliable supply of food. Global agriculture monitoring systems are critical to providing this kind of intelligence and global earth observations are an essential component of an effective global agricultural monitoring system as they offer timely, objective, global information on croplands distribution, crop development and conditions as the growing season progresses. The Global Agriculture Monitoring Project (GLAM), a joint NASA, USDA, UMD and SDSU initiative, has built a global agricultural monitoring system that provides the USDA Foreign Agricultural Service (FAS) with timely, easily accessible, scientifically-validated remotely-sensed data and derived products as well as data analysis tools, for crop-condition monitoring and production assessment. This system is an integral component of the USDA’s FAS Decision Support System (DSS) for agriculture. It has significantly improved the FAS crop analysts’ ability to monitor crop conditions, and to quantitatively forecast crop yields through the provision of timely, high-quality global earth observations data in a format customized for FAS alongside a suite of data analysis tools. FAS crop analysts use these satellite data in a ‘convergence of evidence’ approach with meteorological data, field reports, crop models, attaché reports and local reports. The USDA FAS is currently the only operational provider of timely, objective crop production forecasts at the global scale. These forecasts are routinely used by the other US Federal government agencies as well as by commodity trading companies, farmers, relief agencies and foreign governments. This paper discusses the operational components and new developments of the GLAM monitoring system as well as the future role of earth observations in global agricultural monitoring. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Changes in Croplands as a Result of Large Scale Mining and the Associated Impact on Food Security Studied Using Time-Series Landsat Images

Lubos Matejicek

and

Veronika Kopackova

Remote Sens. 2010, 2(6), 1463-1480; https://doi.org/10.3390/rs2061463 - 01 Jun 2010

Cited by 28 | Viewed by 10218

Abstract

Geographic information systems and satellite remote sensing information are emerging technologies in land-cover change assessment. They now provide an opportunity to gain insights into land-cover change properties through the spatio-temporal data capture over several decades. The time series of Landsat images covering the 1985–2009 period is used here to explore the impacts of surface mining and reclamation, which constitute a dominant force in land-cover changes in the northwestern regions of the Czech Republic. Advanced quantification of the extent of mining activities is important for assessing how these land-cover changes affect ecosystem services such as croplands. The images employed from 1985, 1988, 1990, 2000, 2002, 2003, 2004, 2005, 2006, 2007, 2008, and 2009 assist in mapping the extent of surface mines and mine reclamation for large surface mines in a few selected areas of interest. The image processing techniques are based on pixel-by-pixel calculation of the vegetation index, such as NDVI. The NDVI values are classified into the defined classes based on CORINE Land Cover 2000 data in a 3280 km² strip of Landsat images. This distribution of NDVI values is used to estimate the land-cover classes in the local areas of interest (184 km², 368 km², 737 km², and 1,474 km²). Thus, the approximate land-cover stability of the 3,280 km² strip during the whole 1985–2009 period is used to explore land-cover disturbances in the local areas of surface mines. In the case of NDVI, it also includes variations, presumably caused by seasonal vegetation effects, and local meteorological conditions. However, the main trends related to mining activities during the long-term period can be clearly understood. As a result, other objectives can be explored in the 1985–2009 period, such as cropland changes to other land use classes, changes of cropland patterns, and their impacts on food security. The presented spatio-temporal modeling based on long time series from 12 satellite images provides considerable experience for processing NDVI in the framework of identification of land-cover classes and also, to a certain degree, cropland variability with its impact on food security. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Determining Regional Actual Evapotranspiration of Irrigated Crops and Natural Vegetation in the São Francisco River Basin (Brazil) Using Remote Sensing and Penman-Monteith Equation

Antônio H. de C. Teixeira

Remote Sens. 2010, 2(5), 1287-1319; https://doi.org/10.3390/rs0251287 - 06 May 2010

