National Remote Sensing Centre

The search for water on the surface of the anhydrous Moon had remained an unfulfilled quest for 40 years. However, the Moon Mineralogy Mapper (M3) on Chandrayaan-1 has recently detected absorption features near 2.8 to 3.0 micrometers on the surface of the Moon. For silicate bodies, such features are typically attributed to hydroxyl- and/or water-bearing materials. On the Moon, the feature is seen as a widely distributed absorption that appears strongest at cooler high latitudes and at several fresh feldspathic craters. The general lack of correlation of this feature in sunlit M3 data with neutron spectrometer hydrogen abundance data suggests that the formation and retention of hydroxyl and water are ongoing surficial processes. Hydroxyl/water production processes may feed polar cold traps and make the lunar regolith a candidate source of volatiles for human exploration.

Abstract Thermokarst is the process whereby the thawing of ice-rich permafrost ground causes land subsidence, resulting in development of distinctive landforms. Accelerated thermokarst due to climate change will damage infrastructure, but also impact hydrology, ecology and biogeochemistry. Here, we present a circumpolar assessment of the distribution of thermokarst landscapes, defined as landscapes comprised of current thermokarst landforms and areas susceptible to future thermokarst development. At 3.6 × 10 6 km 2 , thermokarst landscapes are estimated to cover ∼20% of the northern permafrost region, with approximately equal contributions from three landscape types where characteristic wetland, lake and hillslope thermokarst landforms occur. We estimate that approximately half of the below-ground organic carbon within the study region is stored in thermokarst landscapes. Our results highlight the importance of explicitly considering thermokarst when assessing impacts of climate change, including future landscape greenhouse gas emissions, and provide a means for assessing such impacts at the circumpolar scale.

Abstract Monthly rainfall data from June to October for 39 years were used to compute Standardized Precipitation Index (SPI) values based on two parameter gamma distribution for a low rainfall and a high rainfall districts of Andhra Pradesh state, India. Comparison of SPI with actual rainfall and rainfall deviation from the mean indicated that SPI values under‐estimate the intensity of dryness/wetness when the rainfall is very low/very high, respectively. As a result, the SPI in the worst drought years of 2002 and 2006 in the low rainfall district indicated only moderate dryness instead of extreme dryness. SPI values of the high rainfall district showed slightly better stretching in both positive and negative directions, compared to that of the low rainfall district. Further, the SPI values of longer time scales (2, 3 and 4 months) showed an extended range compared to that of 1 month, but the sensitivity in drought years has not improved significantly. Normality tests were conducted based on Shapiro‐Wilk statistic, p‐values and absolute value of the median to ascertain whether non‐normality of SPI is a possible reason. Although the results confirmed normal distribution, the scatter plot indicated deviation of the cumulative probability distribution of SPI from normal probability in the lower and upper ranges. Therefore, it is suggested that SPI as a stand alone indicator needs to be interpreted with caution to assess the intensity of drought. Further investigations should include sensitivity of SPI to the estimated shape and scale at lower and upper bounds of the gamma distribution and use of other distributions, such as Pearson III, to standardize the computational procedures, before using SPI as a substitute to the rainfall deviations from normal, for drought intensity assessment. Copyright © 2009 Royal Meteorological Society

