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The study explores the potential of next-generation satellite hyperspectral imaging systems for screening and predicting surface-soil contamination and degradation by exploiting various spectral indices and signature-matching techniques at a heavily industrialized area in India. The soil moisture content, desertification and salinity status, clay or fine material content, heavy metal content, vegetation health status, and stress levels were assessed from continuum-removed spectral reflectance values. Results indicated the presence of water in two tailings ponds, high salinity, and desertification values in most of the tailings ponds and dump sites, clay boundary liner along four ponds, high heavy metal indices along three ponds and all dump sites, highly stressed vegetation near all tailings ponds and coal dump sites, and pollutants in nearby water channels. The results suggest a strategy for the initial identification of priority areas for ground-based investigations and an alternative rapid methodology to monitor large industrial hubs in India. [ABSTRACT FROM AUTHOR]

Turbidity is an essential biogeochemical parameter for water quality management because it shapes the physical landscape and regulates ecological systems. It varies spatially and temporally across large water bodies, but monitoring based on point-source field observations remains a difficult task in developing countries due to the need for logistics and costs. In this study, we present a novel semi-analytical approach for estimating turbidity from remote sensing reflectance (R rs) in moderate to highly turbid waters in the lower part of the Godavari River (i.e., locations near Rajahmundry). The proposed method includes two sub-algorithms—Normalized Difference Turbidity Index (NDTI) and semi-empirical single-band turbidity ( T s ) algorithm—to retrieve spectral reflectance information corresponding to the study locations for turbidity modeling. Sentinel-2 Multi-Spectral Imager data have been used to quantify the turbidity in the Google Earth Engine (GEE) platform. The correlation analysis was observed between spectral reflectance values and in situ turbidity data using cubic polynomial regression equations. The results indicated that the T s , which uses the only red-edge wavelength, identified turbidity as the most accurate across all locations (highest R2 = 0.91, lowest RMSE = 0.003), followed by NDTI (highest R2 = 0.85, lowest RMSE = 0.05), respectively. The remote sensing data application provides a better way to monitor turbidity at large spatio-temporal scales in attaining the water quality standards of the Godavari River. [ABSTRACT FROM AUTHOR]

Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages, and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground-based hyperspectral canopy spectral reflectance measurements were recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., Forsyth County, GA, USA; spectral range: 350–2500 nm) and LAI data from 132 farmer's fields in Southern India. Among 29 hyperspectral vegetation indices tested, 8 were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, the elongation stage was the most accurately estimated using vegetation indices that exhibited a significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND705 (red-edge, blue, and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage. [ABSTRACT FROM AUTHOR]

• Photometric correction of IIRS images obtained from the Chandrayaan-2 mission. • IIRS data were found to be comparatively darker than Chandrayaan-1 M3. • Derived IIRS reflectances show minimal variation compared to M3 values. • Derived phase function fits for 950, 1500, and 1700 nm channels have high R2. • Absence of major artifacts (including cross-tracks) on IIRS data. India's second mission to the Moon, Chandrayaan-2, carried one of the most advanced imaging spectrometers ever sent to image a planetary surface – the IIRS (Imaging Infra-Red Spectrometer) – a 250 band spectrometer developed at the Space Applications Centre (SAC), ISRO, India. In this study, IIRS data between 800 and 2000 nm have been used to derive photometrically corrected reflectance from calibrated radiance data supplied by ISRO's online archive (Pradan) of Chandrayaan-2 data. Additionally, the photometric properties of three IIRS bands (950, 1500, and 1700 nm) have been studied in detail. Since the images obtained by IIRS are at different viewing geometry orientations, the purpose of performing a photometric correction on IIRS images is to re-project each pixel to a standard viewing geometry of incidence angle, i = 30°, emission angle, e = 0° and, phase angle, α = 30°. In order to correct the lunar limb darkening effect, a combination of the Lommel-Seeliger function and McEwen's model was used. In addition, topographic data from the merged Digital Elevation Model of Lunar Reconnaissance Orbiter – Lunar Orbiter Laser Altimeter (LRO-LOLA) and SELENE Terrain Camera (TC) were utilized to remove effects from local topography. A photometric function was then applied on four images captured by IIRS which were included in the first public release of data in August 2021. Radiance and reflectance (obtained after implementing photometric correction on IIRS images) values were compared with L1 and L2 M3 (Moon Mineralogy Mapper) data from Chandrayaan-1. It was found that although IIRS images are much darker than M3, corrected IIRS reflectance images had a much smaller variation in terms of reflectance values with M3. Moreover, the associated spectral reddening and reflectance values are within range when compared with data from other lunar missions. In addition, the reflectance spectra derived from the corrected IIRS images have been plotted for each image. However, the spectra exhibit significantly deeper absorptions than their corresponding M3 data for the same pixel. Phase functions derived from three bands have a good correlation with the reflectance values and phase angles. Consequently, this function can be applied to all IIRS images as and when more data are released to the public. [ABSTRACT FROM AUTHOR]

