Delineation and characterization of waterlogging and salt affected areas in a canal irrigated semiarid region of north west India

Indian Remote Sensing (IRS) Linear Imaging Self Scanning (LISS II) data are interpreted, followed by ground verification facilitated identification of waterlogged areas (ponded water), salt affected soils (salt efflorescence) and high water table zones (potential waterlogging zones) in the Indira Gandhi Nahar Pariyojona (IGNP) command area (India). The false colour composites (bands 4, 3, 2) for February 1996, November 1996 and June 1998 on 1:50 000 scale revealed occurrence and seasonal dynamics of permanent waterlogging in low-lying flats and depressions. The extent of waterlogging was higher in February 1996 due to less evaporation and more agricultural operation during the period. Salt accumulation was higher in November 1996 due to freshly precipitated seasonal salts. Seepage and accumulation of excess irrigation water through coarse sandy mass was primarily responsible for the development of waterlogging in the irrigated zone. The capillary rise of soluble salts with a rising water table and high evaporative demand caused secondary soil salinization. A ground truth study found areas with a high water table (<1.5 m) with patchy crop stands and a potentially sensitive zone with a fluctuating (1.5–6.0 m) water table with poor vegetative growth. The soil characteristics showed moderate to high soil salinity in the control section of soil profiles. These were characterized by medium to coarse texture, weak to moderately strong structure, weak consistency, low organic matter content and the presence of abundant CaCO₃ nodules. The composition of saturated soil paste showed a preponderance of chlorides and sulphates of sodium, calcium and magnesium. The presence of fine texture and calcium carbonate layers at a depth below the surface caused the development of a perched water table indicating unsuitability for traditional irrigated agriculture. The quality of pond water was extremely poor and unfit for reuse. The ground water was saline in some areas but normally lies within the prescribed limit. The quality of drainage water was poor in saline depressions and unsuitable for reuse but moderate in other areas suggesting its safe reuse when mixed with good quality water. Suitable soil and water management practices were necessary for sustainable crop production in the command area.     

Geoinformatics as a tool for the assessment of the impact of ground water quality for irrigation on soil health

Secondary salinisation is the most harmful and extended phenomenon of the unfavourable effects of irrigation on the soil and environment. An attempt was made to study the impact of poor quality ground water on soils in terms of secondary salinisation and availability of soil nutrients in Faridkot district of Punjab of northern India. Based on physiographic analysis of IRS 1C LISS-III data and semi-detailed soil survey, the soil map was finalized on a 1:50,000 scale and digitized using Arc Info GIS. Georeferenced surface soil samples (0–0.15 m) from 231 sites were collected and analyzed for available phosphorus (P) and potassium (K). Interpolation by kriging produced digital spatial maps of available P and K. Ground water quality map was generated in GIS domain on the basis of EC (electrical conductivity) and RSC (residual sodium carbonate) of ground water samples collected from 374 georeferenced tube wells. Integration of soil and ground water quality maps enabled generating a map showing degree (high, moderate and low) and type (salinity, sodicity and both) of vulnerability to secondary salinization. Fine-textured soils have been found to be highly sensitive to secondary salinisation, whereas medium-textured soils as moderately sensitive to secondary salinisation. The resultant map was integrated with available P and K maps to show the combined influence of soil texture and ground water quality on available soil nutrients. The results show that available P and K in the soils of different physiographic units were found in the order of Ap1 < Ap2 < Ap3. The soils of all physiographic units had sizeable area having high content of P (>22.5 kg / ha) and medium available K (135–335 kg ha⁻¹) in most of the test sites when irrigated with saline, sodic or poor quality water.     

An easy and low-cost method for preprocessing and matching small-scale amateur aerial photography for assessing agricultural land use in Burkina Faso

In recent decades, the Kou watershed in south-western Burkina Faso has suffered from poor water management. Despite the abundance of water, most water users regularly face water shortages because of the increase in the amount of land under irrigation. To help them achieve a more equitable allocation of irrigated land, local stakeholders need an easily managed low-cost tool for monitoring and mapping these irrigated zones. The aim of this study was to develop a fast and low-cost procedure for mosaicing and geo referencing amateur small-scale aerial photographs for land-use surveys. Sets of tens (2009) and hundreds (2007) of low-altitude aerial photographs, with a resolution of 0.4 m and 0.8 m, respectively, were used to create a detailed land-cover map of typical African small-scale irrigated agriculture. A commercially available stitching tool and GIS allowed geo referenced 'mono-images’ to be constructed; both mosaics were warped on a high-resolution SPOT image with a horizontal root mean square error (RMSE) of about 11 m. The RMSE between the two image datasets was 2 m. This approach is less sensitive to atmospheric conditions that are non-predictable in programming satellite imagery.     