Cited by 100 | Viewed by 14417

Abstract

To achieve sustainable development and to ensure water availability in hydrological basins, water managers need tools to determine the actual evapotranspiration (ET) on a large scale. Field energy balances from irrigated and natural ecosystems together with a net of agro-meteorological stations were used to develop two models for ET quantification at basin scale, based on the Penman-Monteith equation. The first model (PM1) uses the resistances to the latent heat fluxes estimated from satellite measurements, while the second one (PM2) is based on the ratio of ET to the reference evapotranspiration (ET₀) and its relation to remote sensing parameters. The models were applied in the Low-Middle São Francisco river basin in Brazil and, after comparison against field results, showed good agreements with PM1 and PM2 explaining, respectively, 79% and 89% of the variances and mean square errors (RMSE) of 0.44 and 0.34 mm d⁻¹. Even though the PM1 model was not chosen for ET calculations, the equation for surface resistance (r_s) was applied to infer the soil moisture conditions in a simplified vegetation classification. The maximum values of r_s were for natural vegetation—caatinga (average of 1,937 s m⁻¹). Wine grape and mango orchard presented similar values around 130 s m⁻¹, while table grape presented the lowest ones, averaging 74 s m⁻¹. Petrolina and Juazeiro, in Pernambuco (PE) and Bahia (BA) states, respectively, were highlighted with the biggest irrigated areas. The highest increments are for vineyards and mango orchards. For the first crop the maximum increment was verified between 2003 and 2004 in Petrolina-PE, when the cultivated area increased 151%. In the case of mango orchards the most significant period was from 2005 to 2006 in Juazeiro-BA (129%). As the best performance was for PM2, it was selected and used to analyse the regional ET at daily and annual scales, making use of Landsat images and a geographic information system for different soil moisture conditions. Considering the daily rates of the regional ET, pixels with values lower than 1.0 mm d⁻¹ occurred outside the rainy season, representing the caatinga species. Values from 1.0 to 5.0 mm d⁻¹ during the driest conditions of the year coincided with irrigated crops, being the highest values for table grapes. The highest accumulated ET values during 2006 were for mango orchards, being around 500–1,300 mm yr⁻¹. Vineyards presented lower values, ranging from 450–800 mm yr⁻¹, while in caatinga they were between 200 and 400 mm yr⁻¹. It could be concluded that irrigated mango orchards and vineyards in that year consumed more water than caatinga by factors of 3 and 2, respectively. The mango orchards and vineyard areas, representing 19.4 and 8.2% of the total irrigated area, respectively, resulting in a total evaporative depletion of 0.22 km³ yr⁻¹ in the growing regions comprised of the agro-meteorological stations. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Potential of Using Remote Sensing Techniques for Global Assessment of Water Footprint of Crops

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Remote Sens. 2010, 2(4), 1177-1196; https://doi.org/10.3390/rs2041177 - 26 Apr 2010

Cited by 60 | Viewed by 12792

Abstract

Remote sensing has long been a useful tool in global applications, since it provides physically-based, worldwide, and consistent spatial information. This paper discusses the potential of using these techniques in the research field of water management, particularly for ‘Water Footprint’ (WF) studies. The WF of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. In this paper evapotranspiration, precipitation, water storage, runoff and land use are identified as key variables to potentially be estimated by remote sensing and used for WF assessment. A mass water balance is proposed to calculate the volume of irrigation applied, and green and blue WF are obtained from the green and blue evapotranspiration components. The source of remote sensing data is described and a simplified example is included, which uses evapotranspiration estimates from the geostationary satellite Meteosat 9 and precipitation estimates obtained with the Climatic Prediction Center Morphing Technique (CMORPH). The combination of data in this approach brings several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which are discussed in detail. This work provides new tools for global WF assessment and represents an innovative approach to global irrigation mapping, enabling the estimation of green and blue water use. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Studies on the Rapid Expansion of Sugarcane for Ethanol Production in São Paulo State (Brazil) Using Landsat Data

Bernardo Friedrich Theodor Rudorff

Daniel Alves Aguiar

Wagner Fernando Silva

Luciana Miura Sugawara

Marcos Adami

and

Mauricio Alves Moreira

Remote Sens. 2010, 2(4), 1057-1076; https://doi.org/10.3390/rs2041057 - 09 Apr 2010