Hydrothermally altered minerals are invariably associated with base metal mineralization. The narrow spectral resolution of Airborne Visible/Infrared Imaging spectrometer -Next Generation (AVIRIS-NG) facilitates the identification and mapping of altered minerals. Spectral angle mapper (SAM) of supervised classification technique was applied on AVIRIS-NG reflectance data for identifying and mapping altered minerals such as talc, kaolinite, soapstone, kaosmec, and montmorillonite. The identified minerals were verified during the ground-truthing survey and, lab generated spectral reflectance curve confirms the presence of minerals. During the field survey, the surface signature of base metal mineralisation was found in the form of malachite stains in the carbonaceous phyllite lithologic unit. Conventional geological technique such as transmitted light petrography and X-ray diffraction analysis confirms the occurrence of base metal mineralization in the area. Therefore, this study justified the delineation of hydrothermally altered zones using AVIRIS-NG hyperspectral data and their validation through lab-generated spectra, petrography, and X-ray diffraction analysis. [ABSTRACT FROM AUTHOR]

Waste rock dumping is a major problem for opencast mine. The key approach to waste rock dump management is its re-use in the form of aggregates, m-sand, etc. which is also the basis of circular economy. The application of multispectral satellite data is considered to be one of the best approaches to characterise the mineral content of the waste rock dumps. In this research, multispectral data from Landsat 9 (OLI) were used to determine the sensitive bands for muscovite schist and gneissic rocks present in the hanging wall and footwall faces of an open-cast manganese mine in India. The behaviour of the spectral signature curves and their association with the surface reflectance values were used to identify these sensitive bands. XRF and XRD were used to identify the minerals of the rocks. The spectral signature reflectance curves obtained from the multispectral data of the identified minerals were compared with the USGS spectral libraries to determine these sensitive bands (bands 4, 5, 6, and 7) for rocks. Applying the band ratio technique, formulae representing the optimal band ratios were developed for mineral mapping. Methods like object-oriented principal component (PC) and independent component (IC) analyses were applied to determine the lithology based on spectral characteristics. False colour composite images in the RGB band combinations (PC4-PC3-PC1) and (IC4-IC2-IC1) were utilised for precise mineral mapping of the area. The methods were also validated with the waste rock dumps of a neighbouring manganese mine having similar geological setting. [ABSTRACT FROM AUTHOR]

Water is an essential natural resource, but increasingly water also forms a threat to the human population, with floods being the most common natural disaster worldwide. Earth Observation has the potential for developing cost-effective methods to monitor risk, with free and open data available at the global scale. In this study, we present the application of remote sensing observations to map flooded areas, using the Vembanad-Kol-Wetland system in the southwest of India as a case study. In August 2018, this region experienced an extremely heavy monsoon season, which caused once-in-a-century floods that led to nearly 500 deaths and the displacement of over a million people. We review the use of existing algorithms to map flooded areas in the Lake Vembanad region using the spectral reflectances of the green, red and near-infrared bands from the MSI sensor on board Sentinel-2. Although the MSI sensor has no cloud-penetrating capability, we show that the Modified Normalised Difference Water Index and the Automated Water Extraction Index can be used to generate flood maps from multi-spectral visible remote sensing observations to complement commonly used SAR-based techniques to enhance temporal coverage (from 12 to 5 days). We also show that local knowledge of paddy cultivation practices can be used to map the manoeuvring of water levels and exclude inundated paddy fields to improve the accuracy of flood maps in the study region. The flood mapping addressed here has the potential to become part of a solution package based on multi-spectral visible remote sensing with capabilities to simultaneously monitor water quality and risk of human pathogens in the environment, providing additional important services during natural disasters. [ABSTRACT FROM AUTHOR]

The mafic-ultramafic terrain of the Bhavani complex in southern India is considered for lithological mapping. The Landsat-8 OLI satellite data was used for the interpretation of different rock types in the study area. The satellite data were digitally processed using ENVI 5.6 image processing software. In the OLI data, excluding bands 8 and 9, the remaining seven bands were used for the generation of colour composite images, band ratios, principal component analysis and SVM classification. Reflectance spectral measurements were carried out in laboratory conditions for five rock samples collected from the study area. The XRF analysis was carried out to estimate the composition of major oxides present in the rock samples. The results obtained from XRF analysis were compared with the rock spectra in characterizing the spectral features of the rock types. The colour composite images (B543, B567, B456, and B457), PCA composite image (PC312 and PC456), band ratios (BR5/5 and BR4/3), colour composite images from band ratios, and SVM classified output are useful in delineation various rock types in the terrain. [ABSTRACT FROM AUTHOR]