FAO 56 Model and Remote Sensing for the Estimation of Crop-Water Requirement in Main Branch Canal of the Bhadra Command area, Karnataka State

Remote sensing and FAO 56 crop water model are used for estimating crop water requirement for paddy crop located in the main branch canal of Bhadra Command Area in Karnataka, India. The estimation of crop-water requirement depends on the meteorological factors, soil type and crop coefficients. The result obtained showed that water requirements of rabi crops higher than those of the kariff crops. The total irrigated area estimated from the IRS image is 29,353 ha. It is found that the total paddy crop acreage is 18,257 ha covering 62 % in the total irrigated area of the command area, Arecanut 20 %, coconut 15 % and sugarcane with other crops 3 %. The water requirement for paddy is 1180.4 mm for its entire growth period. The total water requirement for irrigation supply for crops in the entire command area is 5,790 at a demand of 0.10501 cusecs per ha.     

Assessing gaps in irrigated agricultural productivity through satellite earth observations—A case study of the Fergana Valley,Central Asia

Improving crop area and/or crop yields in agricultural regions is one of the foremost scientific challenges for the next decades. This is especially true in irrigated areas because sustainable intensification of irrigated crop production is virtually the sole means to enhance food supply and contribute to meeting food demands of a growing population. Yet, irrigated crop production worldwide is suffering from soil degradation and salinity, reduced soil fertility, and water scarcity rendering the performance of irrigation schemes often below potential. On the other hand, the scope for improving irrigated agricultural productivity remains obscure also due to the lack of spatial data on agricultural production (e.g. crop acreage and yield). To fill this gap, satellite earth observations and a replicable methodology were used to estimate crop yields at the field level for the period 2010/2014 in the Fergana Valley, Central Asia, to understand the response of agricultural productivity to factors related to the irrigation and drainage infrastructure and environment. The results showed that cropping pattern, i.e. the presence or absence of multi-annual crop rotations, and spatial diversity of crops had the most persistent effects on crop yields across observation years suggesting the need for introducing sustainable cropping systems. On the other hand, areas with a lower crop diversity or abundance of crop rotation tended to have lower crop yields, with differences of partly more than one t/ha yield. It is argued that factors related to the infrastructure, for example, the distance of farms to the next settlement or the density of roads, had a persistent effect on crop yield dynamics over time. The improvement potential of cotton and wheat yields were estimated at 5%, compared to crop yields of farms in the direct vicinity of settlements or roads. In this study it is highlighted how remotely sensed estimates of crop production in combination with geospatial technologies provide a unique perspective that, when combined with field surveys, can support planners to identify management priorities for improving regional production and/or reducing environmental impacts.     

Detection of sub-surface waterlogging using Terra-1 MODIS data

Waterlogging due to rising ground watertable, being a sub-surface phenomenon, is not amenable to detection by optical remote sensing. Microwave and thermal sensor data have, however, shown some promise in the detection of sub-surface waterlogging. The present study was taken up to evaluate the potential of near-IR, short-wave IR (SWIR) and thermal-IR data from Moderate Resolution Imaging Spectrometer (MODIS) aboard Terra-1 acquired during day-and-night time postmonsoon data for detection of sub-surface waterlogging. The approach involves retrieval of day-and-night land surface temperature (LST), generation of normalized difference of channel-2 and 6 (ND₂₆); 2 and 7 (ND₂₇); ground truth collection involving concurrent ground water table observations to satellite date of pass, thresholding of normalized differences (NDs) and correlating the NDs with depth of ground water table. Amongst various spectral indices, day and night-time LST difference (DLST) and night-time LST have been found to correlate well with the incidence of waterlogging (water table depth < 2m), followed by normalized difference of band-2 (841–876 nm) and band-7 (2105–2155 nm). The sensitivity of threshold limits for these indices was maximum for DLST followed by ND₂₆ and ND₂₇. Poor accuracy of detecting sub-surface waterlogging with thermal bands during day time is attributed to the non-corresponding of the time of Terra MODIS data acquisitions with thermal maxima of the terrain. Though the ND₂₇ gave better accuracy to detect subsurface waterlogging, it is very sensitive to threshold limits.     

