Improved Assessment of Atmospheric Water Vapor Content in the Himalayan Regions Around the Kullu Valley in India Using Landsat‐8 Data

Varade, Divyesh; Dikshit, Onkar

doi:10.1029/2018wr023806

Cited by 13 publications

(4 citation statements)

References 36 publications

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“…NDVI is a very sensitive indicator to detect changes in vegetation (Bhardwaj et al 2016;Varade and Dikshit 2019). Figure 6 shows the distribution of NDVI for three years in Srinagar city.…”

Section: Assessment Of Ndvi and Ndwimentioning

confidence: 99%

Understanding the Linkages Between Spatio-temporal Urban Land System Changes and Land Surface Temperature in Srinagar City, India, Using Image Archives from Google Earth Engine

Murtaza

Shafai

Peer

et al. 2022

Preprint

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Land-use and land-cover (LULC) is a necessary component of sustainable natural resource management. There are considerable negative effects on the environment, ecosystem services, and natural resources as a result of the rapid anthropogenic land system changes. One of the significant negative implications of the rapidly changing urban LULC is the increase in the Land Surface Temperature (LST) resulting in the urban heat island effect. In this study, we used a time series of Landsat satellite images from 1992 to 2020 in the Srinagar city of the Kashmir valley, North-western Himalayas India to understand the linkages between LULC dynamics and Land Surface Temperature (LST), derived from the archived images using the Google Earth Engine (GEE). Furthermore, the relation between LST and biophysical indices, i.e., Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI) was also analysed. LULC change detection analysis revealed that the built-up area has increased significantly, while the vegetation cover and water bodies extent has decreased during the study period. The results showed that the built-up in the city has increased significantly from 12% in 1992 to 40% in 2020, the corresponding plantation has decreased from 26–17% in 2020, Forests have decreased from 4–2% and waterbody extent has declined from 6–4%2 in 28 years. Urban development of Srinagar city has resulted in the depletion of natural land covers, modification of natural drainage, and loss of green and blue spaces over the past few decades. The study revealed that the maximum LST in the city has increased by 11°C from 1992 to 2020. During the same period of time, the minimum LST in the city has increased by 4°C, indicating the impact of urbanization on the city environment. It was found that the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), two important urban environmental indices are negatively correlated with LST. The findings shall serve as a vital source of knowledge for urban planners and decision-makers to better develop sustainable urban environmental management strategies for Srinagar city.

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“…NDVI is a very sensitive indicator to detect changes in vegetation (Bhardwaj et al 2016;Varade and Dikshit 2019). Figure 6 shows the distribution of NDVI for three years in Srinagar city.…”

Section: Assessment Of Ndvi and Ndwimentioning

confidence: 99%

Understanding the Linkages Between Spatio-temporal Urban Land System Changes and Land Surface Temperature in Srinagar City, India, Using Image Archives from Google Earth Engine

Murtaza

Shafai

Peer

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…A rural aerosol and midlatitude winter atmospheric model was considered for the atmospheric correction (Cooley et al, 2002;Cao et al, 2012;Li et al, 2018). The reference snow cover maps were derived by thresholding the normalized NDSI images (Hulley et al, 2015;Varade & Dikshit, 2018), and the thresholds (th NDSI ), shown in Table 1, were selected based on the observation of the histogram of these images (Gascoin et al, 2018(Gascoin et al, , 2019Hall et al, 1987;Varade & Dikshit, 2019a, 2019b. The percentage of snow cover area based on these maps after masking for cloud, layover, and shadow is also shown in Table 1.…”

Section: 1029/2019wr025449mentioning

confidence: 99%

Identification of Snow Using Fully Polarimetric SAR Data Based On Entropy and Anisotropy