In agriculture, soil fertility is maintained by using the compost, which contains Nitrogen (N), Phosphorus (P) and Potassium (K). Thus, it is required to acquire information about fertility status of soil and to apply the essential amount of composts. The laboratory-based chemical analysis methods for soil macronutrients test can be laborious, time-consuming, cost-intensive and destructive in nature. To overcome these issues, the hyperspectral remote sensing is employed for identification and determination of macronutrients of soil. The objective of this study is spectral unmixing of compositions of soil and NPK compost by using Derivative Analysis for Spectral Unmixing (DASU) approach. The proposed methodology has been tested for soil samples collected from an area located around Roorkee, UK, India. The applied methodology studies the spectral reflectance by using spectroradiometer data. The spectral regions 989.3 nm for pure NPK compost and 2195.1 nm for pure soils have been found optimal spectral absorption features. Accuracy assessment has been carried out on the basis of linear regression model between the true and estimated abundances. The coefficient of determination (R2) values for compositions of silt clay soil and NPK compost has been found at 989.3 nm spectral region as 0.892, 0.897 for compositions of loamy soil and NPK compost and 0.906 for sandy soil and NPK compost. Similarly, R2 values obtained at 2195.1 nm spectral region for silt clay soil and NPK compost is 0.932, 0.926 for compositions of loamy soil and NPK compost and 0.933 for sandy soil and NPK compost. The output of this study provides the fractional abundances of compositions of soil and NPK compost. Further, the results have been validated in laboratory by using chemical analysis methods. Thus, it may be concluded that hyperspectral remote sensing may be used in situ to estimate soil fertility status of farm soil. [ABSTRACT FROM AUTHOR]

The present study evaluates the water quality status of 6-km-long Kali River stretch that passes through the Aligarh district in Uttar Pradesh, India, by utilizing high-resolution IRS P6 LISS IV imagery. In situ river water samples collected at 40 random locations were analyzed for seven physicochemical and four heavy metal concentrations, and the water quality index (WQI) was computed for each sampling location. A set of 11 spectral reflectance band combinations were formulated to identify the most significant band combination that is related to the observed WQI at each sampling location. Three approaches, namely multiple linear regression (MLR), backpropagation neural network (BPNN) and gene expression programming (GEP), were employed to relate WQI as a function of most significant band combination. Comparative assessment among the three utilized approaches was performed via quantitative indicators such as R2, RMSE and MAE. Results revealed that WQI estimates ranged between 203.7 and 262.33 and rated as "very poor" status. Results further indicated that GEP performed better than BPNN and MLR approaches and predicted WQI estimates with high R2 values (i.e., 0.94 for calibration and 0.91 for validation data), low RMSE and MAE values (i.e., 2.49 and 2.16 for calibration and 4.45 and 3.53 for validation data). Moreover, both GEP and BPNN depicted superiority over MLR approach that yielded WQI with R2 ~ 0.81 and 0.67 for calibration and validation data, respectively. WQI maps generated from the three approaches corroborate the existing pollution levels along the river stretch. In order to examine the significant differences among WQI estimates from the three approaches, one-way ANOVA test was performed, and the results in terms of F-statistic (F = 0.01) and p-value (p = 0.994 > 0.05) revealed WQI estimates as "not significant," reasoned to the small water sample size (i.e., N = 40). The study therefore recommends GEP as more rational and a better alternative for precise water quality monitoring of surface water bodies by producing simplified mathematical expressions. [ABSTRACT FROM AUTHOR]

Studies of crop coefficients as a function of vegetation indices are reported less often for sugarcane than other crops because of the variation in crop evapotranspiration (ETc) and spectral response over the long growth period. In this study, the possibility of correlating crop coefficient (Kc) and ground based normalized difference vegetation index (NDVI) of a sugarcane crop was investigated based on 2 years field experiments conducted in 2015 and 2016 in semi-arid India. The Kc values for the full crop season were determined by the field water balance method and ground NDVI was estimated from spectral reflectance measurements using a field spectro-radiometer. The sugarcane Kc values for the tillering (development stage), grand growth (mid-season) and maturity stages (end season) were 0.70, 1.20 and 0.78, respectively. The results found that the Kc was 16.6% less than that suggested by FAO-56. The sugarcane NDVI ranged from 0.48 to 0.69 at the tillering stage and 0.69 to 0.93 in the grand growth stage. Unlike other crops, sugarcane NDVI at the maturity stage did not reduce from 0.85 even at harvest due to the continued production of fresh green leaves at the top of the plant. Regression equations were developed to estimate the seasonal distribution of Kc with NDVI as the dependent variables and ratio of days t T after planting (t) to the total crop period (T) as the independent variable. The relationship between crop Kc and NDVI was characterized with 2nd order polynomial regression but correlation was moderately strong (r = 0.75, n = 315). Stronger correlations between Kc and NDVI were obtained by splitting growth period into the growth phase (r = 0.98, n = 245) and decline phase (r = 0.99, n = 70). The estimated Kc will be helpful for correcting irrigation scheduling of sugarcane in semi-arid conditions. The Kc-NDVI relationships for sugarcane investigated in this study are important for potential real time irrigation water management in the future. [ABSTRACT FROM AUTHOR]

