Murali Krishna Gumma

Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India

Diverse irrigated areas were mapped in the Krishna River Basin (258,912 km2), southern India, using an irrigated fraction approach and multiple ancillary data sources. Unsupervised classification of a monthly time series of net difference vegetation index (NDVI) images from the Moderate Resolution Imaging Spectrometer (MODIS) over January–December 2002 generated 40 classes. Nine generalized classes included five irrigated classes with distinct NDVI time signatures: continuous irrigation, double‐cropped, irrigated with low biomass, minor irrigation, and groundwater irrigation. Areas irrigated by surface water began greening 45 days after groundwater‐irrigated areas, which allowed separation of surface and groundwater irrigation in the classification. The fraction of each class area irrigated was determined using three different methods: ground truth data, a linear regression model calibrated to agricultural census data, and visual interpretation of Landsat TM imagery. Irrigated fractions determined by the three methods varied least for the double‐cropped irrigated class (0.62–0.79) and rangeland (0.00–0.02), and most for the minor irrigated class (0.06–0.43). Small irrigated patches (<0.1 km2) accounted for more irrigated area than all major surface water irrigated areas combined. The irrigated fractions of the minor and groundwater‐irrigated classes differed widely by method, suggesting that mapping patchy and small irrigated areas remains challenging, but comparison of multiple data sources improves confidence in the classification and highlights areas requiring more intensive fieldwork.

Mapping rice areas of South Asia using MODIS multitemporal data

Our goal is to map the rice areas of six South Asian countries using moderate-resolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the worlds harvested rice area and is also home to 74% of the population that lives on less than

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

2.00 a day. The population of the region is growing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R2 values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas.

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

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.

Sub-pixel Area Calculation Methods for Estimating Irrigated Areas

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

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 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.

Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data

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.

Mapping Asian Cropping Intensity With MODIS

ABSTRACT The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia, using MODIS 250 m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period. Rice-fallow cropland areas are those areas where rice is grown during the kharif growing season (June–October), followed by a fallow during the rabi season (November–February). These cropland areas are not suitable for growing rabi-season rice due to their high water needs, but are suitable for a short -season (≤3 months), low water-consuming grain legumes such as chickpea (Cicer arietinum L.), black gram, green gram, and lentils. Intensification (double-cropping) in this manner can improve smallholder farmer’s incomes and soil health via rich nitrogen-fixation legume crops as well as address food security challenges of ballooning populations without having to expand croplands. Several grain legumes, primarily chickpea, are increasingly grown across Asia as a source of income for smallholder farmers and at the same time providing rich and cheap source of protein that can improve the nutritional quality of diets in the region. The suitability of rainfed and irrigated rice-fallow croplands for grain legume cultivation across South Asia were defined by these identifiers: (a) rice crop is grown during the primary (kharif) crop growing season or during the north-west monsoon season (June–October); (b) same croplands are left fallow during the second (rabi) season or during the south-east monsoon season (November–February); and (c) ability to support low water-consuming, short-growing season (≤3 months) grain legumes (chickpea, black gram, green gram, and lentils) during rabi season. Existing irrigated or rainfed crops such as rice or wheat that were grown during kharif were not considered suitable for growing during the rabi season, because the moisture/water demand of these crops is too high. The study established cropland classes based on the every 16-day 250 m normalized difference vegetation index (NDVI) time series for one year (June 2010–May 2011) of Moderate Resolution Imaging Spectroradiometer (MODIS) data, using spectral matching techniques (SMTs), and extensive field knowledge. Map accuracy was evaluated based on independent ground survey data as well as compared with available sub-national level statistics. The producers’ and users’ accuracies of the cropland fallow classes were between 75% and 82%. The overall accuracy and the kappa coefficient estimated for rice classes were 82% and 0.79, respectively. The analysis estimated approximately 22.3 Mha of suitable rice-fallow areas in South Asia, with 88.3% in India, 0.5% in Pakistan, 1.1% in Sri Lanka, 8.7% in Bangladesh, 1.4% in Nepal, and 0.02% in Bhutan. Decision-makers can target these areas for sustainable intensification of short-duration grain legumes.

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

Agricultural systems are geographically extensive, have profound significance to society, and affect regional energy, climate, and water cycles. Since most suitable lands worldwide have been cultivated, there is a growing pressure to increase yields on existing agricultural lands. In tropical and subtropical regions, multicropping is widely used to increase food production, but regional-to-global information related to multicropping practices is poor. The high temporal resolution and moderate spatial resolution of the MODIS sensors provide an ideal source of information for characterizing cropping practices over large areas. Relative to studies that document agricultural extensification, however, systematic assessment of agricultural intensification via multicropping has received relatively little attention. The goal of this work was to help close this information gap by developing methods that use multitemporal remote sensing to map multicropping systems in Asia. Image time-series analysis is especially challenging in this part of the world because atmospheric conditions including clouds and aerosols lead to high frequencies of missing or low-quality observations, especially during the Asian Monsoon. The methodology that we developed builds upon the algorithm used to produce the MODIS Land Cover Dynamics product (MCD12Q2), but uses an improved methodology optimized for crops. We assessed our results at the aggregate scale using state, district, and provincial level inventory statistics reporting total cropped and harvested areas, and at the field scale using survey results for 191 field sites in Bangladesh. While the algorithm highlighted the dominant continental-scale patterns in agricultural practices throughout Asia, and produced reasonable estimates of state and provincial level total harvested areas, field-scale assessment revealed significant challenges in mapping high cropping intensity due to abundant missing data.

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

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.