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.     

Global irrigated area map (GIAM), derived from remote sensing,for the end of the last millennium

A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3‐band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time‐series for 1997–1999, (b) Système pour l'Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50 km monthly time series for 1961–2000, (d) Global 30 Arc‐Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite‐1 Synthetic Aperture Radar (JERS‐1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992–1993. A single mega‐file data‐cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re‐sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments.

Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space–time spiral curve (ST‐SC) plots, (b) brightness–greenness–wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very‐high‐resolution imagery (VHRI) ‘zoom‐in views’ in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high‐resolution Landsat‐ETM+ Geocover 150 m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re‐classify the MDFC, and the class identification and labelling protocol repeated. The sub‐pixel area (SPA) calculations were performed by multiplying full‐pixel areas (FPAs) with irrigated area fractions (IAFs) for every class.

A 28 class GIAM was produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year‐round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but ‘equipped for irrigation’ at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467 million hectares (Mha), which is sum of the non‐overlapping areas of: (a) 252 Mha from season one, (b) 174 Mha from season two and (c) 41 Mha from continuous year‐round crops. The TAAI at the end of the last millennium was 399 Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (370 Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79–91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org).     

Land-use land-cover classification analysis of Giba catchment using hyper temporal MODIS NDVI satellite images

Landsat-based land-use land-cover (LULC) mapping studies were previously conducted in Giba catchment, comprising an area of 4019 km². No attempt has been done to map LULC of this catchment through the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time-series data. This article is aimed to see whether time-series MODIS NDVI data set is applicable for LULC mapping of Giba catchment or not. MODIS NDVI data sets of the year 2010 were used for classification analysis. The original data were subjected to MODIS Reproduction Tool and stacking. The re-projected and stacked images were filtered using Harmonic Analysis of Time-Series filtering algorism to remove the effects of cloud and other noises. The MODIS NDVI data sets (16-day maximum value composite) were classified using the ISODATA clustering algorithm available under ERDAS IMAGINE software. A series of unsupervised classification runs were carried out with a pre-defined number of classes (5–24). From this classification, the optimal numbers of classes were determined to be eight after checking for average divergence analysis. The classification result became eight LULC classes namely: bare land, grass land, irrigated land, cultivated land, area closure, shrub land, bush land, and forest land with an overall accuracy of 87.7%. It was therefore concluded that MODIS NDVI time-series image is applicable for mapping large watersheds.     

Crop and land cover classification in Iran using Landsat 7 imagery

Remote sensing provides one way of obtaining more accurate information on total cropped area and crop types in irrigated areas. The technique is particularly well suited to arid and semi‐arid areas where almost all vegetative growth is associated with irrigation. In order to obtain more information with regard to crop patterns in the irrigated areas in the Zayandeh Rud basin, a classification analysis was made of the Landsat 7 image of 2 July 2000. The target of the classification was to primarily focus on the agricultural land use. The date of the image fell in the transition period where the first crops were harvested and many fields were being prepared for the second crop. The image has therefore captured an instantaneous picture of a system generally in transition from the first to the second crop, but with significant differences from system to system, both with respect to crop types and agricultural cycles. The overall accuracy of image registration was about 30 m (one pixel). Fieldwork was conducted on various occasions in August–October 2000 and May–October 2001. Farmers were interviewed to determine the situation on 2 July 2000. Fields were mapped in detail with the GPS instruments, and data compiled for 112 fields. Using a supervised classification system, training areas were selected and initial classifications were made to determine the validity of the classes. After merging several classes and testing several new classes a final classification system was made. All seven Landsat bands were used in the determination of the feature statistics. The final classification was made with the minimum distance algorithm. The statistics with respect to areas and crop type for the districts was obtained by crossing the raster map with the irrigation district raster map. The results with respect to crop type and total irrigated area per district were compared with those of previous studies. This included both NOAA/AVHRR and conventional agricultural district statistics.     

Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains

Improved and up-to-date land use/land cover (LULC) data sets that classify specific crop types and associated land use practices are needed over intensively cropped regions such as the U.S. Central Great Plains, to support science and policy applications focused on understanding the role and response of the agricultural sector to environmental change issues. The Moderate Resolution Imaging Spectroradiometer (MODIS) holds considerable promise for detailed, large-area crop-related LULC mapping in this region given its global coverage, unique combination of spatial, spectral, and temporal resolutions, and the cost-free status of its data. The objective of this research was to evaluate the applicability of time-series MODIS 250 m normalized difference vegetation index (NDVI) data for large-area crop-related LULC mapping over the U.S. Central Great Plains. A hierarchical crop mapping protocol, which applied a decision tree classifier to multi-temporal NDVI data collected over the growing season, was tested for the state of Kansas. The hierarchical classification approach produced a series of four crop-related LULC maps that progressively classified: 1) crop/non-crop, 2) general crop types (alfalfa, summer crops, winter wheat, and fallow), 3) specific summer crop types (corn, sorghum, and soybeans), and 4) irrigated/non-irrigated crops. A series of quantitative and qualitative assessments were made at the state and sub-state levels to evaluate the overall map quality and highlight areas of misclassification for each map.The series of MODIS NDVI-derived crop maps generally had classification accuracies greater than 80%. Overall accuracies ranged from 94% for the general crop map to 84% for the summer crop map. The state-level crop patterns classified in the maps were consistent with the general cropping patterns across Kansas. The classified crop areas were usually within 1-5% of the USDA reported crop area for most classes. Sub-state comparisons found the areal discrepancies for most classes to be relatively minor throughout the state. In eastern Kansas, some small cropland areas could not be resolved at MODIS' 250 m resolution and led to an underclassification of cropland in the crop/non-crop map, which was propagated to the subsequent crop classifications. Notable regional areal differences in crop area were also found for a few selected crop classes and locations that were related to climate factors (i.e., omission of marginal, dryland cropped areas and the underclassification of irrigated crops in western Kansas), localized precipitation patterns (overclassification of irrigated crops in northeast Kansas), and specific cropping practices (double cropping in southeast Kansas).     

Discrimination of irrigated and rainfed rice in a tropical agricultural system using SPOT VEGETATION NDVI and rainfall data

The classification of irrigated crops by remote sensing requires the use of time series data, since the timing, cropping intensity and duration of cropping is quite variable over the course of a year. Rice is the dominant irrigated crop in tropical and sub‐tropical Asia, where rainfall is high, but is seasonal and often uni‐modal. Existing crop classification methods for rice are not able to distinguish between rainfed and irrigated crops, leading to errors in classification and estimated irrigated area. This paper describes a technique, a ‘peak detector algorithm’, to successfully discriminate between rainfed and irrigated rice crops in Suphanburi province, Thailand. The methodology uses a three‐year time series of Satellite pour l'Observation de la Terre (SPOT) VEGETATION S10 Normalized Difference Vegetation Index (NDVI) data (10 day composites) to identify cropping intensity (number, timing and peak values). Peak NDVI is then lag‐correlated with long term average rainfall data. There is a high correlation at a 40–50 day lag, between a peak rainfall and a ‘single’ peak NDVI of rainfed rice. In irrigated areas, there are multiple peaks, and multiple correlations with low values for at least 90 days after peak rainfall. The methodology currently uses a mask to remove un‐cropped and non‐rice areas, which is derived from existing Geographical Information Systems (GIS). The method achieves a classification accuracy of 89% or better against independent groundtruth data. The procedure is designed as a second level of analysis to refine classifications using other techniques of mapping irrigated area at global and regional scales.     

Analysis of multi-temporal SPOT NDVI images for small-scale land-use mapping

Land-use information is required for a number of purposes such as to address food security issues, to ensure the sustainable use of natural resources and to support decisions regarding food trade and crop insurance. Suitable land-use maps often either do not exist or are not readily available. This article presents a novel method to compile spatial and temporal land-use data sets using multi-temporal remote sensing in combination with existing data sources. Satellite Pour l'Observation de la Terre (SPOT)-Vegetation 10-day composite normalized difference vegetation index (NDVI) images (1998–2002) at 1km² resolution for a part of the Nizamabad district, Andhra Pradesh, India, were linked with available crop calendars and information about cropping patterns. The NDVI images were used to stratify the study area into map units represented by 11 distinct NDVI classes. These were then related to an existing land-cover map compiled from high resolution Indian Remote Sensing (IRS)-images (Liss-III on IRS-1C), reported crop areas by sub-district and practised crop calendar information. This resulted in an improved map containing baseline information on both land cover and land use. It is concluded that each defined NDVI class represents a varying but distinct mix of land-cover classes and that the existing land-cover map consists of too many detailed ‘year-specific’ features. Four groups of the NDVI classes present in agricultural areas match well with four categories of practised crop calendars. Differences within a group of NDVI classes reveal area specific variations in cropping intensities. The remaining groups of NDVI classes represent other land-cover complexes. The method illustrated in this article has the potential to be incorporated into remote sensing and Geographical Information System (GIS)-based drought monitoring systems.     