• Plot level rice lodging assessment with ten band multi-spectral UAV imagery. • Identification of optimal spectral, textural, and colour features for lodging assessment. • RF-based classifier yields >95 % accuracy in lodging assessment with combined features. • Demonstrated superiority of present approach and model over comparable studies. Rice plant lodging leads to change in canopy structure, yield loss and creates a menace in harvest operations. In situ assessment of lodging is time consuming, labour intensive, inefficient and inaccurate. Its assessment contributes greatly in in-situ field management and damage analysis. In this study, imaging observations from ten-band (within 444–842 nm) multispectral camera at 0.06 m Ground Sampling Distance (GSD) on-board an unmanned aerial vehicle (UAV) were acquired over a rice research farm (22.7930 N and 72.57140 E), Anand, Gujarat in western part of India. A set of features such as spectral reflectance, vegetation indices, colour coordinates and index, textural parameters and combination of all these were used for discriminating lodged rice crop from standing ones. All these features were extracted and analysed to optimize the sensitive features followed by discrimination of these two classes of rice using ensemble learning based Random Forest (RF) classifier. The analysis revealed that Green, Red-edge and Near-infrared (NIR) bands showed most optimal spectral features for lodging detection. The mean texture of these bands was also found to be sensitive indicators for rice lodging. Combined features with RF classifier produced an overall accuracy of 96.1% with kappa coefficient (κ) of 0.92 followed by textural features with an overall accuracy of 93.5 % and κ of 0.86. Plot level lodging assessment revealed that lodged area varied from 0.1 % to 15.5 % of the cropped area over different plots. The results were validated with the visually interpreted lodged areas using RGB image that resulted into R2 of 0.97 with relative root mean square error (rRMSE) of 0.02 %. Our results conclude that multispectral UAV based remote sensing can help in rapid damage assessment and plot-level field management with high precision. [ABSTRACT FROM AUTHOR]

Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (8652) to form a road index (RJ) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The 8652 is converted to road index by normalization. The morphological operators (Top-hat or Bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square tilter (SSF), is proposed. Then, an iterative adaptive threshold (lAT) based online search with variational min-max and markov random fields (MRF) model are used on the SSF image to segment the roads and non-roads. The roads are extracting by using the rules based on the connected component analysis. IAT and MRF model segmentation methods prove the proposed index (RJ) able to extract road features productively. The proposed methodology is a combination of saturation based adaptive thresholding and morphology (SATM), and saturation based MRF (SMRF), applied to OLI images of several urban cities of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper. [ABSTRACT FROM AUTHOR]

Variability in the leaf phenology of tropical trees impacts their growth. How phenology of tree species responds over rainfall gradient is relevant to study in the light of current climatic changes. Airborne visible and infrared imaging spectrometer-next generation (AVIRIS-NG) spectral datasets have been considered for this study as they not only provide wider area of coverage, but also high spatial and spectrally resolved output. Canopy-level spectra of 16 common species of four different forest covers in India were synced with observed phenology phase and the annual rainfall in each forest cover was recorded. Reflectance spectra of the same species in the four forest covers distinctively differed over rainfall gradient, indicating intra-species pliability. Consistent lower reflectance/higher absorption at chlorophyll bands of all the common deciduous species in the higher annual rainfall region over that with relatively lower rainfall indicated that deciduous species acclimate green foliage phase of the phenology cycle. Boxplots of reflectance values of chlorophyll absorption band of 16 species showed a decrease in the variability of the datasets over the four forest covers, revealing that increasing rainfall provides better synchrony in the phenology phase of the observed tree species. The study highlights the importance of AVIRIS-NG spectral datasets in monitoring different phases of forest phenology associated with growth potential dynamics effectively under changing rainfall pattern. [ABSTRACT FROM AUTHOR]

By studying the spectral reflectance features of different land cover types and leveraging information of primarily “BLUE” band along with “RED” and “NIR” bands, this article seeks to introduce a new built-up index such as powered B1 built-up index (PB1BI). The proposed index, while being conceptually simple and computationally inexpensive, can extract the built-up areas from Landsat7 satellite images efficiently. For Landsat7 satellite imagery, classification performances of the proposed index along with support vector machine (SVM), artificial neural network (ANN), and three existing built-up indices have been examined for three study sites of 1° Latitude $\times 1^\circ $ Longitude ($\approx 12\,100~{\mathrm {sq}}\cdot {\mathrm {km}}$) area from three diverse geographical regions in India. The computed value of the M-Statistics for PB1BI is consistently greater than 1.80, indicating a better spectral separability between built-up and nonbuilt-up classes by the index. In order to improve the performance of the built-up indices, this article has suggested a bootstrapping method for threshold estimation in addition to the existing Otsu’s method for the same. It has been found that using bootstrapping method instead of Otsu’s method for threshold estimation has helped to improve the classification performance of built-up indices up to 17.75% and 40.49% in terms of overall accuracy and kappa ($\kappa $) coefficient, respectively. It has been observed that for the validation set, average overall accuracy (97.45%) and kappa ($\kappa $) coefficient (0.907) of PB1BI for considered study sites are not only significantly higher than existing indices but also comparable with the same of SVM (99.10% and 0.942) and ANN (87.24% and 0.450). This article has also shown that the proposed index provides a stable performance for multitemporal analysis of the study sites and is able to capture growth in built-up region in time horizon. The classification performance of PB1BI has also been verified for Landsat8 imagery across 11 study sites from different continents around the globe, and the results show overall accuracy and $\kappa $ to be consistently more than 90% and 0.75, respectively. For considered study sites, the reported values of average accuracy and $\kappa $ of PB1BI for built-up classification using Landsat8 satellite data are 95.7151% and 0.8843, respectively. [ABSTRACT FROM AUTHOR]