The Indo‐Gangetic Plain (IGP) encompasses a vast area, (accounting for ∼21% of the land area of India), which is densely populated (accommodating ∼40% of the Indian population). Highly growing economy and population over this region results in a wide range of anthropogenic activities. A large number of thermal power plants (most of them coal fed) are clustered along this region. Despite its importance, detailed investigation of aerosols over this region is sparse. During an intense field campaign of winter 2004, extensive aerosol and atmospheric boundary layer measurements were made from three locations: Kharagpur (KGP), Allahabad (ALB), and Kanpur (KNP), within the IGP. These data are used (1) to understand the regional features of aerosols and BC over the IGP and their interdependencies, (2) to compare it with features at locations lying at far away from the IGP where the conditions are totally different, (3) to delineate the effects of mesoscale processes associated with changes in the local atmospheric boundary layer (ABL), (4) to investigate the effects of long‐range transport or moving weather phenomena in modulating the aerosol properties as well as the ABL characteristics, and (5) to examine the changes as the season changes over to spring and summer. Our investigations have revealed very high concentrations of aerosols along the IGP, the average mass concentrations ( M T ) of total aerosols being in the range 260 to 300 μ g m −3 and BC mass concentrations ( M B ) in the range 20 to 30 μ g m −3 (both ∼5 to 8 times higher than the values observed at off‐IGP stations) during December 2004. Despite, BC constituted about 10% to the total aerosol mass concentration, a value quite comparable to those observed elsewhere over India for this season. The dynamics of the local atmospheric boundary layer (ABL) as well as changes in local emissions strongly influence the diurnal variations of M T and M B , both being inversely correlated with the mixed layer height ( Z i ) and the ventilation coefficient ( V c ). The share of BC to total aerosols is highest (∼12%) during early night and lowest (∼4%) in the early morning hours. While an increase in the V c results in a reduction in the concentration almost simultaneously, an increase in Z imax has its most impact on the concentration after ∼1 day. Accumulation mode aerosols contributed ∼90% to the aerosol concentration at ALB, ∼77 % at KGP and 74% at KNP. The BC mass mixing ratio was ∼10% over all three locations and is comparable to the value reported for Trivandrum, a tropical coastal location in southern India. This indicates presence of submicron aerosols species other than BC (such as sulfate) over KGP and KNP. A cross‐correlation analysis showed that the changes in M B at KGP is significantly correlated with those at KNP, located ∼850 km upwind, and ALB after a delay of ∼7 days, while no such delay was seen between ALB and KNP. Back trajectory analyses show an enhancement in M B associated with trajectories arriving from west, the farther from to the west they arrive, the more is the increase. This, along with the ABL characteristics, indicate two possibilities: (1) advection of aerosols from the west Asia and northwest India and (2) movement of a weather phenomena (such as cold air mass) conducive for build up of aerosols from the west to east. As the winter gives way to summer, the change in the wind direction and increased convective mixing lead to a rapid decrease in M B .

For more than 20 years the Normalized Difference Vegetation Index (NDVI) has been widely used to monitor vegetation stress. It takes advantage of the differential reflection of green vegetation in the visible and near-infrared (NIR) portions of the spectrum and provides information on the vegetation condition. The Land Surface Water Index (LSWI) uses the shortwave infrared (SWIR) and the NIR regions of the electromagnetic spectrum. There is strong light absorption by liquid water in the SWIR, and the LSWI is known to be sensitive to the total amount of liquid water in vegetation and its soil background. In this study we investigated the LSWI characteristics relative to conventional NDVI-based drought assessment, particularly in the early crop season. The area chosen for the study was the state of Andhra Pradesh located in the Indian peninsular. The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index (VI) product from the Aqua satellite was used in the study. The analysis was carried out for the years 2002 (deficit year) and 2005 (normal year) using the NDVI from the MODIS VI product and deriving the LSWI using the NIR and SWIR reflectance available with the MODIS VI product. The response of LSWI to rainfall, observed in the rate of increase in LSWI in the subsequent fortnights, shows that this index could be used to monitor the increase in soil and vegetation liquid water content, especially during the early part of the season. The relationship between the cumulative rainfall and the current fortnight LSWI is stronger in the low rainfall region (<500 mm), while the one-fortnight lagged LSWI had a stronger relationship in the high rainfall region (>500 mm). The relationship between LSWI and the cumulative rainfall for the entire state was mixed in 2002 and 2005. The strength of the relationship was weak in the high rainfall region. When LSWI was regressed directly with NDVI for three LSWI ranges, it was observed that the NDVI with the one-fortnight lag had a strong relationship with the LSWI in most of the categories.