Fourier analysis of historical NOAA time series data to estimate bimodal agriculture

In the present study, NDVI time‐series 10‐day composites derived from NOAA AVHRR data were used to estimate bimodal agriculture areas (where there are two seasons of cultivation per annum) using Fourier approach. The NDVI sequence was transformed into harmonic signals and the amplitude and phase of first and second harmonics were used for the analysis. A classification was applied, using a decision tree, to discriminate bimodal agriculture area from other land cover types, principally over the Asian sub‐region. When the amplitude of second harmonics in a sample region, where bimodal agriculture is predominant, was compared with the irrigated area statistics developed by FAO‐UF, a linear relationship was determined. The derived function was applied to transform the amplitude of second harmonics to bimodal agriculture area estimates. Thus large‐scale irrigation projects appear on the map and provide an encouraging initial result. This result indicates that estimating bimodal agriculture area that is one of the main sources of information for irrigated area mapping at regional or global scale, with improved accuracy possible if greater spatial, temporal resolution is achieved, for instance from MODIS or SPOT vegetation time series NDVI data, combined with (1) an improved decision tree classification algorithm and (2) a greater precision and geographical distribution of ground‐truth data. The principle merits of this approach are automation and repeatability.     

Satellite imagery and household survey for tracking chickpea adoption in Andhra Pradesh,India

The objective of this study was to map the temporal changes in chickpea cropped area over the last decade in Andhra Pradesh using remote-sensing imagery. Moderate Resolution Imaging Spectroradiometer (MODIS) data composited for every 16 days were used to map the spatial distribution of seasonal crop extent in Andhra Pradesh. MODIS derived 16 day normalized difference vegetation index (NDVI) and maximum value composite (MVC) with seasonal ground survey information for the years 2005–2006 and 2012–2013 were used. A subset of ground survey information was also used to assess the pixel-based accuracies of the MODIS-derived major cropland extent. Chickpea-growing areas were identified and mapped based on their characteristic growing periods during the post-rainy season. Significant growth in the chickpea-growing areas was observed in the four districts of Andhra Pradesh between 2001 and 2012. The area cropped to chickpea almost tripled from 0.22 million ha during 2000–2001 to 0.6 million ha by 2012–2013. Furthermore, survey data were also used to assess the accuracy of the MODIS estimates of chickpea-growing areas. When compared with ground survey, the 10 land-use and land-cover classes derived from the MODIS temporal imagery resulted in overall accuracies of 86% of actual. The accuracy of areas identified as cropped to chickpea was 94%. To complement this remote-sensing study, a state-level representative primary household survey was conducted to elicit information on the socio-economic characteristics of chickpea-growing farmers, the extent of adoption of improved cultivars, costs and returns from chickpea cultivation, competitiveness of chickpea with other post-rainy crops, etc. during 2012–13. The findings revealed that nearly 98% of the chickpea cropped area is now under improved cultivars, with an average increase in yield of 37% over yields achieved with unimproved varieties. The average annual per capita incomes have increased to US$ 1.89 day^?1 with this silent chickpea revolution across the rain-fed areas of Andhra Pradesh.     

A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US

We present a dryland irrigation mapping methodology that relies on remotely sensed inputs from the MODerate Resolution Imaging Spectroradiometer (MODIS) instrument, globally extensive ancillary sources of gridded climate and agricultural data and on an advanced image classification algorithm. The methodology involves four steps. First, we use climate-based indices of surface moisture status and a map of cultivated areas to generate a potential irrigation index. Next, we identify remotely-sensed temporal and spectral signatures that are associated with presence of irrigation defined as full or partial artificial application of water to agricultural areas under dryland conditions excluding irrigated pastures, paddy rice fields, and other semiaquatic crops. Here, the temporal indices are based on the difference in annual evolution of greenness between irrigated and non-irrigated crops, while spectral indices are based on the reflectance in the green and are sensitive to vegetation chlorophyll content associated with moisture stress. Third, we combine the climate-based potential irrigation index, remotely sensed indices, and learning samples within a decision tree supervised classification tool to make a binary irrigated/non-irrigated map. Finally, we apply a tree-based regression algorithm to derive the fraction of irrigated area within each pixel that has been identified as irrigated. Application of the proposed procedure over the continental US in the year 2001 produces an objective and comprehensive map that exhibits expected patterns: there is a strong east-west divide where the majority of irrigated areas is concentrated in the arid west along dry lowland valleys. Qualitative assessment of the map across different climatic and agricultural zones reveals a high quality product with sufficient detail when compared to existing large area irrigation databases. Accuracy assessment indicates that the map is highly accurate in the western US but less accurate in the east. Comparison of area estimates made with the new procedure to those reported at the state and county levels shows a strong correlation with a small bias and an estimated RMSE of 2500 km², or little over 2% of the total irrigated area in the US. As a result, the future application of the new procedure at a global scale is promising but may require a better potential irrigation index, as well as the use of remotely sensed skin temperature measurements.     

