Browsing by Author "Singh, Prachi"
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Publication A hyperspectral R based leaf area index estimator: model development and implementation using AVIRIS-NG(Taylor and Francis Ltd., 2022) Singh, Prachi; Srivastava, Prashant K.; Mall, R.K.; Bhattacharya, Bimal K.; Prasad, RajendraHyperspectral Remote Sensing (HRS) data is vital for crop growth monitoring due to availability of contiguous bands. This research work provides a new novel crop estimator model given the name �Crop Stage estimator� developed using the HRS data on an open-source R platform. The generic model structure provides an easy way to test and modify the importance of crop parameter namely Leaf Area Index to deduce crop growth stages of winter wheat (Triticum aestivum L.) particularly during �heading, tillering and booting. Further, to know the LAI variations at different agriculture sites, the best model was implemented using the AVIRIS-NG (Airborne Visible Near-Infrared Imaging Spectrometer - Next Generation) hyperspectral datasets. The analysis indicates that during tillering stage the performance was found best during calibration (r = 0.66, RMSE =0.40, and Bias =-0.80) and validation (r = 0.98, RMSE =0.20, and Bias =0.12) in comparison to the ground measurements. � 2022 Informa UK Limited, trading as Taylor & Francis Group.Publication Band selection algorithms for foliar trait retrieval using AVIRIS-NG: a comparison of feature based attribute evaluators(Taylor and Francis Ltd., 2022) Malhi, Ramandeep Kaur M.; Pandey, Manish Kumar; Anand, Akash; Srivastava, Prashant K.; Petropoulos, George P.; Singh, Prachi; Sandhya Kiran, G.; Bhattarcharya, B.K.Interband information overlapping enhances redundancy in hyperspectral data. This makes identification of application-specific optimal bands essential for obtaining accurate information about foliar traits. The current study investigated the performance of three novel Band Selection (BS) algorithms (i.e. the Chi-squared-statistics based attribute evaluator (CSS), the Recursive elimination of features-based attribute evaluator (REF) and the Correlation-based attribute subset evaluator (CBS)) in identifying the spectral bands of Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) from visible and Near Infrared (NIR) regions that are sensitive to variation in Chlorophyll Content (CC). Identified bands were employed to formulate Hyperspectral Indices (HIs) by incorporating combinations of Blue, Green, Red, and NIR regions. CC models were built by establishing a linear fit between ground CC and HIs. For all the three BS algorithms, optimum bands varied for visible and NIR regions. REF-HI (NIR,R), REF-HI(NIR,R + G), CSS-HI(NIR,R) and CSS-HI(NIR,R + G) had the best correlation with CC. HI(NIR,R) is identified as the best HI and REF the best BS algorithm for retrieving CC. � 2021 Informa UK Limited, trading as Taylor & Francis Group.Publication Belowground fungal volatiles perception in okra (Abelmoschus esculentus) facilitates plant growth under biotic stress(Elsevier GmbH, 2021) Singh, Jyoti; Singh, Prachi; Vaishnav, Anukool; Ray, Shatrupa; Rajput, Rahul Singh; Singh, Shiv Mohan; Singh, Harikesh BahadurMicrobial volatile organic compounds (mVOCs) have great potential in plant ecophysiology, yet the role of belowground VOCs in plant stress management remains largely obscure. Analysis of biocontrol producing VOCs into the soil allow detailed insight into their interaction with soil borne pathogens for plant disease management. A root interaction trial was set up to evaluate the effects of VOCs released from Trichoderma viride BHU-V2 on soil-inhabiting fungal pathogen and okra plant growth. VOCs released into soil by T. viride BHU-V2 inhibited the growth of collar rot pathogen, Sclerotium rolfsii. Okra plants responded to VOCs by increasing the root growth (lateral roots) and total biomass content. VOCs exposure increased defense mechanism in okra plants by inducing different enzyme activities i.e. chitinase (0.89 fold), ?-1,3-glucanase (0.42 fold), peroxidase (0.29 fold), polyphenol oxidase (0.33 fold) and phenylalanine lyase (0.7 fold) when inoculated with S. rolfsii. In addition, T. viride BHU-V2 secreted VOCs reduced lipid peroxidation and cell death in okra plants under pathogen inoculated condition. GC/MS analysis of VOCs blend revealed that T. viride BHU-V2 produced more number of antifungal compounds in soil medium as compared to standard medium. Based on the above observations it is concluded that okra plant roots perceive VOCs secreted by T. viride BHU-V2 into soil that involved in induction of plant defense system against S. rolfsii. In an ecological context, the findings reveal that belowground microbial VOCs may play an important role in stress signaling mechanism to interact with plants. � 2021Publication Delineation of groundwater potential