Cited by 315 | Viewed by 33417

Abstract

This study’s overarching aim is to establish the areal extent and characteristics of the rapid sugarcane expansion and land use change in São Paulo state (Brazil) as a result of an increase in the demand for ethanol, using Landsat type remotely sensed data. In 2003 flex fuel automobiles started to enter the Brazilian consumer market causing a dramatic expansion of sugarcane areas from 2.57 million ha in 2003 to 4.45 million ha in 2008. Almost all the land use change, for the sugarcane expansion of crop year 2008/09, occurred on pasture and annual crop land, being equally distributed on each. It was also observed that during the 2008 harvest season, the burned sugarcane area was reduced to 50% of the total harvested area in response to a protocol that aims to cease sugarcane straw burning practice by 2014 for mechanized areas. This study indicates that remote sensing images have efficiently evaluated important characteristics of the sugarcane cultivation dynamic providing quantitative results that are relevant to the debate of sustainable ethanol production from sugarcane in Brazil. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Per-Field Irrigated Crop Classification in Arid Central Asia Using SPOT and ASTER Data

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Remote Sens. 2010, 2(4), 1035-1056; https://doi.org/10.3390/rs2041035 - 08 Apr 2010

Cited by 145 | Viewed by 14288

Abstract

The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5–5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15–30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Digital Northern Great Plains: A Web-Based System Delivering Near Real Time Remote Sensing Data for Precision Agriculture

Xiaodong Zhang

Santhosh Seelan

and

George Seielstad

Remote Sens. 2010, 2(3), 861-873; https://doi.org/10.3390/rs2030861 - 22 Mar 2010

Cited by 14 | Viewed by 10030

Abstract

The US Northern Great Plains is one of the world’s most agriculturally productive areas. Growers in the region are eager to adopt modern technology to improve productivity and income. Use of information derived from remote sensing satellites to better manage farms and rangelands while reducing environmental impacts has gained popularity in recent years. However, prohibitive costs and non-availability of near real time remote sensing imagery has slowed the adoption of this technology for in-field decision making. Digital Northern Great Plains (DNGP), a web based remote sensing data dissemination system, was developed to address these drawbacks. It provides end users easy and free access to a variety of imagery and products in near real time. With delivery of archived and current data, DNGP has helped farmers and ranchers reduce operational costs and increase productivity through a variety of innovative applications. Moreover, negative environmental impacts were lessened. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Decadal Variations in NDVI and Food Production in India

Ramakrishna R. Nemani

and

Ranga B. Myneni

Remote Sens. 2010, 2(3), 758-776; https://doi.org/10.3390/rs2030758 - 11 Mar 2010

Cited by 56 | Viewed by 14106

Abstract

In this study we use long-term satellite, climate, and crop observations to document the spatial distribution of the recent stagnation in food grain production affecting the water-limited tropics (WLT), a region where 1.5 billion people live and depend on local agriculture that is constrained by chronic water shortages. Overall, our analysis shows that the recent stagnation in food production is corroborated by satellite data. The growth rate in annually integrated vegetation greenness, a measure of crop growth, has declined significantly (p < 0.10) in 23% of the WLT cropland area during the last decade, while statistically significant increases in the growth rates account for less than 2%. In most countries, the decade-long declines appear to be primarily due to unsustainable crop management practices rather than climate alone. One quarter of the statistically significant declines are observed in India, which with the world’s largest population of food-insecure people and largest WLT croplands, is a leading example of the observed declines. Here we show geographically matching patterns of enhanced crop production and irrigation expansion with groundwater that have leveled off in the past decade. We estimate that, in the absence of irrigation, the enhancement in dry-season food grain production in India, during 1982–2002, would have required an increase in annual rainfall of at least 30% over almost half of the cropland area. This suggests that the past expansion of use of irrigation has not been sustainable. We expect that improved surface and groundwater management practices will be required to reverse the recent food grain production declines. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques

Sudhanshu Sekhar Panda

Daniel P. Ames

and

Suranjan Panigrahi

Remote Sens. 2010, 2(3), 673-696; https://doi.org/10.3390/rs2030673 - 01 Mar 2010

Cited by 258 | Viewed by 26278

Abstract

Spatial variability in a crop field creates a need for precision agriculture. Economical and rapid means of identifying spatial variability is obtained through the use of geotechnology (remotely sensed images of the crop field, image processing, GIS modeling approach, and GPS usage) and data mining techniques for model development. Higher-end image processing techniques are followed to establish more precision. The goal of this paper was to investigate the strength of key spectral vegetation indices for agricultural crop yield prediction using neural network techniques. Four widely used spectral indices were investigated in a study of irrigated corn crop yields in the Oakes Irrigation Test Area research site of North Dakota, USA. These indices were: (a) red and near-infrared (NIR) based normalized difference vegetation index (NDVI), (b) green and NIR based green vegetation index (GVI), (c) red and NIR based soil adjusted vegetation index (SAVI), and (d) red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for corn yield during 3 years (1998, 1999, and 2001) and for the pooled data of these 3 years. Initially, Back-propagation Neural Network (BPNN) models were developed, including 16 models (4 indices * 4 years including the data from the pooled years) to test for the efficiency determination of those four vegetation indices in corn crop yield prediction. The corn yield was best predicted using BPNN models that used the means and standard deviations of PVI grid images. In all three years, it provided higher prediction accuracies, coefficient of determination (r²), and lower standard error of prediction than the models involving GVI, NDVI, and SAVI image information. The GVI, NDVI, and SAVI models for all three years provided average testing prediction accuracies of 24.26% to 94.85%, 19.36% to 95.04%, and 19.24% to 95.04%, respectively while the PVI models for all three years provided average testing prediction accuracies of 83.50% to 96.04%. The PVI pool model provided better average testing prediction accuracy of 94% with respect to other vegetation models, for which it ranged from 89–93%. Similarly, the PVI pool model provided coefficient of determination (r²) value of 0.45 as compared to 0.31–0.37 for other index models. Log₁₀ data transformation technique was used to enhance the prediction ability of the PVI models of years 1998, 1999, and 2001 as it was chosen as the preferred index. Another model (Transformed PVI (Pool)) was developed using the log₁₀ transformed PVI image information to show its global application. The transformed PVI models provided average corn yield prediction accuracies of 90%, 97%, and 98% for years 1998, 1999, and 2001, respectively. The pool PVI transformed model provided as average testing accuracy of 93% along with r² value of 0.72 and standard error of prediction of 0.05 t/ha. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices

Jerry L. Hatfield

and

John H. Prueger

Remote Sens. 2010, 2(2), 562-578; https://doi.org/10.3390/rs2020562 - 23 Feb 2010

Cited by 240 | Viewed by 14488

Abstract

The paper investigates the value of using distinct vegetation indices to quantify and characterize agricultural crop characteristics at different growth stages. Research was conducted on four crops (corn, soybean, wheat, and canola) over eight years grown under different tillage practices and nitrogen management practices that varied rate and timing. Six different vegetation indices were found most useful, depending on crop phenology and management practices: (a) simple ratio for biomass, (b) NDVI for intercepted PAR, (c) SAVI for early stages of LAI, (d) EVI for later stages of LAI, (e) CIgreen for leaf chlorophyll, (f) NPCI for chlorophyll during later stages, and (g) PSRI to quantify plant senescence. There were differences among varieties of corn and soybean for the vegetation indices during the growing season and these differences were a function of growth stage and vegetative index. These results clearly imply the need to use multiple vegetation indices to best capture agricultural crop characteristics. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Urban and Peri-Urban Agriculture in Developing Countries Studied using Remote Sensing and In Situ Methods

Kwasi Appeaning Addo

Remote Sens. 2010, 2(2), 497-513; https://doi.org/10.3390/rs2020497 - 02 Feb 2010

