Venkateswarlu Dheeravath

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

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 However, 1.6 to 2.5 times the blue water required by irrigated croplands is actually withdrawn from reservoirs or pumping of ground water, suggesting an irrigation efficiency of only between 40–62 percent.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.

Influence of resolution in irrigated area mapping and area estimation

The overarching goal of this paper was to determine how irrigated areas change with resolution (or scale) of imagery. Specific objectives investigated were to (a) map irrigated areas using four distinct spatial resolutions (or scales), (b) determine how irrigated areas change with resolutions, and (c) establish the causes of differences in resolution-based irrigated areas. The study was conducted in the very large Krishna River basin (India), which has a high degree of formal contiguous, and informal fragmented irrigated areas. The irrigated areas were mapped using satellite sensor data at four distinct resolutions: (a) NOAA AVHRR Pathfinder 10,000 m, (b) Terra MODIS 500 m, (c) Terra MODIS 250 m, and (d) Landsat ETM 30 m. The proportion of irrigated areas relative to Landsat 30 m derived irrigated areas (9.36 million hectares for the Krishna basin) were (a) 95 percent using MODIS 250 m, (b) 93 percent using MODIS 500 m, and (c) 86 percent using AVHRR 10,000 m. In this study, it was found that the precise location of the irrigated areas were better established using finer spatial resolution data. A strong relationship (R 2 0.74 to 0.95) was observed between irrigated areas determined using various resolutions. This study proved the hypotheses that “the finer the spatial resolution of the sensor used, greater was the irrigated area derived,” since at finer spatial resolutions, fragmented areas are detected better. Accuracies and errors were established consistently for three classes (surface water irrigated, ground water/conjunctive use irrigated, and nonirrigated) across the four resolutions mentioned above. The results showed that the Landsat data provided significantly higher overall accuracies (84 percent) when compared to MODIS 500 m (77 percent), MODIS 250 m (79 percent), and AVHRR 10,000 m (63 percent).

Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data

Abstract: Maps of irrigated areas are essential for Ghana’s agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m

Sub-pixel Area Calculation Methods for Estimating Irrigated Areas

The goal of this paper was to develop and demonstrate practical methods for computing sub-pixel areas (SPAs) from coarse-resolution satellite sensor data. The methods were tested and verified using: (a) global irrigated area map (GIAM) at 10-km resolution based, primarily, on AVHRR data, and (b) irrigated area map for India at 500-m based, primarily, on MODIS data. The sub-pixel irrigated areas (SPIAs) from coarse-resolution satellite sensor data were estimated by multiplying the full pixel irrigated areas (FPIAs) with irrigated area fractions (IAFs). Three methods were presented for IAF computation: (a) Google Earth Estimate (IAF-GEE); (b) High resolution imagery (IAF-HRI); and (c) Sub-pixel de-composition technique (IAF-SPDT). The IAF-GEE involved the use of “zoom-in-views” of sub-meter to 4-meter very high resolution imagery (VHRI) from Google Earth and helped determine total area available for irrigation (TAAI) or net irrigated areas that does not consider intensity or seasonality of irrigation. The IAF-HRI is a well known method that uses finer-resolution data to determine SPAs of the coarser-resolution imagery. The IAF-SPDT is a unique and innovative method wherein SPAs are determined based on the precise location of every pixel of a class in 2-dimensional brightness-greenness-wetness (BGW) feature-space plot of red band versus near-infrared band spectral reflectivity. The SPIAs computed using IAF-SPDT for the GIAM was within 2 % of the SPIA computed using well known IAF-HRI. Further the fractions from the 2 methods were significantly correlated. The IAF-HRI and IAF-SPDT help to determine annualized or gross irrigated areas (AIA) that does consider intensity or seasonality (e.g., sum of areas from season 1, season 2, and continuous year-round crops). The national census based irrigated areas for the top 40 irrigated nations (which covers about 90% of global irrigation) was significantly better related (and had lesser uncertainties and errors) when compared to SPIAs than FPIAs derived using 10-km and 500-m data. The SPIAs were closer to actual areas whereas FPIAs grossly over-estimate areas. The research clearly demonstrated the value and the importance of sub-pixel areas as opposed to full pixel areas and presented 3 innovative methods for computing the same.

Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security, determined using MODIS 250 m time-series data and spectral matching techniques, in the Krishna River basin (India)

The objective of this study was to investigate the changes in cropland areas as a result of water availability using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series data and spectral matching techniques (SMTs). The study was conducted in the Krishna River basin in India, a very large river basin with an area of 265 752 km2 (26 575 200 ha), comparing a water-surplus year (2000–2001) and a water-deficit year (2002–2003). The MODIS 250 m time-series data and SMTs were found ideal for agricultural cropland change detection over large areas and provided fuzzy classification accuracies of 61–100% for various land‐use classes and 61–81% for the rain-fed and irrigated classes. The most mixing change occurred between rain-fed cropland areas and informally irrigated (e.g. groundwater and small reservoir) areas. Hence separation of these two classes was the most difficult. The MODIS 250 m-derived irrigated cropland areas for the districts were highly correlated with the Indian Bureau of Statistics data, with R 2-values between 0.82 and 0.86. The change in the net area irrigated was modest, with an irrigated area of 8 669 881 ha during the water-surplus year, as compared with 7 718 900 ha during the water-deficit year. However, this is quite misleading as most of the major changes occurred in cropping intensity, such as changing from higher intensity to lower intensity (e.g. from double crop to single crop). The changes in cropping intensity of the agricultural cropland areas that took place in the water-deficit year (2002–2003) when compared with the water-surplus year (2000–2001) in the Krishna basin were: (a) 1 078 564 ha changed from double crop to single crop, (b) 1 461 177 ha changed from continuous crop to single crop, (c) 704 172 ha changed from irrigated single crop to fallow and (d) 1 314 522 ha changed from minor irrigation (e.g. tanks, small reservoirs) to rain-fed. These are highly significant changes that will have strong impact on food security. Such changes may be expected all over the world in a changing climate.