zone and site suitability of rainwater harvesting structures using remote sensing and in situ geophysical measurements(wiley, 2021) Singh, Prachi; Anand, Akash; Srivastava, Prashant K.; Singh, Arjun; Pandey, Prem ChandraThe present situation of groundwater tables is falling at a rapid rate, because regular withdrawal of groundwater is high compared to the recharge rate. This study focuses on the delineation of a groundwater potential zone in Lalganj Ajhara block, District Pratapgarh, Uttar Pradesh, using in situ vertical electric survey data, remote sensing (RS), and geographic information system (GIS). Present research covers the spatial analysis of different thematic layers generated through satellite images and field data for the identification of suitable sites for rainwater harvesting structures within the study area. A Schlumberger array is generated through the geotechnical field survey and soil depth analysis is done for three cross-section profiles which are generated through the sample locations. Sentinel-2A data along with the digital elevation model (DEM) are used to prepare different thematic layers, and density analysis is done using GIS tools. The groundwater potential is estimated using multi-criteria overlay analysis and using in situ measurements. The site suitability analysis is done by estimating probable location of check dams, desilting points, dug well, and recharge pit within the study area. � 2021 John Wiley & Sons Ltd. All rights reserved.Publication Denoising AVIRIS-NG Data for Generation of New Chlorophyll Indices(Institute of Electrical and Electronics Engineers Inc., 2021) Singh, Prachi; Srivastava, Prashant K.; Malhi, Ramandeep Kaur M.; Chaudhary, Sumit K.; Verrelst, Jochem; Bhattacharya, Bimal K.; Raghubanshi, Akhilesh S.The availability of Airborne Visible and Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data has enormous possibilities for quantification of Leaf Chlorophyll Content (LCC). The present study used the AVIRIS-NG campaign site of Western India for generation and validation of new chlorophyll indices by denoising the AVIRIS-NG data. For validation, concurrent to AVIRIS-NG flight overpass, field samplings were performed. The acquired AVIRIS-NG was subjected to Spectral Angle Mapper (SAM) classifier for discriminating the crop types. Three smoothing techniques i.e., Fast-Fourier Transform (FFT), Mean and Savitzky-Golay filters were evaluated for their denoising capability. Raw and filtered data was used for developing new chlorophyll indices by optimizing AVIRIS-NG bands using VIs based on parametric regression algorithms. In total, 20 chlorophyll indices and corresponding 20 models were developed for mapping LCC in the area. SAM identified 17 crop types in the area, while FFT found to be the best for filtering. Performance of these models when checked based on Pearson correlation coefficient ( {r} ) and Centered Root Mean Square Difference (CRMSD), indicated that LCC-CCI10 based on normalized difference type index formed through Near Infrared band and blue band is the best estimator of LCC ({r}_{textit {cal}}=0.73,{r}_{textit {val}}=0.66,CRMSD=4.97). The approach was also tested using AVIRIS-NG image of the year 2018, which also showed a promising correlation ( {r} =0.704 , CRSMD = 8.98, Bias = -0.5) between modeled and field LCC. � 2001-2012 IEEE.Publication Development of hyperspectral indices for anti-cancerous Taxol content estimation in the Himalayan region(Taylor and Francis Ltd., 2022) Gupta, Ayushi; Singh, Prachi; Srivastava, Prashant K.; Pandey, Manish K.; Anand, Akash; Chandra Sekar, K.; Shanker, KarunaMonitoring and management of rare and economically important species in the highly complex terrain are challenging and thus need advanced technological development. In this study, the hyperspectral radiometer data of Taxus wallichiana were acquired at highly complex terrain of the Pindari region of the Himalaya and processed by using several sophisticated algorithms to deduce Taxol content in the plants. The spectroradiometer data were denoised through three different types of smoothing filters such as Average Mean, Savitzky Golay, and Fast Fourier Transform (FFT) followed by feature selection for allocation of best bands for Taxol content estimation. The results showed that the Average Mean filter in combination with feature selection performed best for Taxol spectral indices generation, model development, and Taxol content prediction. The best model showed a correlation of 0.719 with a relative root mean square error (RMSEr) value of 0.678 for Taxol content prediction. � 2022 Informa UK Limited, trading as Taylor & Francis Group.Publication Evaluation of Simulated AVIRIS-NG Imagery Using a Spectral Reconstruction Method for the Retrieval of Leaf Chlorophyll Content(MDPI, 2022) Verma, Bhagyashree; Prasad, Rajendra; Srivastava, Prashant