The language of color pervades the natural world, with flowers being prime exemplars of communicating with color. Our understanding of floral colors is predominantly qualitative. It hinders the comprehensive decoding of information relayed through vibrant displays. Here we present the results of quantitative studies of floral colors by integrating hyperspectral remote sensing and color science. Further, we present the Indian Floral Spectral database, comprising spectral reflectance data for 686 angiosperm species in Kerala, India. Analysis of the elements in the database reveals intriguing insights into the altitudinal variability in human-perceived floral colors across nine angiosperm species. Our research introduces a novel approach that utilizes floral spectral reflectance data to study subtle changes in the landscape. Through the examination of floral color variations, we gain valuable insights into intra and inter-specific plant communications. The quantified floral colors posit as an objective plant trait, a prospective Tier I indicator of biodiversity targets. • Study examines floral color as a key trait in plant reproduction during the reproductive phase. • Study explores objective floral color representation, reporting altitudinal variations in intra/inter-specific floral color via spectral reflectance. • Quantifying diverse floral colors across landscapes offers insight into natural selection's process and outcomes. • Altitudinal variability of inter and intraspecific floral colours is prominent and significant. • Quantified floral colours are prospective Tier I indicators of biodiversity targets of the sustainable development goals. [ABSTRACT FROM AUTHOR]

Remote sensing measurements in coral reef environments commonly confront the problem of overlying atmosphere and modification of spectral signal due to water column over the bottom substrates. In order to correct these problems, hyperspectral observations offer an advantage over multispectral observations. Airborne hyperspectral remote sensing data from Airborne Visible Infrared Imaging Spectrometer- Next Generation (AVIRIS-NG) sensor was acquired during low tidal condition on 14 February 2016 at Pirotan reef, Gulf of Kachchh region, India. The objective of this study is to map benthic coverage and bottom topography over Pirotan reef. The methodology involved atmospheric correction, simultaneous retrieval of water parameters, bathymetry, water column correction and mapping. Atmospheric correction was performed by removing path radiance and aerosol contribution and dividing by atmospheric transmittance and incoming solar irradiance to obtain remote sensing reflectance. Model derived error minimization technique was used for simultaneous retrieval of water parameters and bathymetry. Derived water parameters were used to account for water column attenuation and retrieve concomitant true bottom signature. [ABSTRACT FROM AUTHOR]

• Comparative studies on the basis of presence and absence of minerals. • Discrimination of the mineral body from the settlements. • Pyrrhotite and limonite (iron ore) minerals have been traced. • Mixture Tuned Matched Filtering algorithm produces a better mineral map. Geology majorly deals with the structures, features, minerals, rocks, etc. of the planet Earth. Geologists are using the technology of remote sensing for structural interpretation and regional mapping. Ores and minerals identification are also done with the help of remote sensing. This paper is mainly focused on the identification of minerals and mapping with the help of various algorithms such as Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Mixture Tuned Matched Filtering (MTMF) using airborne hyperspectral data. Minerals are identified on the basis of the visible and near-infrared spectral reflectance. Spectral reflectance is having absorption features at different positions and absorption peaks are used for the analysis of imagery. This technique provides surface mineralogical details. SAM algorithm mainly computes the angle between the unknown pixel spectrum and unique pixel spectrum. SFF algorithm matched the continuum removed the spectrum of the imagery pixel from the continuum removed reference spectra. MTMF algorithm detects the abundance of the minerals and removes the erroneous positive. Total 13 endmembers (minerals) were identified in the study area. These minerals are grouped into clay minerals, iron minerals, carbonate minerals, and other minerals. These endmembers are used for the mineral map creation from different algorithms. Algorithms produce the diverse kind of mineral map and these are compared with each other on the basis of the mineralogy and discrimination of mineralized region from settlements. Mineral map produced by MTMF algorithm provides convenient results with better accuracy than other algorithms. [ABSTRACT FROM AUTHOR]

Classification of crops is very important to study different growth stages and forecast yield. Remote sensing data plays a significant role in crop identification and condition assessment over a large spatial scale. Importance of Normalized Difference Indices (NDIs) along with surface reflectances of remotely sensed spectral bands have been evaluated for classification of eight types of Rabi crops utilizing the Landsat-8 and Sentinel-2 datasets and performances of both the satellites are compared. Landsat-8 and Sentinel-2A images are acquired for the location of crops and seven and nine spectral bands are utilized respectively for the classification. Experiments are carried out considering the different combinations of surface reflectances of spectral bands and optimal NDIs as features in support vector machine classifier. Optimal NDIs are selected from the set of 7C2 and 9C2 NDIs of Landsat-8 and Sentinel-2A datasets respectively using the partial informational correlation measure, a nonparametric feature selection approach. Few important vegetation indices (e.g. enhanced vegetation index) are also experimented in combination with the surface reflectances and NDIs to perform the crop classification. It has been observed that combination of surface reflectances and optimal NDIs can classify the crops more efficiently. The average overall accuracy of 80.96% and 88.16% are achieved using the Landsat-8 and Sentinel-2A datasets respectively. It has been observed that all the crop classes except Paddy and Cotton achieve producer accuracy and user accuracy of more than 75% and 85% respectively. This technique can be implemented for crop identification with adequate accessibility of crop information. [ABSTRACT FROM AUTHOR]