To detect landslides by object-based image analysis using criteria based on shape, color, texture, and, in particular, contextual information and process knowledge, candidate segments must be delineated properly. This has proved challenging in the past, since segments are mainly created using spectral and size criteria that are not consistent for landslides. This paper presents an approach to select objectively parameters for a region growing segmentation technique to outline landslides as individual segments and also addresses the scale dependence of landslides and false positives occurring in a natural landscape. Multiple scale parameters were determined using a plateau objective function derived from the spatial autocorrelation and intrasegment variance analysis, allowing for differently sized features to be identified. While a high-resolution Resourcesat-1 Linear Imaging and Self Scanning Sensor IV (5.8 m) multispectral image was used to create segments for landslide recognition, terrain curvature derived from a digital terrain model based on Cartosat-1 (2.5 m) data was used to create segments for subsequent landslide classification. Here, optimal segments were used in a knowledge-based classification approach with the thresholds of diagnostic parameters derived from If-means cluster analysis, to detect landslides of five different types, with an overall recognition accuracy of 76.9%. The approach, when tested in a geomorphologically dissimilar area, recognized landslides with an overall accuracy of 77.7%, without modification to the methodology. The multiscale classification-based segment optimization procedure was also able to reduce the error of commission significantly in comparison to a single-optimal-scale approach.

India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study.

Landslides are geomorphological processes that shape the landscapes of all continents, dismantling mountains and contributing sediments to the river networks. Caused by geophysical and meteorological triggers, including intense or prolonged rainfall, seismic shaking, volcanic activity, and rapid snow melting, landslides pose a serious threat to people, property, and the environment in many areas. Given their abundance and relevance, investigators have long experimented with techniques and tools for landslide detection and mapping using primarily aerial and satellite optical imagery interpreted visually, or processed by semi-automatic or automatic procedures or algorithms. Optical (passive) sensors have known limitations due to their inability to capture Earth surface images through the clouds and to work in the absence of daylight. The alternatives are active, “all-weather” and “day-and-night”, microwave radar sensors capable of seeing through the clouds and working in presence and absence of daylight. We review the literature on the use of Synthetic Aperture Radar (SAR) imagery to detect and map landslide failures – i.e., the single most significant movement episodes in the history of a landslide – and of landslide failure events – i.e., populations of landslides in areas ranging from a few to several thousand square kilometres caused by a single trigger. We examine 54 articles published in representative journals presenting 147 case studies in 32 nations, in all continents, except Antarctica. Analysis of the geographical location of 70 study areas shows that SAR imagery was used to detect and map landslides in most morphological, geological, seismic, meteorological, climate, and land cover settings. The time history of the case studies reveals the increasing interest of the investigators in the use of SAR imagery for landslide detection and mapping, with less than one article per year from 1995 to 2011, rising to about 5 articles per year between 2012 and 2020, and an average period of about 4.2 years between the launch of a satellite and the publication of an article using imagery taken by the satellite. To detect and map landslides, investigators use a common framework that exploits the phase and the amplitude of the electromagnetic return signal recorded in the SAR images, to measure terrain surface properties and their changes. To discriminate landslides from the surrounding stable terrain, a classification of the ground properties is executed by expert visual (heuristic) interpretation, or through numerical (statistical) modelling approaches. Despite undisputed progress over the last 26 years, challenges remain to be faced for the effective use of SAR imagery for landslide detection and mapping. In the article, we examine the theoretical, research, and operational frameworks for the exploitation of SAR images for landslide detection and mapping, and we provide a perspective for future applications considering the existing and the planned SAR satellite missions.

In India, human population has increased six-fold from 200 million to 1200 million that coupled with economic growth has resulted in significant land use and land cover (LULC) changes during 1880–2010. However, large discrepancies in the existing LULC datasets have hindered our efforts to better understand interactions among human activities, climate systems, and ecosystem in India. In this study, we incorporated high-resolution remote sensing datasets from Resourcesat-1 and historical archives at district (N = 590) and state (N = 30) levels to generate LULC datasets at 5 arc minute resolution during 1880–2010 in India. Results have shown that a significant loss of forests (from 89 million ha to 63 million ha) has occurred during the study period. Interestingly, the deforestation rate was relatively greater under the British rule (1880–1950s) and early decades after independence, and then decreased after the 1980s due to government policies to protect the forests. In contrast to forests, cropland area has increased from 92 million ha to 140.1 million ha during 1880–2010. Greater cropland expansion has occurred during the 1950–1980s that coincided with the period of farm mechanization, electrification, and introduction of high yielding crop varieties as a result of government policies to achieve self-sufficiency in food production. The rate of urbanization was slower during 1880–1940 but significantly increased after the 1950s probably due to rapid increase in population and economic growth in India. Our study provides the most reliable estimations of historical LULC at regional scale in India. This is the first attempt to incorporate newly developed high-resolution remote sensing datasets and inventory archives to reconstruct the time series of LULC records for such a long period in India. The spatial and temporal information on LULC derived from this study could be used by ecosystem, hydrological, and climate modeling as well as by policy makers for assessing the impacts of LULC on regional climate, water resources, and biogeochemical cycles in terrestrial ecosystems. • A gridded, annual database of land-use history (1880–2010) for India • A significant increase of deforestation rate under British rule • Increased forest area driven by government policies after the 1980s • Cropland area increased by 50 million ha or 56% for the period from 1880 to 2010. • Rapid cropland expansion occurred during 1950–1980 driven by technology and policy.