Assessment of surface and subsurface waterlogging,water level fluctuations,and lithological variations for evaluating groundwater resources in Ganga Plains

Abstract

In the present study, the multi-temporal satellite images of IRS P6 LISS III were used to map waterlogging dynamics over different seasons. An area of 594.36 km² (6.75%) and 4.17 km² (0.04%) was affected by surface waterlogging during pre and postmonsoon season, respectively. The average annual groundwater level fluctuations were calculated using 18 years (1990–2007) pre and postmonsoon groundwater level data to identify the areas which are under groundwater induced waterlogging conditions. The soil map clearly indicates that salinity and sodicity exhibit the highest severity and occur in areas with shallow groundwater levels. The hydrogeomorphical units mapped using IRS P6 LISS III satellite images are flood plain, alluvial plain, paleochannels, and oxbow lakes. The study revealed that 44.65% areas have very good to excellent groundwater resources. The litholog data clearly indicate an alternating sequence of clay and sand in which deep aquifers made up of coarse sand would be best suited for adequate water supply and good groundwater quality. The integrated study utilizing digital spatial data pertaining to waterlogging, soil salinity, water level fluctuation, and lithological variation proved that planning of any surface and subsurface water resources development activity should be taken up after assessments of said parameters.     

Assessing NDVI Spatial Pattern as Related to Irrigation and Soil Salinity Management in Al-Hassa Oasis,Saudi Arabia

Sustainability of irrigated agriculture in arid and semi arid lands depends, mainly on the level of soil salinity and the quality of irrigation water. Remotely sensed data can provide information about the extent of vegetated irrigated areas. Al-Hassa oasis, Saudi Arabia is probably the largest oasis in the world depends mostly on tapped ground water to irrigate mainly date palm groves for its economic survival. This study tried to investigate the extent of soil salinity and the quality of irrigation water and the relationship with vegetation growth, employing NDVI derived from Landsat satellite imagery.     

Resource constraint mapping and management in arid tract of Punjab

The arid tract of Punjab experiences various problems like thick sand cover (sand dunes) in large area, poor retention of water and nutrients in coarse textured soils, soil salinity and/or alkalinity, water logging and poor ground water quality. In the present study multidate remotely sensed data both in the form of aerial photographs and satellite imagery on 1:50,000 scale were interpreted visually to map physiography and soils. The ground water samples from tubewells distributed all over the area were collected and analysed to prepare ground water quality map. The soil and ground water quality maps were integrated to produce a resource constraint map of the area showing physical, chemical and hydrological constraints. The study revealed that alluvial plain suffers from hydrological constraints due to marginal to.poor ground water in 86% of the total area. The sand dunes show both physical and hydrological constraints due to coarse textured (sandy) soils and brackish ground water. The basins having soil salinity and brackish ground water cover 0.10% of the area. Keeping in view the type of constraint, locale specific measures like levelling and stabilisation of sand dunes, reclamation of salt affected and water logged areas followed by plantation of tree species which act as biopumps are suggested. The conjuctive use of surface (canal) and ground water is essential to prevent secondary salinization and sodification. The study demonstrates the potential usefulness of remote sensing technology in mapping natural resources and assess the nature, magnitude and spatial distribution of resource constraints.     

Methane Emission Pattern of Indian Rice-Ecosystems

The rice land is linked to the climate change due to its methane emission potential. The systems of growing rice and associated soil and crop management practices that have evolved are varied and complex. However, from the methane emission point of view, water regime is a crucial parameter. According to IPCC guidelines the rice ecosystem need to be categorized into four strata for methane emission study. The remote sensing based stratification map previously developed was used for in-situ weekly/monthly measurements of methane emission from the representative ecosystems, samples were collected and analysed using gas chromatography following the IPCC standards for three consecutive years; 2003, 2004 and 2005. This paper highlights the results of methane emission measurement and pattern from rice lands of India based on in-situ measurements. The CH₄ emission pattern of irrigated crop in dry season showed a steady increase in the beginning which peaks during flowering stage, decreasing gradually thereafter. The results were consistent for different varieties and across the years. The emission pattern of irrigated wet season crop showed two peaks. The emission pattern also showed the influence of crop variety as well as year (of observation). The mean emission coefficient derived from all categories and all samples (n = 471) weighted for the Indian rice crop was 74.05 + 43.28 kg/ha.     

