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This article presents the refractive index (RI) sensitivity study of cladding reduced uniform long-period fiber grating. The displacement of resonant wavelength with the change in ambient RI is observed to inspect the sensitivity. Here, the grating period is fixed at a value (550 μm) to analyze RI sensitivity of cladding modes (HE13-HE14). However, it varies in earlier mathematical studies to adjust the resonant wavelength of all modes at a specific wavelength. The three-layer fiber geometry-based mathematical approach is employed to estimate the core and cladding modes. The obtained results support decline in quantity of cladding modes sustained by a single-mode fiber. The decrease in cladding radius has shown enhancement in RI sensitivity. For a reduction of 22 μm in cladding radius, the highest ordered HE14 cladding mode has shown 5.8 times improvement in its sensitivity. [ABSTRACT FROM AUTHOR]

Remote sensing is one of the most important methods for analysing the multitemporal changes over a certain period. As a cost-effective way, remote sensing allows the long-term analysis of agricultural land by collecting satellite imagery from different satellite missions. Landsat is one of the longest-running world missions which offers a moderate-resolution earth observation dataset. Land surface mapping and monitoring are generally performed by incorporating classification and change detection models. In this work, a deep learning-based change detection (DCD) algorithm has been proposed to detect long-term agricultural changes using the Landsat series datasets (i.e., Landsat-7, Landsat-8, and Landsat-9) during the period 2012 to 2023. The proposed algorithm extracts the features from satellite data according to their spectral and geographic characteristics and identifies seasonal variability. The DCD integrates the deep learning-based (Environment for visualizing images) ENVI Net-5 classification model and posterior probability-based post-classification comparison-based change detection model (PCD). The DCD is capable of providing seasonal variations accurately with distinct Landsat series dataset and promises to use higher resolution dataset with accurate results. The experimental result concludes that vegetation has decreased from 2012 to 2023, while build-up land has increased up to 88.22% (2012–2023) for Landsat-7 and Landsat-8 datasets. On the other side, degraded area includes water (3.20–0.05%) and fallow land (1–0.59%). This study allows the identification of crop growth, crop yield prediction, precision farming, and crop mapping. [ABSTRACT FROM AUTHOR]

The mountain systems of the Himalayan regions are changing rapidly due to climatic change at a local and global scale. The Indian Western Himalaya ecosystem (between the tree line and the snow line) is an underappreciated component. Yet, knowledge of vegetation distribution, rates of change, and vegetation interactions with snow-hydroclimatic elements is lacking. The purpose of this study is to investigate the linkage between the spatiotemporal variability of vegetation (i.e., greenness and forest) and related snow-hydroclimatic parameters (i.e., snow cover, land surface temperature, Tropical Rainfall Measuring Mission (TRMM), and Evapotranspiration (ET)) in Himachal Pradesh (HP) Basins (i.e., Beas, Chandra, and Bhaga). Spatiotemporal variability in forest and grassland has been estimated from MODIS land cover product (MCD12Q1) using Google Earth Engine (GEE) for the last 19 years (2001–2019). A significant inter- and intra-annual variation in the forest, grassland, and snow-hydroclimatic factors have been observed during the data period in HP basins (i.e., Beas, Chandra, and Bhaga basin). The analysis demonstrates a significant decrease in the forest cover (214 ha/yr.) at the Beas basin; however, a significant increase in grassland cover is noted at the Beas basin (459 ha/yr.), Chandra (176.9 ha/yr.), and Bhaga basin (9.1 ha/yr.) during the data period. Spatiotemporal forest cover loss and gain in the Beas basin have been observed at ~7504 ha (6.6%) and 1819 ha (1.6%), respectively, from 2001 to 2019. However, loss and gain in grassland cover were observed in 3297 ha (2.9%) and 10,688 ha (9.4%) in the Beas basin, 1453 ha (0.59%) and 3941 ha (1.6%) in the Chandra basin, and 1185 ha (0.92%) and 773 ha (0.60%) in the Bhaga basin, respectively. Further, a strong negative correlation (r = −0.65) has been observed between forest cover and evapotranspiration (ET). However, a strong positive correlation (r = 0.99) has been recorded between grassland cover and ET as compared to other factors. The main outcome of this study in terms of spatiotemporal loss and gain in forest and grassland shows that in the Bhaga basin, very little gain and loss have been observed as compared to the Chandra and Beas basins. The present study findings may provide important aid in the protection and advancement of the knowledge gap of the natural environment and the management of water resources in the HP Basin and other high-mountain regions of the Himalayas. For the first time, this study provides a thorough examination of the spatiotemporal variability of forest and grassland and their interactions with snow-hydroclimatic factors using GEE for Western Himalaya. [ABSTRACT FROM AUTHOR]