Varade

Singh

Dikshit

et al. 2020

Water Resources Research

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Recently, with the extensive availability of fully polarimetric synthetic aperture radar (SAR) data, methods that are simple and efficient, and involve lesser computation and data processing, are needed to be explored for snow cover mapping. This paper analyzes different polarimetric parameters such as entropy, anisotropy, and the mean scattering angle for the identification of snow cover area. We present a novel index for mapping snow cover based on the assessment of entropy (H) and anisotropy (A) using fully polarimetric SAR data and refer to it as the radar snow fraction (RSF). The RSF is proposed as an extension of the H(1‐A) metric by applying a sigmoidal function to this metric. The experiments to evaluate the applicability of the proposed RSF are carried out using fully polarimetric SAR data of L‐band ALOS‐2/PALSAR‐2 and C‐band RADARSAT‐2 data sets corresponding to different geographical locations in the Indian Himalayas. The developed snow cover maps from the proposed method were validated with respect to reference snow cover maps derived by thresholding the Normalized Differenced Snow Index developed from multispectral data (e.g., Landsat‐8 imagery). These maps were also statistically compared with those obtained from the conventional radar snow index, which is based on the polarization fraction. We determined a mean overall accuracy of 0.8 between the developed snow cover maps and the reference maps for the different data sets used for experiments. The results showed that, in general, the RSF outperformed the other polarimetric parameters for snow cover detection.

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“…The surface types in different regions are different, so the retrieval method obtained by using statistical methods is mainly suitable for local regions. Although the physical method is based on energy balance and has high retrieval accuracy [27,28], it requires many parameters. Due to the insufficient information for remote sensing observation, the number of unknowns is more than the number of equations, which forms an "ill-conditioned" problem.…”

Section: Introductionmentioning

confidence: 99%

“…−WVC r (g/cm 2 )LST and LSE+4BTs(27,29,31,32) LST and LSE+4BTs(28,29,31,32) LST and LSE+4BTs(27,28,31,32) 4BTs(27,29,31,32) 4BTs(28,29,31,32) 4BTs(27,28,31,32) …”

mentioning

confidence: 99%

A Novel Physics-Statistical Coupled Paradigm for Retrieving Integrated Water Vapor Content Based on Artificial Intelligence

Mei,

Mao,

Shi

et al. 2023

Remote Sensing

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Retrieval of integrated water vapor content (WVC) from remote sensing data is often ill-posed because of insufficient observational information. There are many factors that cause WVC changes, which yield instability in the accuracy of many traditional algorithms. To overcome this problem, we developed a novel fully-coupled paradigm for the robust retrieval of WVC from thermal infrared remote sensing data. Through the derivation of the physical radiative transfer equation, we determined two conditions that need to be satisfied for the deep learning retrieval paradigm of WVC. The first condition is that the input parameters and output parameters of the deep learning need to be able to build a complete set of solvable equations in theory. The second condition is that, if there is a strong relationship between input parameters and output parameters, it can be directly retrieved. If it is a weak relationship, we need to use prior knowledge to improve the portability and accuracy of the algorithm. The training and test data of deep learning is composed of representative solutions of physical methods and solutions of statistical methods. The representative solutions of the physical methods were obtained from the physical forward model, and the statistical solutions were obtained from multi-source data which can compensate for the defect that the physical model cannot simulate mixed pixels. MODIS L1B data was used for case analysis of paradigm retrieval, and the analysis indicated that four thermal infrared bands were usually needed as the input parameters of deep learning and the integrated water vapor content as the output parameter. When land surface temperature and emissivity were taken as prior knowledge, the root-mean-square error (RMSE) of the retrieved WVC was 0.07 g/cm2. The optimal accuracy RMSE was 0.27 g/cm2. When there was a strong correlation between input parameters and output parameters, i.e., if there were two bands that were very sensitive to WVC in the band combination, high-precision retrieval could also be achieved without prior knowledge. All the analyses show that the paradigm of deep learning coupling physics and statistics can accurately retrieve WVC, which is a significant improvement on the traditional method and solves the problem of lack of physical interpretation of deep learning.

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Improved Assessment of Atmospheric Water Vapor Content in the Himalayan Regions Around the Kullu Valley in India Using Landsat‐8 Data

Cited by 13 publications

References 36 publications

Understanding the Linkages Between Spatio-temporal Urban Land System Changes and Land Surface Temperature in Srinagar City, India, Using Image Archives from Google Earth Engine

Understanding the Linkages Between Spatio-temporal Urban Land System Changes and Land Surface Temperature in Srinagar City, India, Using Image Archives from Google Earth Engine

Identification of Snow Using Fully Polarimetric SAR Data Based On Entropy and Anisotropy

A Novel Physics-Statistical Coupled Paradigm for Retrieving Integrated Water Vapor Content Based on Artificial Intelligence

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