Impact of cropping pattern changes on the exploitation of water resources: A remote sensing and GIS approach

A remote sensing-based approach was applied to study the impact of changes in cropping system on the exploitation of water resources in two districts namely Ludhiana in central Punjab and Muktsar in South-Western Punjab. Rice-wheat remained dominant rotation in Ludhiana while cottonwheat rotation was replaced partially by rice-wheat in Muktsar within a span of over five years (1998–99 to 2003–04). The solo rice-wheat system in Ludhiana district has resulted in large-scale ground water exploitation as is evident from the faster decline in water table (up to 0.9m year⁻¹) and higher tube-wells density (440 per 1000 ha). As a result, nearly 60 per cent of the total area of Ludhiana district has the water table depth greater than 10m and in some blocks, it has reached to a depth of 22 m. In Muktsar district, corresponding rise in water table is 0.2m per year and tube well density is 114 per 1000 ha. Irrigation water associated with paddy cultivation in Ludhiana and adjoining areas moves laterally through the buried paleo-channels of Sutlaj in the deeper soil profile and gets accumulated in the basin lands of Muktsar and adjoining areas and causes an extra water flux and subsequent rise in water table, recorded at 3 to 7m depth. To minimize the hydrological imbalance of the state, it is suggested to diversify some of the area in the central districts from irrigation water intensive rice-wheat system to less water intensive cropping system.     

Evaluation of multisensor data for delineating salt affected soils in arid rajasthan

The Landsat (MSS and TM), SPOT (PLA and MLA) and IRS (LISS-I and LISS-II) images of crop free period (April, May), rainfed crop (October) and rabi irrigated crop (January, February) have been evaluated for their capabilities of mapping (1) primary salt affected soils: (slightly, moderately and severely) (2) saline water irrigated saline soils, (3) sodic water irrigated sodic soils and (4) salt affected soils due to tank seepage in the arid region of Rajasthan. The moderately and severe salt affected soils could be mapped with Landsat, (IRS LISS-I) and SPOT, images of any season. However, the summer season imagery provided maximum extent of salt affected soils. The LISS-II imagery also provided delineation of slightly salt affected soils in addition to the moderate and severely salt affected soils. The delineation of saline and sodic water irrigated areas was possible by using Landsat False Colour Composite for the January month by their characteristic reflectance, existing cropping pattern and the quality of irrigation water being used in the area. The IRS (LISS-II) and SPOT PLA images for the May month were also used for mapping of saline and sodic water irrigated soils.     

Satellite remote sensing derived spatial water utilisation index (wui) for benchmarking of irrigation systems

Water Utilisation Index (WUI) defined as area irrigated per unit volume is a measure of water delivery performance and constitutes one of the important spatial performance indicators of an irrigation system. WUI also forms basis for evaluating the adequacy of seasonal irrigation supplies in an irrigation system (inverse of WUI is delta, i.e. depth of water supplied to a given irrigation unit). In the present study WUI and adequacy indicators were used in benchmarking the performance of Nagarjunasagar Left Canal Command (NSLC) in Andhra Pradesh. Optimised temporal satellite data of rabi season during the years 1990–91 and 1998–99 was used in deriving irrigated crop areas adopting hierarchical classification approach. Paddy is the predominant crop grown and cotton, chillies, sugarcane etc. are the other crops grown in the study area. Equivalent wet area (paddy crop area) was estimated using the operationally used project specific conversion factors. WUI was estimated at disaggregated level viz., distributary, irrigation block, irrigation zone level using the canal discharge data. At project level, WUI estimated to be 65 ha/MCM and 92 ha/MCM during rabi season of 1990–91 and 1998–99 years respectively. A comparison of total irrigated area and discharges corresponding to both the years indicate that irrigation service is extensive and sub optimal during 1998–99 and it is intensive and optimal in 1990–91. It was also observed that WUI is lesser in blocks of with higher Culturable Command Area (CCA) compared to the blocks of lower CCA. All the disaggregated units were ranked into various groups of different levels of water distribution performance. The study demonstrates the utility of WUI as spatial performance indicator and thus useful for benchmarking studies of irrigation command areas. The WUI together with satellite data derived spatial irrigation intensity, crop productivity constitutes important benchmarking indices in irrigation command areas.     