A Geographical Information System (GIS) integration tool is proposed to demarcate the groundwater potential zone in a soft rock area using seven hydrogeologic themes: lithology, geomorphology, soil, net recharge, drainage density, slope and surface water bodies. Except for net recharge and slope, the other five themes are derived from remote sensing data. IRS-1B LISS-II data was used for a 631 km2 area in Midnapur District, West Bengal, India. While slope was calculated using topographic sheets, net recharge was obtained from annual water table fluctuation data. Each feature of all the thematic maps was evaluated according to its relative importance in the prediction of groundwater potential. The evolved GIS-based model of the study area was found to be in strong agreement with available borehole and pumping test data.

Abstract. The present study deals with the impact of agriculture crop residue burning on aerosol properties during October 2006 and 2007 over Punjab State, India using ground based measurements and multi-satellite data. Spectral aerosol optical depth (AOD) and Ångström exponent (α) values exhibited larger day to day variation during crop residue burning period. The monthly mean Ångström exponent "α" and turbidity parameter "β" values during October 2007 were 1.31±0.31 and 0.36±0.21, respectively. The higher values of "α" and "β" suggest turbid atmospheric conditions with increase in fine mode aerosols over the region during crop residue burning period. AURA-OMI derived Aerosol Index (AI) and Nitrogen dioxide (NO2) showed higher values over the study region during October 2007 compared to October 2006 suggesting enhanced atmospheric pollution associated with agriculture crop residue burning.

Rapid satellite-based flood inundation mapping and delivery of flood inundation maps during a flood event can provide crucial information for planners and decision makers to prioritize relief and rescue operations. The present study is undertaken to optimize the threshold ranges for the classification of flood water in Synthetic Aperture Radar (SAR) images (of 20° to 49° incidence angles) for quick flood inundation mapping and response during flood disasters. This is done through assessing the signature of flood water in Horizontal transmit and Horizontal received (HH), Horizontal transmit and Vertical received (HV), Vertical transmit and Horizontal received (VH), and Vertical transmit and Vertical received (VV) polarization radar data. The mean backscattering signature profiles of various water bodies were analyzed to discriminate flood water from other water bodies. The study shows that there is better demarcation of land-water surface in HH polarization. VV polarization has the potential to identify partially submerged features, which can be useful in flood damage assessments. The backscatter of flood water in HV and VH is the same and both HV and VH polarizations are adequate for the mapping of flood water. At near range to far range, −8 to −12 dB, −15 to −24 dB, and −6 to −15 dB can be used as optimum ranges for the classification of flood water in HH, HV, and VV polarizations. These optimum threshold ranges can be applied to the automation of flood mapping using SAR images in near-real time, where much time was often spent on finding the thresholds in order to produce flood inundation maps in a short time from the onset of flood disasters and deliver such maps to the concerned agencies.