Cited by 48 | Viewed by 18975

Abstract

Urban farming, practiced by about 800 million people globally, has contributed significantly to food security and food safety. The practice has sustained livelihood of the urban and peri-urban low income dwellers in developing countries for many years. Its popularity among the urban low income is largely due to lack of formal jobs and as a means of adding up to household income. There is increasing need to sustainably manage urban farming in developing nations in recent times. Population increase due to rural-urban migration and natural, coupled with infrastructure developments are competing with urban farming for available space and scarce resources such as water for irrigation. Lack of reliable data on the extent of urban/peri-urban areas being used for farming has affected developing sustainable policies to manage urban farming in Accra. Using ground based survey methods to map the urban farmlands are inherently problematic and prohibitively expensive. This has influenced accurate assessment of the future role of urban farming in enhancing food security. Remote sensing, however, allows areas being used as urban farmlands to be rapidly established at relatively low cost. This paper will review advances in the use of remote sensing technology to develop an integrated monitoring technique for urban farmlands in Accra. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

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

Prasad S. Thenkabail

Munir A. Hanjra

Venkateswarlu Dheeravath

and

Muralikrishna Gumma

Remote Sens. 2010, 2(1), 211-261; https://doi.org/10.3390/rs2010211 - 04 Jan 2010

Cited by 84 | Viewed by 17837

Abstract

This paper presents an exhaustive review of global croplands and their water use, for the end of last millennium, mapped using remote sensing and non-remote sensing approaches by world’s leading researchers on the subject. A comparison at country scale of global cropland area estimated by these studies had a high R²-value of 0.89–0.94. The global cropland area estimates amongst different studies are quite close and range between 1.47–1.53 billion hectares. However, significant uncertainties exist in determining irrigated areas which, globally, consume nearly 80% of all human water use. The estimates show that the total water use by global croplands varies between 6,685 to 7,500 km³ yr⁻¹ and of this around 4,586 km³ yr⁻¹ is by rainfed croplands (green water use) and the rest by irrigated croplands (blue water use). Irrigated areas use about 2,099 km³ yr⁻¹ (1,180 km³ yr⁻¹ of blue water and the rest from rain that falls over irrigated croplands). However, 1.6 to 2.5 times the blue water required by irrigated croplands is actually withdrawn from reservoirs or pumping of ground water, suggesting an irrigation efficiency of only between 40–62 percent. The weaknesses, trends, and future directions to precisely estimate the global croplands are examined. Finally, the paper links global croplands and their water use to a paradigm for ensuring future food security. Full article

(This article belongs to the Special Issue Global Croplands)

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Graphical abstract

Open AccessArticle

An Empirical Algorithm for Estimating Agricultural and Riparian Evapotranspiration Using MODIS Enhanced Vegetation Index and Ground Measurements of ET. I. Description of Method

and

Remote Sens. 2009, 1(4), 1273-1297; https://doi.org/10.3390/rs1041273 - 10 Dec 2009

Cited by 61 | Viewed by 12552

Abstract

We used the Enhanced Vegetation Index (EVI) from MODIS to scale evapotranspiration (ET_actual) over agricultural and riparian areas along the Lower Colorado River in the southwestern US. Ground measurements of ET_actual by alfalfa, saltcedar, cottonwood and arrowweed were expressed as fraction of potential (reference crop) ET_o (ET_oF) then regressed against EVI scaled between bare soil (0) and full vegetation cover (1.0) (EVI*). EVI* values were calculated based on maximum and minimum EVI values from a large set of riparian values in a previous study. A satisfactory relationship was found between crop and riparian plant ET_oF and EVI*, with an error or uncertainty of about 20% in the mean estimate (mean ET_actual = 6.2 mm d⁻¹, RMSE = 1.2 mm d⁻¹). The equation for ET_actual was: ET_actual = 1.22 × ET_o-BC × EVI*, where ET_o-BC is the Blaney Criddle formula for ET_o. This single algorithm applies to all the vegetation types in the study, and offers an alternative to ET_actual estimates that use crop coefficients set by expert opinion, by using an algorithm based on the actual state of the canopy as determined by time-series satellite images. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