Water Productivity Mapping (WPM) Using Landsat ETM+ Data for the Irrigated Croplands of the Syrdarya River Basin in Central Asia

The overarching goal of this paper was to espouse methods and protocols for water productivity mapping (WPM) using high spatial resolution Landsat remote sensing data. In a world where land and water for agriculture are becoming increasingly scarce, growing “more crop per drop” (increasing water productivity) becomes crucial for food security of future generations. The study used time-series Landsat ETM+ data to produce WPMs of irrigated crops, with emphasis on cotton in the Galaba study area in the Syrdarya river basin of Central Asia. The WPM methods and protocols using remote sensing data consisted of: (1) crop productivity (ton/ha) maps (CPMs) involving crop type classification, crop yield and biophysical modeling, and extrapolating yield models to larger areas using remotely sensed data; (2) crop water use (m3/ha) maps (WUMs) (or actual seasonal evapotranspiration or actual ET) developed through Simplified Surface Energy Balance (SSEB) model; and (3) water productivity (kg/m3) maps (WPMs) produced by dividing raster layers of CPMs by WUMs. The SSEB model calculated WUMs (actual ET) by multiplying the ET fraction by reference ET. The ET fraction was determined using Landsat thermal imagery by selecting the “hot” pixels (zero ET) and “cold” pixels (maximum ET). The grass reference ET was calculated by FAO Penman-Monteith method using meteorological data. The WPMs for the Galaba study area demonstrated a wide variations (0-0.54 kg/m3) in water productivity of cotton fields with overwhelming proportion (87%) of the area having WP less than 0.30 kg/m3, 11% of the area having WP in range of 0.30-0.36 kg/m3, and only 2% of the area with WP greater than 0.36 kg/m3. These results clearly imply that there are opportunities for significant WP increases in overwhelming proportion of the existing croplands. The areas of low WP are spatially pin-pointed and can be used as focus for WP improvements through better land and water management practices.

A global map of rainfed cropland areas at the end of last millennium using remote sensing and geospatial techniques

Rainfed agriculture plays a critical role in most part of the tropics and subtropics of the world. Eighty percent of the agricultural land worldwide is under rainfed agriculture; and significant proportion of rural economy still depends on rainfed agriculture with characteristically low yield levels. In this context the International Water Management Institute (IWMI) produced the first satellite sensor based Global map of rainfed cropland areas at 10Km resolution (GMRCA10Km). The study used a mega-file of 159 global data layers involving the AVHRR and SPOT time-series, GTOPO30 DEM, mean monthly rainfall, and forest cover. A suite of innovative techniques were developed that begins with the image segmentation, quantitative spectral matching techniques (SMTs) and spectral correlation similarity (SCS R2). The SCS was found to be the most useful technique in grouping identical classes. Mixed classes were resolved using a decision trees, time series plots, and principal component analysis algorithms. A wide array of groundtruth data, and high-resolution images were used to identify and label classes. The outcome was the GMRCA10Km estimated to be 1.75 billion hectares for the main cropping period. The sub-pixel areas (SPAs) of GMRCA10Km provide more realistic estimates of the actual area cultivated unlike the full pixel areas (FPAs) often calculated from the raster datasets. Three distinct GMRCA10Km maps have been produced: viz., Aggregated 7-class, Dis-aggregated 18-class and Generic 255-class. The aggregated classes will suffice for broad range of users at global level. The GMRCA10Km product line consists of maps, images, area calculations, snap-shots, class characteristics, and animations.

The spatial data and knowledge gateways at the International Water Management Institute (IWMI)

In this paper we discuss spatial data and knowledge base (SDKB) gateway portals developed by the International Water Management Institute (IWMI). Our vision is to generate and/or facilitate easy and free access to state-of-art SDKB of excellence globally. Our mission is to make SDKB accessible online, globally, for free. The IWMI data storehouse pathway (IWMIDSP; http://www.iwmidsp.org) is a pathfinder global public good (GPG) portal on remote sensing and GIS (RS/GIS) data and products with specific emphasis on river basin data, but also storing valuable data on Nations, Regions, and the World. A number of other specialty GPG portals have also been released. These include Global map of irrigated area (http://www.iwmigiam.org), Drought monitoring system for southwest Asia (http://dms.iwmi.org), Tsunami satellite sensor data catalogue (http://tsdc.iwmi.org), and Knowledge base system (KBS) for Sri Lanka (http://www.iwmikbs.org). The IWMIDSP has been the backbone of several other projects such as global irrigated area mapping, drought monitoring system, wetlands, and knowledge base systems. A discussion on these pathfinder web portals follow.