Characterization of Surface Geological Material in Northwest India and Adjoining Areas of Pakistan Using Normalized Difference Water Index, Land Surface Temperature and Silica Index

2018 ◽  
Vol 46 (10) ◽  
pp. 1645-1656 ◽  
Author(s):  
Sanjay Das
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Geological Material ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Northwest India

scholarly journals Lake Phenology of Freeze-Thaw Cycles Using Random Forest: A Case Study of Qinghai Lake

2020 ◽  
Vol 12 (24) ◽  
pp. 4098
Author(s):  
Weixiao Han ◽  
Chunlin Huang ◽  
Hongtao Duan ◽  
Juan Gu ◽  
Jinliang Hou
Keyword(s):  
Surface Water ◽  
Random Forest ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Qinghai Lake ◽  
Water Index ◽  
Freeze Thaw ◽  
Normalized Difference Water Index ◽  
Break Up

Lake phenology is essential for understanding the lake freeze-thaw cycle effects on terrestrial hydrological processes. The Qinghai-Tibetan Plateau (QTP) has the most extensive ice reserve outside of the Arctic and Antarctic poles and is a sensitive indicator of global climate changes. Qinghai Lake, the largest lake in the QTP, plays a critical role in climate change. The freeze-thaw cycles of lakes were studied using daily Moderate Resolution Imaging Spectroradiometer (MODIS) data ranging from 2000–2018 in the Google Earth Engine (GEE) platform. Surface water/ice area, coverage, critical dates, surface water, and ice cover duration were extracted. Random forest (RF) was applied with a classifier accuracy of 0.9965 and a validation accuracy of 0.8072. Compared with six common water indexes (tasseled cap wetness (TCW), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), automated water extraction index (AWEI), water index 2015 (WI2015) and multiband water index (MBWI)) and ice threshold value methods, the critical freeze-up start (FUS), freeze-up end (FUE), break-up start (BUS), and break-up end (BUE) dates were extracted by RF and validated by visual interpretation. The results showed an R2 of 0.99, RMSE of 3.81 days, FUS and BUS overestimations of 2.50 days, and FUE and BUE underestimations of 0.85 days. RF performed well for lake freeze-thaw cycles. From 2000 to 2018, the FUS and FUE dates were delayed by 11.21 and 8.21 days, respectively, and the BUS and BUE dates were 8.59 and 1.26 days early, respectively. Two novel key indicators, namely date of the first negative land surface temperature (DFNLST) and date of the first positive land surface temperature (DFPLST), were proposed to comprehensively delineate lake phenology: DFNLST was approximately 37 days before FUS, and DFPLST was approximately 20 days before BUS, revealing that the first negative and first positive land surface temperatures occur increasingly earlier.

scholarly journals Assessing the Impacts of the 2009/2010 Drought on Vegetation Indices, Normalized Difference Water Index, and Land Surface Temperature in Southwestern China

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaoqiang Zhang ◽  
Yasushi Yamaguchi ◽  
Fei Li ◽  
Bin He ◽  
Yaning Chen
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Deciduous Forest ◽  
Southwestern China ◽  
Evergreen Forest ◽  
Drought Period ◽  
Water Index ◽  
Normalized Difference Water Index

Droughts are projected to increase in severity and frequency on both regional and global scales. Despite the increasing occurrence and intensity of the 2009/2010 drought in southwestern China, the impacts of drought on vegetation in this region remain unclear. We examined the impacts of the 2009/2010 drought in southwestern China on vegetation by calculating the standardized anomalies of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and Land Surface Temperature (LST). The standardized anomalies of NDVI, EVI, and NDWI exhibited positively skewed frequency distributions, while the standardized anomalies of LST exhibited a negatively skewed frequency distribution. These results implied that the NDVI, EVI, and NDWI declined, while LST increased in the 2009/2010 drought-stricken vegetated areas during the drought period. The responses of vegetation to the 2009/2010 drought differed substantially among biomes. Savannas, croplands, and mixed forests were more vulnerable to the 2009/2010 drought than deciduous forest and grasslands, while evergreen forest was resistant to the 2009/2010 drought in southwestern China. We concluded that the 2009/2010 drought had negative impacts on vegetation in southwestern China. The resulting assessment on the impacts of drought assists in evaluating and mitigating its adverse effects in southwestern China.

scholarly journals Reconstruction of All-Weather Daytime and Nighttime MODIS Aqua-Terra Land Surface Temperature Products Using an XGBoost Approach

2021 ◽  
Vol 13 (22) ◽  
pp. 4723
Author(s):  
Weiwei Tan ◽  
Chunzhu Wei ◽  
Yang Lu ◽  
Desheng Xue
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Indices ◽  
Model Performance ◽  
Longwave Radiation ◽  
Water Index ◽  
Importance Analysis ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Summer Time

Generating spatiotemporally continuous land surface temperature (LST) data is in great demand for hydrology, meteorology, ecology, environmental studies, etc. However, the thermal infrared (TIR)-based LST measurements are prone to cloud contamination with missing pixels. To repair the missing pixels, a new XGBoost-based linking approach for reconstructing daytime and nighttime Moderate Resolution Imaging Spectroradiometer (MODIS) LST measurements was introduced. The instantaneous solar radiation and two soil-related predictors from China Data Assimilation System (CLDAS) 0.0625°/1-h data were selected as the linking variables to depict the relationship with instantaneous MODIS LST data. Other land surface properties, including two vegetation indices, the water index, the surface albedo, and topographic parameters, were also used as the predictor variables. The XGBoost method was used to fit an LST linking model by the training datasets from clear-sky pixels and was then applied to the MODIS Aqua-Terra LSTs during summer time (June to August) in 2017 and 2018 across China. The recovered LST data was further rectified with the Savitzky–Golay (SG) filtering method. The results showed the distribution of the reconstructed LSTs present a reasonable pattern for different land-cover types and topography. The evaluation results using in situ longwave radiation measurements showed the RMSE varies from 3.91 K to 5.53 K for the cloud-free pixels and from 4.42 K to 4.97 K for the cloud-covered pixels. In addition, the reconstructed LST products correlated well with CLDAS LST data with similar LST spatial patterns. The variable importance analysis revealed that the two soil-related predictors and the elevation variable are key parameters due to their great contribution to the XGBoost model performance.

scholarly journals Analysis on Thermal Islands Effectors in Ramadi City, Iraq Using Multi-temporal Landsat Images

2020 ◽  
Vol 1 (135) ◽  
pp. 67-78
Author(s):  
Ismael Abbas Hurat
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Landsat Images ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Landsat 7 ◽  
Multi Temporal

This paper analyzes the effects of urban density, vegetation cover, and water body on thermal islands measured by land surface temperature in Al Anbar province, Iraq using multi-temporal Landsat images. Images from Landsat 7 ETM and Landsat 8 OLI for the years 2000, 2014, and 2018 were collected, pre-processed, and anal yzed. The results suggested that the strongest correlation was found between the Normalized Difference Built-up Index (NDBI) and the surface temperature. The correlation between the Normalized Difference Vegetation Index (NDVI) and the surface temperature was slightly weaker compared to that of NDBI. However, the weakest correlation was found between the Normalized Difference Water Index (NDWI) and the temperature. The results obtained in this research may help the decision makers to take actions to reduce the effects of thermal islands by looking at the details in the produced maps and the analyzed values of these spectral indices.

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