The present study reports the existence of a unique and unusual feature of the Zanskar valley, Ladakh, India – a proglacial ‘red/brown-coloured lake’ observed from space-based remote sensing data. The lake has not changed in colour and size over the years. Here we report observations from 2004 onwards till recently. The local geology plays an important role, and iron-dominated lithology of the region interacts with snow and glacial meltwaters and subsequently deposits red/brown-coloured suspended silt in this proglacial lake. Spectral analysis of reflectance data from Sentinel-2 images in visible–infrared region of electromagnetic spectrum suggests that the colour of the lake is due to high concentration of suspended solids, having dominant reflectance at 660–700 nm and thus causing the red/brown colour of the water. [ABSTRACT FROM AUTHOR]

Robust calibration algorithms are needed for the accurate assessment of soil properties in the diffuse reflectance spectroscopy (DRS) approach. Despite several studies on different calibration algorithms, the prediction accuracy of soil properties using DRS need to be improved. Specifically, the utility of local modeling approaches for small spectral libraries is less examined compared with global modeling approaches. In this study, we compared global modeling approaches such as partial-least-squares regression (PLSR), lasso, ridge regression with several locally-weighted PLSR (PLSR LW ) approaches. We also examined seven different distance measures: Euclidean distance, covariance-based distance, correlation-based distance, surface difference spectrum, information-based distance, optimized principal component Mahalanobis, and locally-linear embeddings used in the PLSR LW approach for their effectiveness in modeling soil properties using DRS. A total of 954 soil samples were collected from three different states of India: West Bengal, Odisha, and Karnataka. Five soil properties such as sand content, clay content, soil organic carbon (SOC) content, extractable iron (Fe) content and extractable zinc (Zn) content were predicted using reflectance spectra over 350–2500 nm. Root-mean-squared error (RMSE) and the coefficient of determination (R 2 ) were used as performance statistics. Among the global modeling approaches, lasso performed better than the PLSR although it is computationally more intensive than the PLSR. In general, the correlation-based PLSR LW performed significantly better than the global approaches. Specifically, the test R 2 values increased from 0.66 to 0.72 for prediction of sand content, from 0.59 to 0.70 for prediction of SOC content, and from 0.70 to 0.74 for prediction of Fe content by using the PLSR LW over PLSR. We also used depth of absorption peak of spectra at approximately 1400, 1900 and 2200 nm for mineralogical characterization of soil samples. The results suggested that the improvement in prediction accuracy of soil properties using the PLSR LW was achieved because calibration samples which had same mineralogy as the test sample were given higher weights. These results suggest that the prediction accuracy of soil properties may also be improved in small spectral libraries if an appropriate local modeling scheme is selected. [ABSTRACT FROM AUTHOR]

In the present paper, a methodology has been developed for the mapping of snow cover in Beas basin, Indian Himalaya using AWiFS (IRS-P6) satellite data. The complexities in the mapping of snow cover in the study area are snow under vegetation, contaminated snow and patchy snow. To overcome these problems, field measurements using spectroradiometer were carried out and reflectance/snow indices trend were studied. By evaluation and validation of different topographic correction models, it was observed that, the normalized difference snow index (NDSI) values remain constant with the variations in slope and aspect and thus NDSI can take care of topography effects. Different snow cover mapping methods using snow indices are compared to find the suitable mapping technique. The proposed methodology for snow cover mapping uses the NDSI (estimated using planetary reflectance), NIR band reflectance and forest/vegetation cover information. The satellite estimated snow or non-snow pixel information using proposed methodology was validated with the snow cover information collected at three observatory locations and it was found that the algorithm classify all the sample points correctly, once that pixel is cloud free. The snow cover distribution was estimated using one year (2004-05) cloud free satellite data and good correlation was observed between increase/decrease areal extent of seasonal snow cover and ground observed fresh snowfall and standing snow data. [ABSTRACT FROM AUTHOR]

In the context of a growing interest in remote sensing for precision agriculture applications, the utility of space-borne hyperspectral imaging for the development of crop-specific spectral library and automatic identification and classification of three important varieties for each of rice, chillies, sugar cane, and cotton crops in Andhra Pradesh state of India has been investigated in this study. The classification of crops at variety level using two spectral libraries developed using hyperspectral reflectance data at canopy scale (in situ hyperspectral measurements) and at pixel scale (Hyperion data) has shown promising results, with 86.5% and 88.8% overall classification accuracy, respectively. This observation highlights the possible integration of in situ hyperspectral measurements with space-borne hyperspectral remote-sensing data for automatic identification and discrimination of various crop varieties. However, considerable spectral similarity is observed between varieties of rice and sugar-cane crops. This spectral similarity, if common to most of the crop species, may pose problems for the accurate discrimination of various crop varieties using spectral measurements. [ABSTRACT FROM AUTHOR]