Glaciers and snow are critical components of the hydrological cycle in the Himalayan region, and they play a vital role in river runoff. Therefore, it is crucial to monitor the glaciers and snow cover on a spatiotemporal basis to better understand the changes in their dynamics and their impact on river runoff. A significant amount of data is necessary to comprehend the dynamics of snow. Yet, the absence of weather stations in inaccessible locations and high elevation present multiple challenges for researchers through field surveys. However, the advancements made in remote sensing have become an effective tool for studying snow. In this article, the snow cover area (SCA) was analysed over the Beas River basin, Western Himalayas for the period 2003 to 2018. Moreover, its sensitivity towards temperature and precipitation was also analysed. To perform the analysis, two datasets, i.e., MODIS-based MOYDGL06 products for SCA estimation and the European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Global Climate (ERA5) for climate data were utilized. Results showed an average SCA of ~56% of its total area, with the highest annual SCA recorded in 2014 at ~61.84%. Conversely, the lowest annual SCA occurred in 2016, reaching ~49.2%. Notably, fluctuations in SCA are highly influenced by temperature, as evidenced by the strong connection between annual and seasonal SCA and temperature. The present study findings can have significant applications in fields such as water resource management, climate studies, and disaster management. [ABSTRACT FROM AUTHOR]

In the present work, the scatterometer satellite (SCATSAT-1) has been implemented and validated to provide the near-real-time estimation of snow cover area (SCA) in the Western Himalayas, India. The SCA derived from standalone SCATSAT-1 L4 (Level-4 India) products, i.e. sigma-nought (σ°), and gamma-nought (γ°) has been validated with different MODIS products individually, i.e. MOD02 L1B (calibrated-radiances) derived NDSI (normalized difference snow index), MOD10A1 L3 (daily composite snow cover), and MOD10A2 L3 (8-Day composite snow cover). The experimental outcomes confirm the potential of SCATSAT-1 in estimating the SCA with respect to other MODIS products and also, suggested the utilization of the different MODIS products for referencing/validation in different scenarios. The findings of this paper suggest that SCATSAT-1 offers the near real-time mapping and monitoring of large-scale snow extent at the global level even under cloudy conditions. [ABSTRACT FROM AUTHOR]

Continuous observation and management of agriculture are essential to estimate crop yield and crop failure. Remote sensing is cost-effective, as well as being an efficient solution to monitor agriculture on a larger scale. With high-resolution satellite datasets, the monitoring and mapping of agricultural land are easier and more effective. Nowadays, the applicability of deep learning is continuously increasing in numerous scientific domains due to the availability of high-end computing facilities. In this study, deep learning (U-Net) has been implemented in the mapping of different agricultural land use types over a part of Punjab, India, using the Sentinel-2 data. As a comparative analysis, a well-known machine learning random forest (RF) has been tested. To assess the agricultural land, the major winter season crop types, i.e., wheat, berseem, mustard, and other vegetation have been considered. In the experimental outcomes, the U-Net deep learning and RF classifiers achieved 97.8% (kappa value: 0.9691) and 96.2% (Kappa value: 0.9469), respectively. Since little information exists on the vegetation cultivated by smallholders in the region, this study is particularly helpful in the assessment of the mustard (Brassica nigra), and berseem (Trifolium alexandrinum) acreage in the region. Deep learning on remote sensing data allows the object-level detection of the earth's surface imagery. [ABSTRACT FROM AUTHOR]