Evaluation of NDWI and MNDWI for assessment of waterlogging by integrating digital elevation model and groundwater level

Accurate information on the extent of waterlogging is required for flood prediction, monitoring, relief and preventive measures. The rule-based classification algorithms were used for differentiating waterlogged areas from other ground features using Resourcesat-2 AWiFS satellite imagery (Indian Remote Sensing Satellite with spatial resolution of 56 m). Two spectral indices normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were used for extracting waterlogged areas in Sri Muktsar Sahib district of Punjab, India. These indices extracted the waterlogged areas (cropped areas inundated with water) but the water features were less enhanced in the NDWI-derived image (when compared with MNDWI-derived image) due to negative values of NDWI and, mixing of water with built up features. The water features were more enhanced with MNDWI and the values of MNDWI were positive for water features mixed with vegetation. The overall accuracy of waterlogged areas extracted from the MNDWI image was 96.9% with the Kappa coefficient of 0.89. The digital elevation model (DEM) was extracted from ASTER-GDEM. The relationships among depth to the water table recorded before the incessant rain in the region, DEM and classified MNDWI images explained the differences in the extent of waterlogging in various directions of the study area. These results suggest that MNDWI can be used to better delineate water features mixed with vegetation compared to NDWI.     

Global rain-fed,irrigated, and paddy croplands: A new high resolution map derived from remote sensing,crop inventories and climate data

Irrigation accounts for 70% of global water use by humans and 33–40% of global food production comes from irrigated croplands. Accurate and timely information related to global irrigation is therefore needed to manage increasingly scarce water resources and to improve food security in the face of yield gaps, climate change and extreme events such as droughts, floods, and heat waves. Unfortunately, this information is not available for many regions of the world. This study aims to improve characterization of global rain-fed, irrigated and paddy croplands by integrating information from national and sub-national surveys, remote sensing, and gridded climate data sets. To achieve this goal, we used supervised classification of remote sensing, climate, and agricultural inventory data to generate a global map of irrigated, rain-fed, and paddy croplands. We estimate that 314 million hectares (Mha) worldwide were irrigated circa 2005. This includes 66 Mha of irrigated paddy cropland and 249 Mha of irrigated non-paddy cropland. Additionally, we estimate that 1047 Mha of cropland are managed under rain-fed conditions, including 63 Mha of rain-fed paddy cropland and 985 Mha of rain-fed non-paddy cropland. More generally, our results show that global mapping of irrigated, rain-fed, and paddy croplands is possible by combining information from multiple data sources. However, regions with rapidly changing irrigation or complex mixtures of irrigated and non-irrigated crops present significant challenges and require more and better data to support high quality mapping of irrigation.     

The application of remote sensing data to diagnose soil degradation in the Dakhla depression – Western Desert,Egypt

Dakhla depression in Egypt’s Western Desert is experiencing two soil degradation processes, notably: soil salinization and sand encroachment. The present study aimed to diagnose the severity of these processes using remote sensing. Soil salinity was determined by spectral regression analysis between tasselled cap spectral transform extracted from a Landsat-8 image acquired in September 2013 along with synchronized soil salinity measurements. Assessment of sand advance rate was conducted by temporal change detection of brilliant crescentic sand dune visualized by Google Earth in old (2002) and recent (2013) images. Results showed that salinized soils (dS/m4<) represent 91% of bare lands and salinization is attributed to aridity, topography and poor drainage. Barchan dunes north and south of Abu Tartur escarpment moved at rates of 5.9 and 3.6 m/year, respectively. The escarpment protected the majority of the depression from massive dune invasion. However, sand encroachment is clearly observed west of the depression.     

Retrieving soil moisture in rainfed and irrigated fields using Sentinel-2 observations and a modified OPTRAM approach