Changes in the reflectance and transmittance of jute canopy at different visible wavelengths of solar radiation are measured at different growth stages throughout the period of half crop cycle. The reflectance is found to gradually decrease and attain a steady value with canopy growth up to about one-third of the crop cycle. As an independent study, vegetation growth parameters such as canopy height, leaf length, leaf area are simultaneously measured. It is concluded that although these parameters have individual properties, the whole canopy ultimately occupies a constant fraction of the air volume over the soil. As a combined effect of the above parameters, the transmittance through the leaf-air composite becomes saturated. A suitable mathematical explanation is given for the above. It is affirmed from these two independent sets of experiments that the vegetation growth is truly echoed in the temporal change in visible wavelength reflectance, the remotely sensible parameter. Thus the present work proposes a methodology for remotely observing the growth of vegetation and it is justified with jute, an important cash crop in eastern India. [ABSTRACT FROM AUTHOR]

This communication demonstrates the benefits of imaging spectrometry in the study of the spectral reflectance characteristics of teak (Tectona grandis L.) growing in Shoolpaneshwar Wildlife Sanctuary, Gujarat, using the EO-1 Hyperion data from NASA, USA. Hyperion is a space-borne sensor providing detailed information of the earth's surface features with an imaging spectrometer having 242 spectral bands ranging from 400 to 2500 nm. An atmospheric scattering and absorption effect within the image was corrected with Atmospheric Correction Now software from ImSpec LLC. MNF transformation was applied to determine the presence of cross-track spectral smile, which appeared as a brightness gradient in visiblenear infrared bands. Destreaking was also applied to minimize the striping artifacts normally found in Hyperion data. The reflectance spectra so derived were compared with raw-data spectra. Sharp variations were seen in the spectral signatures of teak with reference to variations in girth class and slope of the area, indicating the high sensitivity associated with the Hyperion sensor. Spectral variation was better in shortwave infrared-I region, indicating its uniqueness for studying the vegetation cover. [ABSTRACT FROM AUTHOR]

Abstract. Spectral reflectance of leaves is influenced primarily by plant pigments, chlorophyll and carotenoids. Such reflectance can be used to study the changes in chlorophyll content and nitrogen status and in turn measures the amount of biomass accumulation. A field experiment was laid out at the Research Farm of ANGR Agricultural University, Hyderabad. The reflectance observations were taken using a hand-held ground radiometer at an interval of 15 days beginning from 30 days after sowing (DAS) until harvest of the crops. The plant pigments were determined simultaneously using DMSO (dimethyl sulphoxide) method in the laboratory. The experimental results revealed the influence of plant pigments on spectral reflectance of maize, groundnut and soybean. It was observed that there was an increase in chlorophyll-a, chlorophyll-b, total chlorophyll and carotenoid content up to flowering and thereafter chlorophyll-a content declined at a faster rate than chlorophyll-b towards physiological maturity. With the increase in concentrations of chlorophyll and carotenoids, there was a decline in spectral reflectance of the blue band (450-520 nm) and the red band (620-680 nm). Whereas, NIR (near-infrared) reflectance in the case of soybean and groundnut was found to be higher than that of maize by 11% and 2%, respectively. This was attributed to canopy cover of soybean and groundnut crops, where the soil was fully covered with vegetation. In case of maize, due to wider spacing, the soil exposure is greater, which results in low reflectance values of the NIR band. Normalized Difference Vegetation Index (NDVI) is linearly related to total chlorophyll content and the growth stages of a crop up to flowering. The NDVI differs significantly during the peak vegetative growth period among the three crop types. The study revealed that the significant differences in reflectance of maize, groundnut and soybean in the red and NIR bands were influenced by concentrations of chlorophyll-a , chlorophyll-b and carotenoids, which indicates the photosynthetic behaviour of the crops. [ABSTRACT FROM AUTHOR]

High-spectral-resolution data of the Airborne Imaging Spectrometer (AIS) developed by the Indian Space Research Organisation were analysed to check their potential for extraction of information concerning crop growth parameters. Reflectance spectra of wheat plots at different growth stages were generated to study the position of red edge. The shift of inflection wavelength towards longer wavelength was observed with increase in Leaf Area Index (LAI) and chlorophyll content. For the range of LAI from 0.08 to 3.16 and chlorophyll content from 2 to 39 (gm[sup -2]), the shift of inflection wavelength was observed in the range of 713-723 nm. A linear relationship was observed between the inflection wavelength and LAI as well as chlorophyll content. [ABSTRACT FROM AUTHOR]

The Wide-Field Sensor (WiFS) on board Indian Remote Sensing Satellites (IRS-1C, IRS-P3 and IRS-1D) has a wide swath (~810 km) and is thus able to provide high repetivity (5/3 days) data acquired from different orbits. The impact of variable viewing geometry from WiFS on crop reflectance was investigated for Delhi (77.2°E and 28.58°N) for all 122 possible acquisitions by IRS-1C during 1997. Using the SAIL simulation model, top-of-canopy red and near-infrared reflectances were simulated for partial and closed crop canopies over soils of different brightness. Results show significant variations in red and near-infrared reflectances. The effects are higher in partial canopies, and they are observed in Normalized Difference Vegetation Index (NDVI) also. For partial canopy, the seasonal variations with solar zenith angle are also significant. For closed canopy, these variations are less, and computed NDVI is relatively insensitive to viewing geometry. [ABSTRACT FROM AUTHOR]