Glacier, snow, and ice are the essential components of the Himalayan cryosphere and provide a sustainable water source for different applications. Continuous and accurate monitoring of glaciers allows the forecasting analysis of natural hazards and water resource management. In past literature, different methodologies such as spectral unmixing, object-based detection, and a combination of various spectral indices are commonly utilized for mapping snow, ice, and glaciers. Most of these methods require human intervention in feature extraction, training of the models, and validation procedures, which may create bias in the implementation approaches. In this study, the deep learning classifier based on ENVINet5 (U-Net) architecture is demonstrated in the delineation of glacier boundaries along with snow/ice over the Bara Shigri glacier (Western Himalayas), Himachal Pradesh, India. Glacier monitoring with Landsat data takes the advantage of a long coverage period and finer spectral/spatial resolution with wide coverage on a larger scale. Moreover, deep learning utilizes the semantic segmentation network to extract glacier boundaries. Experimental outcomes confirm the effectiveness of deep learning (overall accuracy, 91.89% and Cohen's kappa coefficient, 0.8778) compared to the existing artificial neural network (ANN) model (overall accuracy, 88.38% and kappa coefficient, 0.8241) in generating accurate classified maps. This study is vital in the study of the cryosphere, hydrology, agriculture, climatology, and land-use/land-cover analysis. [ABSTRACT FROM AUTHOR]

Change detection is one of the vital ways to analyse the multitemporal variations over a specified period using remote sensing data. In recent years, deep learning (DL) algorithms have become the choice of many remote sensing researchers to solve the problems of conventional change detection methods and to improve their accuracy. In the present work, the DL classifier has been incorporated with the post-classification comparison (PCC), named DL-based change detection (DLCD), to extract the features from satellite imagery based on their spatial and spectral properties and detect the seasonal variability. For demonstration purposes, the dataset has been acquired over the agricultural land in Punjab State, India, using the Sentinel-2 optical dataset during the period 2017–2018. Due to the climatology of Punjab, this region is well-suited for wheat cultivation. Therefore, we have computed the change maps for the rabi seasonal crop (wheat) which is planted usually in October and grows throughout the winter season to be harvested in the spring season (April). To confirm the effectiveness of the proposed approach, the performance of DLCD has been cross-validated with random forest (RF)–based PCC, convolutional neural network (CNN)–based PCC and support vector machine (SVM)–based PCC. Experiential outcomes have shown that DLCD achieved a higher accuracy (94.8–97.2% in classified maps and 91.8–95% in change maps) as compared to the RF-PCC (87.6–90.2% in classification and 88–89.4% in change maps), CNN-PCC (90.4–93.4% in classified maps and 87.4–90% in change maps) and SVM-PCC (86–88.8% in classified maps and 86–88.8% in change maps). This study can be significant in terms of extraction of various crop types, water surfaces and manmade features, as well as various land-use patterns using DLCD. [ABSTRACT FROM AUTHOR]

Earth observation via optical-based remote sensing is one of the effective solutions to cover the large swath and to deliver the very high-resolution dataset at the different wavelengths. But the applicability of optical imaging is limited by daytime only and adversely affected by the presence of clouds. In such scenarios, microwave data is more preferable due to the potential of penetrating through the clouds. Recently launched (26 September 2016) scatterometer satellite (SCATSAT-1) data by the Indian Space Research Organization (ISRO) has the potential of providing all-weather, day-night monitoring and daily data-delivery services at the global level. Along with the numerous advantages, the Ku-band (13.535 GHz) based SCATSAT-1 cannot provide sufficient information as provided by the multispectral optical sensors. Therefore, in the present work, the microwave-based SCATSAT-1 and optical-based MODIS (moderate resolution imaging spectroradiometer) have been fused using the nearest-neighbour approach to examine its effects in cloud removal and its applications in classification. The study has been performed over Himachal Pradesh, India. This study has also discussed the impact of different classifiers such as artificial neural network (ANN), spectral angle mapper (SAM), support vector machine (SVM), and random forest (RF), on the fusion of SCATSAT-1 (including backscattered coefficients, i.e. sigma-nought and gamma-nought at HH and VV polarizations) and MODIS dataset. Experimental results have confirmed that the accuracy of implemented classified maps significantly increases with the fusion of both datasets as compared to the individual implementation of SCATSAT-1- and MODIS-classified maps. From quantitative analysis, the RF classifier performs better as compared to other classifiers, i.e. ANN, SAM, and SVM on the fused dataset. This study has many applications in the near real-time monitoring of snow/ice, agriculture activities, and hydrological studies. [ABSTRACT FROM AUTHOR]