K.; Singh, Prachi; Badola, Anushree; Sharma, JyotiThe leaf chlorophyll content (LCC) is a vital parameter that indicates plant production, stress, and nutrient availability. It is critically needed for precision farming. There are several multispectral images available freely, but their applicability is restricted due to their low spectral resolution, whereas hyperspectral images which have high spectral resolution are very limited in availability. In this work, hyperspectral imagery (AVIRIS-NG) is simulated using a multispectral image (Sentinel-2) and a spectral reconstruction method, namely, the universal pattern decomposition method (UPDM). UPDM is a linear unmixing technique, which assumes that every pixel of an image can be decomposed as a linear composition of different classes present in that pixel. The simulated AVIRIS-NG was very similar to the original image, and its applicability in estimating LCC was further verified by using the ground based measurements, which showed a good correlation value (R = 0.65). The simulated image was further classified using a spectral angle mapper (SAM), and an accuracy of 87.4% was obtained, moreover a receiver operating characteristic (ROC) curve for the classifier was also plotted, and the area under the curve (AUC) was calculated with values greater than 0.9. The obtained results suggest that simulated AVIRIS-NG is quite useful and could be used for vegetation parameter retrieval. � 2022 by the authors.Publication High resolution retrieval of leaf chlorophyll content over Himalayan pine forest using Visible/IR sensors mounted on UAV and radiative transfer model(Elsevier B.V., 2023) Singh, Prachi; Srivastava, Prashant K.; Verrelst, Jochem; Mall, R.K.; Rivera, Juan Pablo; Dugesar, Vikas; Prasad, RajendraForests play an essential role towards net primary productivity, biological cycles and provide habitat to flora & fauna. To monitor key physiological activities in forest canopies such as photosynthesis, respiration, transpiration, spatially-explicit and precise information of the biochemical (biological) variables such as Leaf Chlorophyll Content (LCC) is required. While lookup-table (LUT)-based Radiative Transfer Model (RTM) inversion against optical remote sensing imagery is regarded as a physically sound and robust approach for retrieving biochemical and biophysical variables, regularization procedures are required to offset the problem of ill-posedness. To optimize the RTM inversion of LCC over a sub-tropical pine forest plantation, in the Western Himalaya, we investigated the role of: (1) cost functions (CFs), (2) added noise, and (3) multiple finest solutions in LUT inversion. Principal CFs were evaluated belonging to three categories: information measures, M-estimates, and minimal contrast approaches. The inversion approaches were applied to a LUT produced by the coupled leaf-canopy model known as PROSAIL RTM and tested in contrast field spectral data obtained from reflectance data derived from UAV (Unmanned Aerial Vehicle) images taken over the canopies of covered pine forests. The Bhattacharyya divergence, an information measure, outperformed all other CFs in LCC inversion, with R2 of 0.94, RMSE of 6.20 ?g/cm2 and NRMSE of 12.27% during the validation. The optimized inversion strategy was subsequently applied to a UAV-acquired multispectral image at an 8.2 cm pixel resolution for detailed landscape forest LCC mapping. The associated residuals as provided by the LUT-based inversion provided insights in the spatial consistency of the LCC map. � 2023 Elsevier B.V.Publication Identification of Optimal Absorbance Spectral Bands from Aviris-Ng Using Standard Derivative Analysis(IEEE Computer Society, 2022) Singh, Prachi; Srivastava, Prashant K.; Mall, R.K.; Verma, Bhagyashree; Prasad, RajendraThe high dimensional hyperspectral data due to its narrow bands ranging between 250-3500 nm, becomes a serious issue for data processing and analysis. Selection of optimal absorbance spectral bands from original spectra brings great possibility in removing the redundancy, quantifying pigments such as chlorophyll, carotenoids, anthocyanin and the retrieval of biophysical variables such as LAI, Biomass corresponding to crops using advance derivative techniques. As part of this study atmospheric corrected reflectance AVIRIS-NG (Airborne Visible Infrared Imaging Spectroradiometer-Next Generation) hyperspectral sensor data over Anand study site on wheat crop were used. Two well proven techniques continuum removal and second derivative were used for the identification of absorption bands and also useful to capture the subtle difference in the spectra required in order to locate any specific feature present in the spectra. In the continuum removed graph, chlorophyll reflection is observed in the green region of electromagnetic spectrum, in red region a peak of reflection is