Manohar Velpuri

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.

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.