Aerosol measurements over the tropical urban site of Hyderabad, India, provide a way of determining the variability of the aerosol characteristics over a duration of 1 year (October 2007 to September 2008). The meteorological pattern over India, mainly driven by the regional monsoons, has a great effect on the amount and size distribution of the aerosols. Higher aerosol optical depth (AOD) values are observed in premonsoon, while the variability of the Ångström exponent ( α ) seems to be more pronounced, with higher values in winter and premonsoon and lower values in the monsoon periods. The AOD at 500 nm (AOD 500 ) is very large over Hyderabad, varying from 0.46 ± 0.17 in postmonsoon to 0.65 ± 0.22 in premonsoon periods. A discrimination of the different aerosol types over Hyderabad is also attempted using values of AOD 500 and α 380–870 . Such discrimination is rather difficult to interpret since a single aerosol type can partly be identified only under specific conditions (e.g., anthropogenic emissions, biomass burning or dust outbreaks), while the presence of mixed aerosols, without dominance of the coarse or accumulation mode, is the usual situation. According to the analysis the three individual components of differing origin, composition and optical characteristics are (1) an urban/industrial aerosol type composed of aerosols produced locally and all year round by combustion activities in the city or long‐range transported (mainly in spring) biomass burning, (2) an aerosol type of mineral origin raised by the wind in the deserts (mainly in premonsoon) or constituting coarse‐mode aerosols under high relative humidity conditions mainly in the monsoon period, and (3) an aerosol type with a marine influence under background conditions occurring in monsoon and postmonsoon periods. Nevertheless, the mixed or undetermined aerosol type dominates with percentages varying from 44.3% (premonsoon) to 72.9% (postmonsoon). Spectral AOD and α data are analyzed to obtain information about the adequacy of the simple use of the Ångström exponent for characterizing the aerosols. This is achieved by taking advantage of the spectral variation of lnAOD versus ln λ , the so‐called curvature. The results show that the spectral curvature can be effectively used as a tool for aerosol types discrimination, since the fine‐mode aerosols exhibit negative curvature, while the coarse‐mode particles are positive.

Abstract Landslide susceptibility is the likelihood of a landslide occurrence in an area predicted on the basis of local terrain conditions. Since last few years, researchers have attempted to analyse the probability of landslide occurrences and introduced different methods of landslide susceptibility assessment. The objective of this paper is to assess the landslide susceptibility in parts of the Darjeeling Himalayas using a relatively simple bivariate statistical technique. Seven factor layers with 24 categories, responsible for landslide occurrences in this area, are prepared from Cartosat and Resourcesat - 1 LISS-IV MX data. Each category was given a weight using the Information Value Method. Weighted sum of these values were used to prepare a landslide susceptibility map. The result shows that 8% area was predicted for high, 32% for moderate and remaining 60% for low landslide susceptibility zones. The high value (0.89) of the area under the receiver operating characteristic curve showed the high accuracy of the prediction model.

Satellite remote sensing provides diverse and useful ocean surface observations. It is of interest to determine if such surface observations can be used to infer information about the vertical structure of the ocean's interior, like that of temperature profiles. Earlier studies used either sea surface temperature or dynamic height/sea surface height to infer the subsurface temperature profiles. In this study we have used neural network approach to estimate the temperature structure from sea surface temperature, sea surface height, wind stress, net radiation, and net heat flux, available from an Arabian Sea mooring from October 1994 to October 1995, deployed by the Woods Hole Oceanographic Institution. On the average, 50% of the estimations are within an error of ±0.5°C and 90% within ±1.0°C. The average RMS error between the estimated temperature profiles and in situ observations is 0.584°C with a depth‐wise average correlation coefficient of 0.92.

The effect of a tropical cyclone on the variation of phytoplankton biomass in terms of surface chlorophyll‐ a is brought out based on satellite observations and mixed layer model simulations in the Arabian Sea during 21 May–3 June 2001. Along the cyclone's passage, chlorophyll‐ a was high with extreme values (5–8 mg m −3 ) in the blooms of phytoplankton. The model simulations indicate deepening of mixed layer on the southeastern edge of the cyclone. This forced mixed layer deepening, due to intense wind stirring and cyclone‐induced divergent geostrophic currents, has lead to the injection of nutrients into the surface layer, resulting in higher chlorophyll‐ a . This study suggests that the short‐lived tropical cyclones would alter the generally prevailing oligotrophic (nutrient depleted) conditions into a productive surface layer in the Arabian Sea during spring intermonsoon.