Surface soil water content plays an important role in driving the exchange of latent and sensible heat between the atmosphere and land surface through transpiration and evaporation processes, regulating key physiological processes affecting plants growth. Given the high impact of water scarcity on yields, and of irrigated agriculture on the overall withdrawal rate of freshwater, it is important to define models that help to improve water resources management for agricultural purposes, and to optimize rainfed crop yield. Recent advances in satellite-based remote sensing have led to valuable solutions to estimate soil water content based on microwave or optical/thermal-infrared data. This study aims at improving soil water content estimation at high spatial and temporal resolution, by means of the Optical Trapezoid Model (OPTRAM) driven by Copernicus Sentinel-2 data. Two different model variations were considered, based on linear and nonlinear parameters constraints, and validated against in situ soil water content measurements made with time domain reflectometry (TDR) on irrigated maize in central Italy and on rainfed maize and pasture in northern Italy. For the first site the non-linear model shows a better correlation between measured and estimated soil water content values (r = 0.80) compared to the linear model (r = 0.73). In both cases the modeled soil moisture tends to overestimate the measured values at medium to high water content level, while both models underestimate soil moisture at low water content level. Estimated versus measured normalized surface soil water for rainfed pasture plots from nonlinear OPTRAM parametrized based on irrigated maize parameterization (SIM1), and site-specific parametrization for rainfed pasture (SIM2), indicate that both models (SIM1 and SIM2) are comparable for rotational grazing pasture (RMSEsim1 = 0.0581 vs. RMSEsim2 = 0.0485 cm3 cm-3) and the continuous grazing pasture (RMSEsim1 = 0.0485 vs. RMSEsim2 = 0.0602 cm3 cm-3), while for the rainfed maize plots SIM1 shows lower RMSE (average for all plots RMSE = 0.0542 cm3 cm-3) compared to the site-specific calibration model (SIM2 – average for all plots RMSE = 0.0645 cm3 cm-3). Finally, OPTRAM estimations are close to in situ measurement values while Surface Soil Moisture at 1 km (SSM1 km) tends to underestimate the measurements during maize crop growing season. Soil moisture retrieval from high-resolution Sentinel-2 optical images allows water stress conditions to be effectively mapped, supporting decision making in irrigation scheduling and other crop management.     

An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level

Soil salinity often leads to reduced crop yield and quality and can render soils barren. Irrigated areas are particularly at risk due to intensive cultivation and secondary salinization caused by waterlogging. Regular monitoring of salt accumulation in irrigation schemes is needed to keep its negative effects under control. The dynamic spatial and temporal characteristics of remote sensing can provide a cost-effective solution for monitoring salt accumulation at irrigation scheme level. This study evaluated a range of pan-fused SPOT-5 derived features (spectral bands, vegetation indices, image textures and image transformations) for classifying salt-affected areas in two distinctly different irrigation schemes in South Africa, namely Vaalharts and Breede River. The relationship between the input features and electro conductivity measurements were investigated using regression modelling (stepwise linear regression, partial least squares regression, curve fit regression modelling) and supervised classification (maximum likelihood, nearest neighbour, decision tree analysis, support vector machine and random forests). Classification and regression trees and random forest were used to select the most important features for differentiating salt-affected and unaffected areas. The results showed that the regression analyses produced weak models (<0.4 R squared). Better results were achieved using the supervised classifiers, but the algorithms tend to over-estimate salt-affected areas. A key finding was that none of the feature sets or classification algorithms stood out as being superior for monitoring salt accumulation at irrigation scheme level. This was attributed to the large variations in the spectral responses of different crops types at different growing stages, coupled with their individual tolerances to saline conditions.     

Major forest changes and land cover transitions based on plant functional types derived from the ESA CCI Land Cover product

Land use and land cover change are of prime concern due to their impacts on CO₂ emissions, climate change and ecological services. New global land cover products at 300 m resolution from the European Space Agency (ESA) Climate Change Initiative Land Cover (CCI LC) project for epochs centered around 2000, 2005 and 2010 were analyzed to investigate forest area change and land cover transitions. Plant functional types (PFTs) fractions were derived from these land cover products according to a conversion table. The gross global forest loss between 2000 and 2010 is 172,171 km², accounting for 0.6% of the global forest area in year 2000. The forest changes are mainly distributed in tropical areas such as Brazil and Indonesia. Forest gains were only observed between 2005 and 2010 with a global area of 9844 km², mostly from crops in Southeast Asia and South America. The predominant PFT transition is deforestation from forest to crop, accounting for four-fifths of the total increase of cropland area between 2000 and 2010. The transitions from forest to bare soil, shrub, and grass also contributed strongly to the total areal change in PFTs. Different PFT transition matrices and composition patterns were found in different regions. The highest fractions of forest to bare soil transitions were found in the United States and Canada, reflecting forest management practices. Most of the degradation from grassland and shrubland to bare soil occurred in boreal regions. The areal percentage of forest loss and land cover transitions generally decreased from 2000–2005 to 2005–2010. Different data sources and uncertainty in the conversion factors (converting from original LC classes to PFTs) contribute to the discrepancy in the values of change in absolute forest area.     

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

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.