Near‐infrared discrimination of leafless saltcedar in wintertime Landsat TM

To test a hypothesis that leafless riparian canopies enable accurate multi‐spectral discrimination of saltcedar (Tamarix ramosissima Ledeb.) from other native species, winter Landsat TM5 data (16 November 2005) were analysed for a reach of the Arkansas River in Colorado, USA. Supporting spectroscopic analysis confirmed that saltcedar could not easily be discriminated from other riparian vegetation using TM5 data when in‐leaf, but bare branches could be easily distinguished due to much lower reflectance than other riparian cover. Use of TM Band 4 (B4) allowed differentiation of wintertime saltcedar into four qualitative density classes judged from high‐resolution low‐oblique aerial photography: high (76%–100%), medium (51%–75%), low (16%–50%), and none (0%–15%). Spectral overlap was removed from the B4 saltcedar classification using TM Band 5 (B5) thresholds to eliminate low‐reflectant wet areas and higher‐reflectant multi‐year darkened weed canopies. The accuracy of a classification algorithm that used B5 thresholds followed by a B4 density slice was judged against high‐resolution aerial photography as providing 98% discrimination of saltcedar cover from other riparian cover and about 90% discrimination of the qualitative density classes. Applying this method to the 2835 km² riparian corridor study area, 1298 km² (45.78%) was identified as containing saltcedar, with over 43% having medium or greater density.     

Change in area of Ebinur Lake during the 1998–2005 period

Ebinur Lake is located in a typical arid region in the north‐west of China. It is an area with the lowest elevation in the Junggar Basin in the Province of Xinjiang. Recent monitoring indicates that the lake surface area has increased. To obtain a continuous record of the change in lake area, a radiometric analysis of SPOT/VEGETATION (VGT) imagery was carried out based on methodology developed for regional lake area mapping. Two indices, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), were selected to identify the water body of Ebinur Lake. The indices are calculated based on the spectral reflectances in the red and near infrared bands of VGT sensor. If the NDVI is less than a critical value (0) and if the NDWI is larger than a critical value (0), the pixel is flagged as a water body. Validation indicates that the methodology to identify water bodies based on multi‐spectral VGT data is applicable in our study area achieving an overall accuracy of 91.4%. Independent monitoring results elicit that the lake surface area was at its lowest in 1998. The yearly average surface area is about 503 km². The lake area increased to 603 km² during 1999. In the period 1999–2001 the area changes are marginal. A large area increase occurred from 2001 to 2002 till the lake area reached a surface area of 791 km². The lake area peaks to 903 km² in 2003 and subsequently decreased to areas of 847 km² in 2004 and 746 km² in 2005. Similar area change dynamics are observed when applying the remote sensing based technique. Seasonally, the typical dynamics elicit a larger surface area in spring and winter and a smaller one during summer.     

Spatiotemporal modelling of methane flux from the rice fields of India using remote sensing and GIS

Rice fields have been accredited as an important source of anthropogenic methane, with estimates of annual emission ranging from 47 to 60 Tg per year, representing 8.5–10.9% of total emission from all sources. In this study, attempts have been made to derive the spatial and temporal pattern of methane emitted from the rice lands of India using an integrated methodology involving satellite remote sensing and geographic information system (GIS) techniques. Multidate SPOT VGT 10‐day Normalized Difference Vegetation Index (NDVI) composite data for a complete year were used to map the rice area, delineate single‐ and double‐cropped rice areas, crop calendar and growth stages. Rainfall, digital elevation and irrigation data were integrated to stratify the rice area into distinct categories related to methane emission. Preliminary analysis of the methane emission pattern was carried out using published values. The results show that around 91% of total methane emission results from wet‐season rice, contributing 4.66 Tg per year. The temporal pattern shows that August and September are the months of peak emission during the wet season, and March and April during the dry season.     