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.

and

Remote Sens. 2009, 1(4), 1125-1138; https://doi.org/10.3390/rs1041125 - 20 Nov 2009

Cited by 41 | Viewed by 12468

Abstract

Large quantities of water are consumed by irrigated crops and riparian vegetation in western U.S. irrigation districts. Remote sensing methods for estimating evaporative water losses by soil and vegetation (evapotranspiration, ET) over wide river stretches are needed to allocate water for agricultural and environmental needs. We used the Enhanced Vegetation Index (EVI) from MODIS sensors on the Terra satellite to scale ET over agricultural and riparian areas along the Lower Colorado River in the southwestern U.S., using a linear regression equation between ET of riparian plants and alfalfa measured on the ground, and meteorological and remote sensing data, with an error or uncertainty of about 20%. The algorithm was applied to irrigation districts and riparian areas from Lake Mead to the U.S./Mexico border. The results for agricultural crops were similar to results produced by crop coefficients developed for the irrigation districts along the river. However, riparian ET was only half as great as crop coefficient estimates set by expert opinion, equal to about 40% of reference crop evapotranspiration. Based on reported acreages in 2007, agricultural crops (146,473 ha) consumed 2.2 × 10⁹ m³ yr⁻¹ of water. All riparian shrubs and trees (47,014 ha) consumed 3.8 × 10⁸ m³ yr⁻¹, of which saltcedar, the dominant riparian shrub (25,044 ha), consumed 1.8 × 10⁸ m³ yr⁻¹, about 1% of the annual flow of the river. This method could supplement existing protocols for estimating ET by providing an estimate based on the actual state of the canopy as determined by frequent-return satellite data. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessArticle

Estimated Wind River Range (Wyoming, USA) Glacier Melt Water Contributions to Agriculture

and

Remote Sens. 2009, 1(4), 818-828; https://doi.org/10.3390/rs1040818 - 28 Oct 2009

Cited by 22 | Viewed by 12018

Abstract

In 2008, Wyoming was ranked 8th in barley production and 20th in hay production in the United States and these crops support Wyoming’s $800 million cattle industry. However, with a mean elevation of 2,040 meters, much of Wyoming has a limited crop growing season (as little as 60 days) and relies on late-summer and early-fall streamflow for agricultural water supply. Wyoming is host to over 80 glaciers with the majority of these glaciers being located in the Wind River Range. These “frozen reservoirs” provide a stable source of streamflow (glacier meltwater) during this critical late-summer and early-fall growing season. Given the potential impacts of climate change (increased temperatures resulting in glacier recession), the quantification of glacier meltwater during the late-summer and early-fall growing seasons is needed. Glacier area changes in the Wind River Range were estimated for 42 glaciers using Landsat data from 1985 to 2005. The total surface area of the 42 glaciers was calculated to be 41.2 ± 11.7 km² in 1985 and 30.8 ± 8.2 km² in 2005, an average decrease of 25% over the 21 year period. Small glaciers experienced noticeably more area reduction than large glaciers. Of the 42 glaciers analyzed, 17 had an area of greater than 0.5 km² in 1985, while 25 were less than 0.5 km² in 1985. The glaciers with a surface area less than 0.5 km²experienced an average surface area loss (fraction of 1985 surface area) of 43%, while the larger glaciers (greater than 0.5 km²) experienced an average surface area loss of 22%. Applying area-volume scaling relationships for glaciers, volume loss was estimated to be 409 × 106 m³ over the 21 year period, which results in an estimated 4% to 10% contribution to warm season (July–October) streamflow. Full article

(This article belongs to the Special Issue Global Croplands)

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Review

Jump to: Editorial, Research

Open AccessReview

Remote Sensing of Irrigated Agriculture: Opportunities and Challenges

and

Remote Sens. 2010, 2(9), 2274-2304; https://doi.org/10.3390/rs2092274 - 27 Sep 2010