For ambient refractive index (ARI) higher than cladding region, the refractive index (RI) sensitivity of long-period fiber grating (LPFG) measured in terms of the shift in the cladding mode resonance wavelength (CMRW) is very low. This may be due to the occurrence of a small CMRW shift with a change in ARI. This paper emphasizes the effect of cladding radius reduction as well as cladding mode-order over the spectral behavior, RI response and figure of merit of LPFG for such ARIs. Therefore, mathematical approaches designed on three-layer and two-layer fiber geometries are employed on single-mode optical fiber (SMF28e) to estimate optical modes. The influence of cladding radius reduction (from 62.5 to 32.5 µm) on spectral profile and RI sensitivity is analyzed for three cladding modes (HE13, HE14 and HE15). The spectral behavior of all three modes for the ARI range (1.458–1.738) has described the shifting of CMRW towards longer wavelengths. From the analysis, the larger shifts in CMRW with ARIs have been shown by HE15 cladding mode at a cladding radius of 32.5 µm as compared to normal cladding LPFG. Moreover, the spectral bands of ARIs near to cladding index have shown very low differentiation in attenuation spectrum depths at reduced cladding radius. [ABSTRACT FROM AUTHOR]

The response of a long‐period fiber grating (LPFG) based refractometric sensor is directly related to the optical modes' effective index of refraction (EIR). In this paper, the quantitative behavior of EIR is investigated with LPFG surrounded dielectric medium's refractive index (RI) higher than cladding RI and cladding radius. To analyze the impact on EIR, the 2‐layer and 3‐layer fiber structure‐based mathematical models have been introduced. In the past literature, the interaction of the ambient medium with the core mode field is rarely investigated. Therefore, such studies have been considered and as a result, a significant impact on core mode EIR has been observed. The influence of ambient‐medium RI (ARI = 1.458–1.738) on EIR is found ascended for its value near to cladding RI at normal cladding radius. However, the range of influencing ARIs has been extended with mode order and cladding radius reduction. This study encourages the measuring of LPFG RI response for the aforesaid ARI range in terms of shift in coupled mode resonance wavelength and improves the precision in sensing ARI. [ABSTRACT FROM AUTHOR]

The present study evaluates the potential of Ku-band Scatterometer Satellite-1 (SCATSAT-1) for quantification of spatiotemporal variability in snow cover area (SCA) over Himalayas (Himachal Pradesh) India. The SCA has been measured using dual-polarized (HH and VV) backscattered SCATSAT-1 data. Two classification approaches, i.e., Linear Mixer Model (LMM) and Artificial Neural Network (ANN) model have been used for the present study. Both available backscatter coefficients sigma-naught σ 0 and gamma-naught γ 0 have been considered for the estimation of SCA. To compute the seasonal snow cover trends for winter (2016‒2017 and 2017‒2018), a post-classification comparison (PCC) based change detection approach has been demonstrated on the classified dataset (LMM and ANN). The SCA maps have been validated using reference snow cover maps generated from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor. The final change-category maps have effectively mapped the snow cover variations with accuracy in between 83.01% and 95.33%. The results indicate the suitability of SCATSAT-1 for estimating the magnitude of snow extent over the Himalayas. [ABSTRACT FROM AUTHOR]

Continuous and accurate monitoring of earth surface changes over rugged terrain Himalayas is important to manage natural resources and mitigate natural hazards. Conventional techniques generally focus on per-pixel based processing and overlook the sub-pixel variations occurring especially in case of low or moderate resolution remotely sensed data. However, the existing subpixel-based change detection (SCD) models are less effective to detect the mixed pixel information at its complexity level especially over rugged terrain regions. To overcome such issues, a topographically controlled SCD model has been proposed which is an improved version of widely used per-pixel based change vector analysis (CVA) and hence, named as a subpixel-based change vector analysis (SCVA). This study has been conducted over a part of the Western Himalayas using the advanced wide-field sensor (AWiFS) and Landsat-8 datasets. To check the effectiveness of the proposed SCVA, the cross-validation of the results has been done with the existing neural network-based SCD (NN-SCD) and per-pixel based models such as fuzzy-based CVA (FCVA) and post-classification comparison (PCC). The results have shown that SCVA offered robust performance (85.6%–86.4%) as compared to NN-SCD (81.6%–82.4%), PCC (79.2%–80.4%), and FCVA (81.2%–83.6%). We concluded that SCVA helps in reducing the detection of spurious pixels and improve the efficacy of generating change maps. This study is beneficial for the accurate monitoring of glacier retreat and snow cover variability over rugged terrain regions using moderate resolution remotely sensed datasets. [ABSTRACT FROM AUTHOR]