observed due to the presence of carotenoid pigment, and in the NIR (Near - Infrared) region dip is observed due to the presence of anthocyanins pigment and Leaf area index. Therefore, findings of the study might be useful to separate and better identification of absorption bands of biophysical and biochemical parameters presence in crop spectra. which indicated that they can be used for species identification of any crop. � 2022 IEEE.Publication Influence of Seed Biopriming and Vermiwash Treatment on Tomato Plant's Immunity and Nutritional Quality upon Sclerotium rolfsii Challenge Inoculation(Springer, 2021) Rajput, Rahul Singh; Singh, Jyoti; Singh, Prachi; Vaishnav, Anukool; Singh, Harikesh BahadurTomato is an important nutritional vegetable crop and its nutrient contents are affected by both biotic and abiotic stresses. The main objective of this study was to determine the effect of seed biopriming with Trichoderma pseudokoningii BHUR2 and vermiwash treatment on nutrient content of tomato and defense response against Sclerotium rolfsii under heat stress condition. The combined application of T. pseudokoningii BHUR2 and vermiwash increased fresh weight of root (4.8-fold) and shoot (5.8-fold), dry weight of root (6.9-fold) and shoot (6.4-fold) and number of fruits per plant (4.2-fold) as compared to control under S. rolfsii inoculated condition. Plants treated with T. pseudokoningii BHUR2 and vermiwash exhibited higher defense response against S. rolfsii, mediated by higher activity of superoxide dismutase (3.57-fold), peroxidase (2.05-fold) and phenylalanine ammonia lyase (2.98-fold) enzymes and accumulation of total phenol content (5.35-fold) as compared to control plants. In addition, combined treatment was found to have a positive impact on nutritional status (N, P, K and Ca and lycopene, total soluble sugar and total protein) in tomato fruit. These results suggest potential of T. pseudokoningii BHUR2 and vermiwash in enhancing tomato immunity against S. rolfsii under heat stress condition, which was due to (1) induction in the antioxidant activity and phenylpropanoid pathway, which minimize oxidative damage and reduce pathogen infection and (2) significant improvement in nutrient content leads to better plant growth. The formulation of Trichoderma BHUR2 can be used for field application to mitigate heat stress in plants. � 2020, Springer Science+Business Media, LLC, part of Springer Nature.Publication Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms(Elsevier B.V., 2022) Verma, Bhagyashree; Prasad, Rajendra; Srivastava, Prashant K.; Yadav, Suraj A.; Singh, Prachi; Singh, R.K.With the availability of high-resolution data due to sensor technology advancement, it is now easier for researchers and scientists to detect or view the spectral variability of different crops. For this study, Leaf chlorophyll content (LCC) and Leaf area index (LAI) of the crops Maize (Zea mays), Mustard (Brassica), and pink Lentils (Lens esculenta) under different irrigation and fertilizer treatments have been analyzed. In total, rigorous assessment of 25-hyperspectral vegetation indices (VIs) at both leaf and canopy level for chlorophyll content, whereas 7- hyperspectral VIs for LAI at canopy level were computed to investigate the robustness of these VIs for LCC and LAI assessment. Variable importance in projection (VIP) using Partial Least Square regression (PLSR) and coefficient of determination (R2) were computed for all the VIs to extract the most sensitive information for the retrieval of LCC and LAI. As a result, the VIs using the red-edge reflectance bands at 705 and 750 nm were found highly responsive to LAI compared to other wavebands. In contrast, the VIs indices made of green (550 nm), red (670, 690, and 700 nm), and red-edge (705, 750 nm) bands were found highly sensitive to the temporal LCC values of lentils and maize crop beds. In addition, the temporal LCC values of Mustard crop beds� were found sensitive to the VIs made of green (550 nm), red (670, 690, and 700 nm), and NIR (800 nm) wavebands. The three VIs having high VIP and R2 values were selected as optimum sets of input to build support vector regression models using radial (SVR-Rad), linear (SVR-Li), polynomial (SVR-Poly), Random Forrest Regression (RFR), Partial least square regression (PLSR), and Hybrid neural fuzzy inference system (HyFIS). The analysis showed that the SVR-Rad model outperformed the SVR-Li, SVR-Poly, RFR, PLSR, and HyFIS models in terms of robustness for biophysical and biochemical parameters retrieval using hyperspectral data. � 2021Publication Killed fungal pathogen triggers antifungal metabolites in Alcaligenes faecalis for plant defense(Academic Press, 2023) Ray, Shatrupa; Singh, Prachi; Singh, Jyoti; Singh, Surendra; Sarma, Birinchi Kumar; Singh, Harikesh BahadurSclerotium rolfsii is a broad host necrotrophic pathogen