Spatial and temporal responses of different crop‐growing environments to agricultural drought: a study in Haryana state,India using NOAA AVHRR data

Spatial and temporal responses to agricultural drought of different districts with different crop‐growing environments were assessed using National Oceanic & Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)‐derived monthly time composite Normalized Difference Vegetation Index (NDVI) images of a drought year (2002) and a normal year (2004) in Haryana state, one of the most prolific agricultural states of India. The seasonal NDVI profiles derived from NOAA AVHRR data, despite coarse spatial resolution, successfully captured the response of vulnerable districts to drought events. The greenness (NDVI) in mid‐season and at the end of the season of drought and normal years was compared. Districts having less irrigation support due to insufficient canal supplies and poor quality of groundwater had very high NDVI deviation from normal, signifying the impact of severe drought conditions in terms of reduced/delayed sown area, poor germination etc. in the year 2002. The districts with high irrigation support (surface water plus good quality groundwater) have either higher NDVI or insignificant deviation from a normal year and are not influenced by meteorological drought. Thus, quality of groundwater in different districts is a key factor to determine the vulnerability and sensitivity of the district to meteorological drought events in the study area state. The results of the study are relevant for vulnerability mapping and drought hazard zonation in the state to aid in‐season and long‐term management of droughts.     

Potential of using NOAA-AVHRR data for estimating irrigated area to help solve an inter-state water dispute

The states of Alabama, Florida and Georgia dispute the apportioning of water from rivers that originate in Georgia and flow through the other two states. Florida and Alabama often claim that Georgia uses more than its fair share of water. In order to address such a dispute, an estimation of the total amount of water used for irrigation by different crops is required. Current estimates of irrigated areas are subject to errors because they are based entirely on survey questionnaires. In this paper, the potential of Advanced Very High Resolution Radiometer (AVHRR) on-board the National Oceanic Space Administration (NOAA) satellites is examined for estimating irrigated area. Two indices, a widely used Normalized Difference Vegetation Index (NDVI) and a newer Vegetation Health Index (VHI), were regressed against irrigated area for 1986, 1989, 1992, 1995 and 2000 for selected regions in Georgia (Baker and Mitchell counties, and Seminole and Decatur counties). The average VHI during a period from the third week of February to the end of September was better related to irrigated area than the corresponding NDVI; R ² was above 0.80 as opposed to 0.49. It is concluded that the VHI, derived from three-channel AVHRR data, can be used to estimate irrigated area. By multiplying irrigated area with the application rate, the volume of irrigation used in a state can be determined, which can contribute to the solution of the water dispute.     

Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data

The overarching goal of this study was to map irrigated areas in the Ganges and Indus river basins using near-continuous time-series (8-day), 500-m resolution, 7-band MODIS land data for 2001-2002. A multitemporal analysis was conducted, based on a mega file of 294 wavebands, made from 42 MODIS images each of 7 bands. Complementary field data were gathered from 196 locations. The study began with the development of two cloud removal algorithms (CRAs) for MODIS 7-band reflectivity data, named: (a) blue-band minimum reflectivity threshold and (b) visible-band minimum reflectivity threshold.A series of innovative methods and approaches were introduced to analyze time-series MODIS data and consisted of: (a) brightness-greenness-wetness (BGW) RED-NIR 2-dimensional feature space (2-d FS) plots for each of the 42 dates, (b) end-member (spectral angle) analysis using RED-NIR single date (RN-SD) plots, (c) combining several RN-SDs in a single plot to develop RED-NIR multidate (RN-MDs) plots in order to help track changes in magnitude and direction of spectral classes in 2-d FS, (d) introduction of a unique concept of space-time spiral curves (ST-SCs) to continuously track class dynamics over time and space and to determine class separability at various time periods within and across seasons, and (e) to establish unique class signatures based on NDVI (CS-NDVI) and/or multiband reflectivity (CS-MBR), for each class, and demonstrate their intra- and inter-seasonal and intra- and inter-year characteristics. The results from these techniques and methods enabled us to gather precise information on onset-peak-senescence-duration of each irrigated and rainfed classes.The resulting 29 land use/land cover (LULC) map consisted of 6 unique irrigated area classes in the total study area of 133,021,156 ha within the Ganges and Indus basins. Of this, the net irrigated area was estimated as 33.08 million hectares—26.6% by canals and 73.4z5 by groundwater. Of the 33.08 Mha, 98.4% of the area was irrigated during khariff (Southwest monsoonal rainy season during June-October), 92.5% irrigated during Rabi (Northeast monsoonal rainy season during November-February), and only 3.5% continuously through the year.Quantitative Fuzzy Classification Accuracy Assessment (QFCAA) showed that the accuracies of the 29 classes varied from 56% to 100%—with 17 classes above 80% accurate and 23 classes above 70% accurate.The MODIS band 5 centered at 1240 nm provided the best separability in mapping irrigated area classes, followed by bands 2 (centered at 859 nm), 7 (2130 nm) and 6 (1640 nm).     