A multi-criteria analysis (MCA) approach to describe the effective utilization of geospatial techniques for disaster risk reduction at village level in Kopili River Basin (KRB) of Assam State, India is presented. The KRB is chronically flood affected due to seasonal monsoon and rise in water levels of Kopili River. Based on the MCA approach using flood hazard layer derived from the spatio-multi-temporal historic satellite data-sets (comprising of sensors from RISAT-1 SAR, Radarsat SAR and IRS AWiFS), socio-economic data (based on five census variables), infrastructure (road network) and land use vulnerabilities (cropped and uncropped areas), flood risk zones are derived. Our study elucidates that 24,837 ha of crop area spread across 95 villages in the KRB falls in high risk zone, about 39,209 ha distributed in 150 villages falls under moderate-high risk zones and remaining area spread over 162 villages is more or less unaffected. The proposed approach can be applied elsewhere in other river basins to estimate the flood risk so as to mitigate the disaster risk posed by the floods.

In this study, we proposed an automated lithological mapping approach by using spectral enhancement techniques and Machine Learning Algorithms (MLAs) using Airborne Visible Infrared Imaging Spectroradiometer-Next Generation (AVIRIS-NG) hyperspectral data in the greenstone belt of the Hutti area, India. We integrated spectral enhancement techniques such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformation and different MLAs for an accurate mapping of rock types. A conjugate utilization of conventional geological map and spectral enhancement products derived from ASTER data were used for the preparation of a high-resolution reference lithology map. Feature selection and extraction methods were applied on the AVIRIS-NG data to derive different input dataset such as (a) all spectral bands, (b) shortwave infrared bands, (c) Joint Mutual Information Maximization (JMIM) based optimum bands, and (d) optimum bands using PCA, to choose optimum input dataset for automated lithological mapping. The comparative analysis of different MLAs shows that the Support Vector Machine (SVM) outperforms other Machine Learning (ML) models. The SVM achieved an Overall Accuracy (OA) and Kappa Coefficient (k) of 85.48% and 0.83, respectively, using JMIM based optimum bands. The JMIM based optimum bands were more suitable than other input datasets to classify most of the lithological units (i.e. metabasalt, amphibolite, granite, acidic intrusive and migmatite) within the study area . The sensitivity analysis performed in this study illustrates that the SVM is less sensitive to the number of samples and mislabeling in the model training than other MLAs. The obtained high-resolution classified map with accurate litho-contacts of amphibolite, metabasalt, and granite can be coupled with an alteration map of the area for targeting the potential zone of gold mineralization.

Abstract A quantitative approach was pursued for identifying the most appropriate three-band combination of Landsat Thematic Mapper (TM) reflective bands data for delineating salt-affected soils of the Indo-Gangetic alluvial plain. The standard deviation and correlation coefficients values of the TM data were used for computing a statistical parameter called the 'Optimum Index Factor' (OIF) that is indicative of the information (variance) content of the data. Amongst all the 20 three-band combinations considered, the band combination 1, 3 and 5 was found to be the best in terms of information content. The normally used band combination 2, 3 and 4 ranked relatively very low. The validation of this conclusion with the accuracy estimates of the delineation of salt-affected soils using the same data revealed a mixed relation between the ranking obtained from the OIF values and the accuracy estimates, thereby pointing towards further investigation in other areas with similar terrain conditions.

Interaction of synthetic aperture radar (SAR) with vegetation is volumetric in nature, hence SAR is sensitive to the variation in vegetation density. At the same time SAR is also sensitive to other target properties such as canopy structure, canopy moisture, soil moisture and surface roughness of the underlying soil. However, the sensitivity of SAR backscatter to the vegetation density depends upon the frequency, polarization and angle of incidence at which the SAR is operated. This paper provides comparative evaluation of the sensitivity of multi‐frequency and multi‐polarized SAR backscatter to the plant density of Prosopis juliflora, a thorny plant. Monitoring of P. juliflora is of importance as the state forest department introduced it to arrest the spread of desert. In carrying out this study, data from the SIR‐C/X‐SAR mission over parts of Gujarat, India, have been used. In the present study, the variation of multi‐frequency (L and C) and multi‐polarized (HH, VV and VH) SAR backscatter with plant density has been studied. The results clearly indicate that cross‐polarized SAR backscatter at longer wavelength is the appropriate choice for the quantitative retrieval of plant density.