Cited by 234 | Viewed by 20305

Abstract

Over the last several decades, remote sensing has emerged as an effective tool to monitor irrigated lands over a variety of climatic conditions and locations. The objective of this review, which summarizes the methods and the results of existing remote sensing studies, is to synthesize principle findings and assess the state of the art. We take a taxonomic approach to group studies based on location, scale, inputs, and methods, in an effort to categorize different approaches within a logical framework. We seek to evaluate the ability of remote sensing to provide synoptic and timely coverage of irrigated lands in several spectral regions. We also investigate the value of archived data that enable comparison of images through time. This overview of the studies to date indicates that remote sensing-based monitoring of irrigation is at an intermediate stage of development at local scales. For instance, there is overwhelming consensus on the efficacy of vegetation indices in identifying irrigated fields. Also, single date imagery, acquired at peak growing season, may suffice to identify irrigated lands, although to multi-date image data are necessary for improved classification and to distinguish different crop types. At local scales, the mapping of irrigated lands with remote sensing is also strongly affected by the timing of image acquisition and the number of images used. At the regional and global scales, on the other hand, remote sensing has not been fully operational, as methods that work in one place and time are not necessarily transferable to other locations and periods. Thus, at larger scales, more work is required to indentify the best spectral indices, best time periods, and best classification methods under different climatological and cultural environments. Existing studies at regional scales also establish the fact that both remote sensing and national statistical approaches require further refinement with a substantial investment of time and resources for ground-truthing. An additional challenge in mapping irrigation across large areas occurs in fragmented landscapes with small irrigated and cultivated fields, where the spatial scale of observations is pitted against the need for high frequency temporal acquisitions. Finally, this review identifies passive and active microwave observations, advanced image classification methods, and data fusion including optical and radar sensors or with information from sources with multiple spatial and temporal characteristics as key areas where additional research is needed. Full article

(This article belongs to the Special Issue Global Croplands)

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Open AccessReview

Remote Sensing and Geospatial Technological Applications for Site-specific Management of Fruit and Nut Crops: A Review

Sudhanshu Sekhar Panda

Gerrit Hoogenboom

and

Joel O. Paz

Remote Sens. 2010, 2(8), 1973-1997; https://doi.org/10.3390/rs2081973 - 23 Aug 2010

Cited by 49 | Viewed by 14811

Abstract

Site-specific crop management (SSCM) is one facet of precision agriculture which is helping increase production with minimal input. It has enhanced the cost-benefit scenario in crop production. Even though the SSCM is very widely used in row crop agriculture like corn, wheat, rice, soybean, etc. it has very little application in cash crops like fruit and nut. The main goal of this review paper was to conduct a comprehensive review of advanced technologies, including geospatial technologies, used in site-specific management of fruit and nut crops. The review explores various remote sensing data from different platforms like satellite, LIDAR, aerial, and field imaging. The study analyzes the use of satellite sensors, such as Quickbird, Landsat, SPOT, and IRS imagery as well as hyperspectral narrow-band remote sensing data in study of fruit and nut crops in blueberry, citrus, peach, apple, etc. The study also explores other geospatial technologies such as GPS, GIS spatial modeling, advanced image processing techniques, and information technology for suitability study, orchard delineation, and classification accuracy assessment. The study also provides an example of a geospatial model developed in ArcGIS ModelBuilder to automate the blueberry production suitability analysis. The GIS spatial model is developed using various crop characteristics such as chilling hours, soil permeability, drainage, and pH, and land cover to determine the best sites for growing blueberry in Georgia, U.S. The study also provides a list of spectral reflectance curves developed for some fruit and nut crops, blueberry, crowberry, redblush citrus, orange, prickly pear, and peach. The study also explains these curves in detail to help researchers choose the image platform, sensor, and spectrum wavelength for various fruit and nut crops SSCM. Full article

(This article belongs to the Special Issue Global Croplands)

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