causing serious damages in crop yields. Apart from chemical fungicides being used to control this pathogen, no bio-fungicides have been reported till date. In this study, we have tried to utilize heat killed S. rolfsii hyphae for triggering biosynthesis of antifungal compounds in the endophytic bacterium Alcaligenes faecalis BHU 12. The endophytic bacterial cell free supernatant (CFS) obtained by growing BHU 12 in presence of freeze-crushed and autoclaved S. rolfsii hyphae caused prominent S. rolfsii hyphal degeneration and inhibition of sclerotial germination under in vitro conditions. This result was further corroborated under in planta conditions wherein spraying of the CFS at the point of infection inhibited further pathogen ingress. This observation was justified by the presence of gallic and shikimic acid in the CFS which served as antifungal agent and augmentor of plant defense system respectively. Infected plants sprayed with the CFS were found to display a prominent increase in phenylalanine ammonia lyase activity and a corresponding increase in total phenolics. In this context, our results described the possible alternative of using endophytic bacterial metabolite compounds as biofungicides. A simultaneous augmentation in seed germination upon treatment with the CFS suggests a possibility of using endophytic bacterial supernatants as biocontrol and biofertilizer alternative instead of whole bacterial cell since indigenous soil microbiota combined with cumbersome inoculation techniques prevents successful colonization of endophytic microbes in planta. � 2023 Elsevier LtdPublication Optimal band characterization in reformation of hyperspectral indices for species diversity estimation(Elsevier Ltd, 2022) Anand, Akash; Malhi, Ramandeep Kaur M.; Srivastava, Prashant K.; Singh, Prachi; Mudaliar, Ashwini N.; Petropoulos, George P.; Kiran, G. SandhyaSpecies diversity quantification is a crucial step towards the biodiversity conservation and ecosystem health. The technological advancements and existing limitations of multispectral remote sensing has increased the popularity of hyperspectral remote sensing which found its use in the estimation of species diversity. The contiguous narrow bands available in hyperspectral data enables the improvised assessment of diversity index but the overlapping of the information could result in the redundancy that needs to be handled. Due to this, the idenfication of optimal bands is very important; hence, the current study provides modified hyperspectral indices through detection of optimum bands for estimating species diversity within Shoolpaneshwar Wildlife Sanctuary (SWS), India. Narrow hyperspectral bands of EO-1 Hyperion image were screened and the best optimum wavelength from visible and Near Infrared (NIR) regions were identified based on coefficient of determination (r2) between band reflectance and in situ measured species diversity. For in situ species diversity measurements, quadrat sampling was carried out in SWS and different Diversity Indices (DIs) namely the Shannon Weiner DI, Margalef DI, McIntosh DI and Brillouin DI were calculated. The identified optimum wavelengths were then employed for modifying 38 existing spectral indices which were then investigated for testing their relation with the in situ DIs. The obtained optimum bands in visible and NIR regions were found to be in correspondence with four DIs. Among several indices used in this study, during validation, modified Non-linear index, modified Red Edge Position Index, modified Structure Insensitive Pigment Index and modified Red Green Ratio Index were identified as the best hyperspectral indices for determining Shannon Weiner DI, Margalef DI, McIntosh DI and Brillouin DI, respectively. � 2021 Elsevier LtdPublication Retrieval of Leaf Area Index Using Inversion Algorithm(IEEE Computer Society, 2022) Verma, Bhagyashree; Prasad, Rajendra; Srivastava, Prashant K.; Singh, PrachiWith the development in sensor technology, there is a spectroradiometer with resolution as high as 1nm and data capture extending from 350nm-2500nm; it helps in viewing spectral variability of the subject of interest. The advantage of such instruments opens up many opportunities for the development of hyperspectral data analysis in precision agriculture. In the presented work, estimation of Leaf Area Index (LAI) is done with inversion technique using Transformed Vegetation Index (TVI), SR (Simple Ratio), NDVI (Normalized difference ratio index) vegetation indices as input parameters, and modeled LAI separately for these three indices. The estimation was done for different growth stages of Maize (Zea mays), Mustard (Brassica), pink Lentils (Lens esculenta), and Wheat (Triticum). A comprehensive comparative analysis was done based on the value of R2. For the variation in LAI, the SR index gave the highest correlation for lentils (R2=0.9329), Mustard (R2=0.893), and wheat (R2=0.9712) whereas, for Maize, NDVI was found to be the best estimator with a correlation of (R2=0.7781). � 2022 IEEE.Publication Spectroradiometry: Types, data collection, and processing(wiley, 2021) Pandey, Prem Chandra; Pandey, Manish Kumar; Gupta, Ayushi; Singh, Prachi; Srivastava, Prashant K.Spectroradiometry has gained popularity over conventional techniques and is now used in numerous fields, such as in hyperspectral remote sensing. Spectroradiometry allows the non-destructive sampling of objects for retrieval of biochemical and biophysical properties to provide the user with critical information more quickly and cheaply. This is facilitated by compilation of these signatures in a database and can further be utilized in the retrieval of relevant information. Hyperspectral imaging technology, which is also based on spectroradiometry, is used in the retrieval of spectral characteristics of surface features at the synoptic scale. This chapter reviews spectroradiometer types, data collection procedures, and their processing, with some examples. � 2021 John Wiley & Sons Ltd. All rights reserved.Publication Statistical unfolding approach to understand influencing factors for taxol content variation in high altitude himalayan region(MDPI, 2021) Gupta, Ayushi; Srivastava, Prashant K.; Petropoulos, George P.; Singh, PrachiTaxol drugs can be extracted from various species of the taxaceae family. It is an alkaloid (metabolic product) used for the treatment of various types of cancer. Since taxol is a metabolic product, multiple aspects such as edaphic, biochemical, topographic factors need to be assessed in determining the variation in Taxol Content (TC). In this study, both sensor-based hyperspectral reflectance data and absorption-based indices were tested together for the development of an advanced statistical unfolding approach to understand the influencing factors for TC in high altitude Himalayan region. Seriation analysis based on permutation matrix was applied with complete linkage and a multi-fragment heuristic scaling rule along with the common techniques such as Principal Component Analysis (PCA) and correlation to understand the relationship of TC with various factors. This study also tested the newly developed taxol indices to rule out the possibility of overlapping of TC determining bands with the foliar pigment�s wavelengths in the visible region. The result implies that T. wallichiana with a high TC is found more in its natural habitat of deep forest, relating it indirectly to elevation in the case of the montane ecosystem. Taxol is the most varying parameter among the measured variables, followed by hyperspectral Taxol content (TC) indices such as TC 2, TC 5, and carotenoids, which suggests that the indices are well versed to capture variations in TC with elevation. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.Publication Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India(Elsevier Ltd, 2022) Malhi, Ramandeep Kaur M.; Anand, Akash; Srivastava, Prashant K.; Chaudhary, Sumit K.; Pandey, Manish K.; Behera, Mukund Dev; Kumar, Amit; Singh, Prachi; Sandhya Kiran, G.Spatially explicit measurement of Above Ground Biomass (AGB) is crucial for the quantification of forest carbon stock and fluxes. To achieve this, an integration of Optical and Synthetic Aperture Radar (SAR) satellite datasets could provide an accurate estimation of forest biomass. This will also help in removing the uncertainties associated with the single sensor-based estimation approaches. Therefore, the present study attempts to integrate Sentinel-2 optical data with Sentinel-1 SAR dataset to estimate AGB in the Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. In this study, two non-parametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions�linear, sigmoidal, radial and polynomial and Random Forest (RF) were employed for the prediction of AGB using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA). Ground based AGB was estimated through allometric equation at 35 sampling sites with the help of tree height and Diameter at Breast's Height (DBH). Standalone collinearity analysis among different parameters resulted in poor correlation of AGB with VH (r = 0.05) and IA (r = 0.015), whereas a significantly good correlation with NDVI (r = 0.80) and VV (r = 0.74) were observed. Inclusion of NDVI with VV and VH together also resulted in a better correlation (r = 0.85) than other combinations. The SVM with linear kernel utilizing parametric the combinations of VV + VH + NDVI and VV + VH + NDVI + IA were found to be best performing on the basis of evaluation metrics. The outcome of this study highlighted the significance of machine learning techniques and synergistic use of different remote sensing data for an improved AGB quantification in tropical forests. � 2021