• The spectral reflectance of salt-affected soils decreased with increased salinity. • Visible-near-infrared spectroscopy predicted properties of salt-affected soils. • The best predictions were obtained for salinity using support vector regression. • The SVR, PLSR, and PCR were robust to predict properties of salt-affected soils. The study aimed to estimate the properties of the salt-affected soils (SAS) using hyperspectral remote sensing. The study was carried out on typical SAS from 372 locations covering 17 coastal districts from the west coast region of India. The spectral reflectance of processed soil samples was recorded in the wavelength range of 350–2500 nm. The full dataset (n = 372) was split into two as a calibration dataset (n = 260, 70% of full dataset) to develop the model and validation dataset (n = 112, 30% of full dataset) to evaluate the performance of the model independently. The spectral data were calibrated using the laboratory estimated soil properties with five different multivariate techniques (a) Linear – partial component regression (PCR) and partial least square regression (PLSR) and (b) Non-linear– multivariate adaptive regression spline (MARS), random forest (RF) and support vector regression (SVR). In general, the spectral reflectance from the soils decreased with increasing levels of salinity (electrical conductivity, EC). The wavelengths 427, 487, 950, 1414, 1917, 2206, 2380 and 2460 nm showed peculiar absorption characteristics. The study showed significant achievement in predicting soil pH, salinity (EC), bulk density, soil available nitrogen, exchangeable magnesium, soil available zinc, and boron with acceptable to excellent predictions (ratio of performance to deviation ranged 1.48–2.06). Amongst predicted models, SVR, PLSR, and PCR were found to be more robust than MARS and RF. The results of the study indicated that the visible near-infrared spectroscopy has the potential to predict properties of the SAS. [ABSTRACT FROM AUTHOR]

• We compared Rao's Q and Shannon indices multi-scale approach to estimate β-diversity. • Landsat-8, RapidEye and Pléiades 1A derived NDVI were used to derive Rao's Q and Shannon Indices. • CV of spectral reflectance exhibits as an indicator of spectral diversity. • β-Diversity-NDVI relationships are sensitive to the scale and spatial resolution. • Results show success of Rao's Q index over Shannon index to explain spectral heterogeneity. Space-borne remote sensing missions provide robust, timely and continuous data to assess biodiversity in remote or protected areas, where direct field observations can be prohibited by difficult accessibility. The objective of this study was to extend the concept of remote sensing based assessment of beta-diversity to multi-scale domain by multi-resolution optical satellite data. This study was conducted in a reserved forest of western Himalaya, India; a region affected by the invasive Lantana camara L (lantana). We calculated and compared Rao's Q and Shannon indices at different spatial resolutions (0.5, 5, and 30 m) and scales (window sizes) by using imageries from Pléiades 1A, RapidEye, and Landsat-8 acquired in April 2013, the pre-monsoon season. Rao's Q index explained diversity more accurately than Shannon index for the three analyzed stand densities. Diversity was better approximated by Rao's Q index calculated by Pléiades 1A at a resolution of 0.5 m at low stand density. We observed higher correlations of the average coefficient of variation (CV) with Rao's Q and Shannon indices for areas associated with mixed spectral reflectance caused by overstory and understory vegetation. Furthermore, CV was lower in open areas dominated by lantana. These results indicated a strong scale and spatial resolution dependence of Rao's Q index on remote sensing-derived spectral heterogeneity information. When applied in heterogeneous forest environments, Rao's Q index could represent a better remote sensing proxy to estimate beta-diversity than the conventional Shannon index. [ABSTRACT FROM AUTHOR]

Optimal Estimation (OE) methods can simultaneously estimate surface and atmospheric properties from remote Visible/Shortwave imaging spectroscopy. Simultaneous solutions can improve retrieval accuracy with principled uncertainty quantification for hypothesis testing. While OE has been validated under benign atmospheric conditions, future global missions will observe environments with high aerosol and water vapor loadings. This work addresses the gap with diverse scenes from NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) India campaign. We refine atmospheric models to represent variable aerosol optical depths and properties. We quantify retrieval accuracy and information content for both reflectance and aerosols over different surface types, comparing results to in situ and remote references. Additionally, we assess uncertainty of maximum a posteriori solutions using linearized estimates as well as sampling-based inversions that more completely characterize posterior uncertainties. Principled uncertainty quantification can combine multiple spacecraft data products while preventing local environmental biases in future global investigations. • We validate Optimal Estimation (OE) atmospheric correction for challenging conditions. • Linearized and MCMC estimates provide rigorous uncertainty quantification. • OE with aerosol and H2O vapor estimation significantly improves reflectance accuracy. • Retrieved aerosol properties are consistent with in-situ and remote space-based data. [ABSTRACT FROM AUTHOR]