Spatial and temporal patterns of surface soil moisture over India estimated using surface wetness index from SSM/I microwave radiometer

Results from an approach to infer surface soil moisture from time series analysis of surface wetness index derived using the Special Sensor Microwave/Imager (SSM/I) are presented. Soil moisture quantification was based on the study of temporal changes in surface wetness index and its scaling to maximum and air‐dry limits of soil in each grid cell (0.33°). The estimated soil moisture of Illinois, USA was compared with field measured soil moisture (0–10?cm) obtained from the Global Soil Moisture Data Bank. A root mean square error of 7.18% was found between estimated and measured volumetric soil moisture. A consistency in soil moisture and rainfall pattern was found in the un‐irrigated areas of northern India (Jodhpur, Varanasi) and southern India (Madurai), influenced by southwest and northeast monsoons, respectively. Soil moisture of more than 0.30 m³m^?3 was observed in the absence of rainfall due to the irrigation of rice crop in (Punjab) during the pre‐southwest monsoon period (May).     

Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity

We present a method for a supervised classification of Normalized Difference Vegetation Index (NDVI) time series that identifies vegetation type and vegetation coverage, absolute in %coverage or relative to a reference NDVI cycle. The shape of the NDVI cycle, which is diagnostic for certain vegetation types, is our primary classifier. A Discrete Fourier Filter is applied to time series data in order to minimize the influence of high‐frequency noise on class assignments. Similarity between filtered NDVI cycles is evaluated using a linear regression technique. The correlation coefficients calculated between the Fourier filtered reference cycle and likewise filtered target cycles describe the similarity of their phenology, and the corresponding regression coefficients are an expression of coverage relative to the reference. The regression coefficients are correlated with field measured vegetation coverage. The Fourier Filtered Cycle Similarity method (FFCS) compensates phenological shifts, which are typical in areas with a strong climate gradient, and prevents the break‐up of classes of identical vegetation types on the basis of vegetation coverage. Some other advantages compared to traditional unsupervised classifications are: synoptic visualization of vegetation type and coverage variation, independence from scene statistics, and consistent classification of biophysical characteristics only, without rock/soil reflectance dominating class assignment as it often does in unsupervised classifications of sparsely vegetated areas. Using the FFCS classification we differentiated a total of five rangeland vegetation types for the area of Syria including their intra‐class coverage variation. Classified classes are dominated by one of two shrub types, one of two annual grass types or a bare soil/sparsely vegetated type.     

Seasonal change in the extent of inundation on floodplains detected by JERS-1 Synthetic Aperture Radar data

Two sets of JERS-1 (Japanese Earth Resource Satellite–1) Synthetic Aperture Radar (SAR) data, coupled with ancillary datasets, were analysed in an effort to find a single algorithm to study the extent of inundation and its variation on floodplains at a regional scale. The SAR data were acquired on 14 January, 1993 and 9 August, 1994. The study area was ca 14?212?km², covering the lower portions of the Cape Fear, Lumber, Little Pee Dee and Waccamaw river basins within the states of North Carolina and South Carolina, USA. The analysis was based on the decision tree classification that classifies the study area into three aquatic categories, water, marsh and flooded forest, and two upland classes, field and non-flooded forest. From January 1993 to August 1994, the aquatic extent varied from 4872?km² to 3496?km², and upland 9340?km² to 10?717?km². The decrease of the water, marsh and flooded forest categories and the increase of the field and non-flooded forest classes were mainly caused by falls in water surface heights and discharges of the rivers and their tributaries from January 1993 to August 1994. The overall classification accuracy was near to 90%. The search for the single algorithm ended with promising results and also prompted additional research.