The Ku-band (13.5 GHz) based scatterometer is the main sensor onboard Scatterometer Satellite (SCATSAT-1) launched on 26th September 2016 by Indian Space Research Organization (ISRO). The SCATSAT-1 satellite sensor provides daily updates on the conditions of atmospheric, oceanographic, agriculture and cryospheric parameters. Moreover, it delivers data products (Level 1-4) in form of different parameters (Sigma-naught $\sigma 0$ , Gamma-naught $\gamma 0$ , brightness temperature BT, wind vectors and velocity) at two different polarization modes (HH and VV). Since launch, several studies have been carried out to explore the potential of SCATSAT-1 satellite sensor for remote observation of the ocean as well as the land surface at the global level. Besides the conventional applications in weather and oceanic domains which are based on wind vector data, emerging applications over land use and land cover are also introduced. This paper aims to address the current status of SCATSAT-1 applications in different scientific domains such as oceanographic, cryospheric, agriculture and land hydrology. It is expected that such an extensive exploration of the applications of SCATSAT-1 satellite sensor will provide important insights for future utilization of scatterometer data. [ABSTRACT FROM AUTHOR]

In recent years, the significant increase in research on spatial information is observed. Classification or clustering is one of the well-known methods in spatial data analysis. Traditionally, classifiers are generally based on per-pixel approaches and are not utilizing the spatial information within pixel, called mixels which is an important source of information to image classification. There are two foremost reasons behind the existence of mixels: (a) coarse or low spatial resolution of sensor and (b) topographic effects that recorded on optical satellite imagery due to differential terrain illuminations over rugged areas such as Himalayas. In the present study, different classification algorithms have been implemented to drive the impact of topography on them. Among various available, three algorithms for the mapping of snow cover region over north Indian Himalayas (India) are compared: (a) maximum likelihood classification (MLC) as supervised classifier; (b) k-mean clustering as unsupervised classifier; and (c) linear spectral mixing model (LSMM) as soft classifier. These algorithms have been implemented on AWiFS multispectral data, and analysis was carried out. The classification accuracy is estimated by the error matrices, and LSMM achieved higher accuracy (84.5-88.5%) as compared to MLC (81-84%) and k-mean (74-81%). The results highlight that topographically derived classifiers achieved better accuracy in mapping as compared to simple classifiers. The study has many applications in snow hydrology, glaciology and climatology of mountain topography. [ABSTRACT FROM AUTHOR]

Remote sensing plays a significant role in monitoring of the undulating the Himalayas. With continuous monitoring, the preservation of natural resources and mitigation of natural hazards is possible. Currently, satellite sensors are not capable enough to deliver the earth surface image at a very high temporal, spectral, and spatial resolution, simultaneously. Therefore, it is essential to perform the pan-sharpening of spatially high-resolution (HR) panchromatic (PAN) spectral band with low-resolution (LR) multispectral (MS) imagery which must be acquired on the same temporal date from multiple sensors. On the other hand, due to the rugged topography of the Himalayas, topographic effects are generally induced in the form of shadow and affect the spatial information or spectral information. Therefore, the focus of the present work is to implement and analyze the performance of topographic correction on nearest-neighbor diffusion (NND)-based pan-sharpening algorithm. In order to evaluate the effectiveness, K-mean classification (KMC) has been implemented over topographically corrected and topographically uncorrected NND pan-sharpened images. From the experimental outcomes, it is inferred that topographically corrected NND pan-sharpened classified image has achieved better accuracy (81.33%) as compared with topographically uncorrected NND pan-sharpened classified imagery (78%). It is expected that the integration of topographic correction and NND algorithm facilitates the better extraction of spectral and spatial information and leads to improvement in results for further analysis such as change detection procedure and classification. The applications of the present study are in monitoring of climate change and mapping land cover changes over rugged terrain regions. [ABSTRACT FROM AUTHOR]