1,721,013 research outputs found
Reducing scaling effect on downscaled land surface temperature maps in heterogenous urban environments
No full textThe literature review indicates that a scaling effect does exist in downscaling land surface temperature (DLST) processes, and no substantial methods were specially developed for addressing it. In this research, the main aim is to develop a new method to reduce the scaling effect on DLST maps at high resolutions. A thermal component-based thermal spectral unmixing (TSU) model was modified and a multiple regression (REG) model was adopted to create DLST maps at high resolutions. A combined variance of red and NIR bands at a very high resolution with a difference image between upscaled LST and DLST was used to develop a new method. With two case data sets, LSTs at coarse resolutions were downscaled by using the modified TSU model and the REG model to create DLST results. The new method with a correction term expression (a linear model created by using a semi-empirical approach) was used to improve the DLST maps in the two case study areas. The experimental results indicate that the new method could reduce the root mean square error and the mean absolute error >30% and >33%, respectively, and thus demonstrate that the proposed method was effective and significant, especially reducing the scaling effect on DLST results at very high resolutions. The novel significance for the new method is directly reducing the scaling effect on DLST maps at high resolutions
Correcting Scaling Effect in Downscaling Surface Temperature at High Resolutions with a Multiple Regional Correction Approach
No full textThere exists a scaling error in currently downscaling land surface temperature (DLST) processes and it is necessary to develop substantial methods specially for reducing it. In this letter, a multiple regional correction (MRC) approach with multiple correction terms (mCTs) was proposed to correct scaling errors in DLST processes. The test results indicate that (1) the proposed approach can effectively correct the scaling effects in DLST processes at high resolutions by reducing root mean square error by ~ 55%; (2) all initial resolutions considered for optical data (0.5 m – 4 m) were effective for developing the MRC approach with mCTs in correcting the scaling errors. Overall, this novel approach can significantly improve the accuracy of DLST maps at very high resolutions
Machine Learning Classifier Evaluation for Different Input Combinations: A Case Study with Landsat 9 and Sentinel-2 Data
No full textHigh-resolution multispectral remote sensing images offer valuable information about various land features, providing essential details and spatially accurate representations. In the complex urban environment, classification accuracy is not often adequate using the complete original multispectral bands for practical applications. To improve the classification accuracy of multispectral images, band reduction techniques are used, which can be categorized into feature extraction and feature selection techniques. The present study examined the use of multispectral satellite bands, spectral indices (including Normalized Difference Built-up Index, Normalized Difference Vegetation Index, and Normalized Difference Water Index) for feature extraction, and the principal component analysis technique for feature selection. These methods were analyzed both independently and in combination for the classification of multiple land use and land cover features. The classification was performed for Landsat 9 and Sentinel-2 satellite images in Delhi, India, using six machine learning techniques: Classification and Regression Tree, Minimum Distance, Naive Bayes, Random Forest, Gradient Tree Boosting, and Support Vector Machine on Google Earth Engine platform. The performance of the classifiers was evaluated quantitatively and qualitatively to analyze the classification results with whole image (comprehensive feature) and small subset (targeted feature). The RF and GTB classifiers were found to outperform all others in the quantitative analysis of all input combinations for both Landsat 9 and Sentinel-2 datasets. RF achieved a classification total accuracy of 96.19% for Landsat and 96.95% for Sentinel-2, whereas GTB achieved 91.62% for Landsat and 92.89% for Sentinel-2 in all band combinations. Furthermore, the RF classifier achieved the highest F1 score of 0.97 in both the Landsat and Sentinel datasets. The qualitative analysis revealed that the PCA bands were particularly useful to classifiers in distinguishing even the slightest differences among the feature class. The findings contribute to the understanding of feature extraction and selection techniques for land use and land cover classification, offering insights into their effectiveness in different scenarios
The Impact of the Land Cover Dynamics on Surface Urban Heat Island Variations in Semi-Arid Cities: A Case Study in Ahmedabad City, India, Using Multi-Sensor/Source Data
No full textThis study examines the behaviour of land surface temperature (LST) and surface urban heat island (SUHI) from MODIS data over Ahmedabad city, Gujarat state (India), from 2003 to 2018. Summer and winter LST patterns were analyzed, both daytime and nighttime. Ahmedabad, one of the fastest growing metropolitan cities in India, is characterized by a semi-arid climate. The investigation focuses on the SUHI variations due to warming or cooling trends of both urban and rural areas, providing quantitative interpretations by means of multi-sensor/source data. Land cover maps, normalized differential vegetation index, surface albedo, evapotranspiration, urban population, and groundwater level were analyzed across the years to assess their impact on SUHI variations. Moreover, a field campaign was carried out in summer 2018 to measure LST in several rural and urban sites. During summer daytime the rural zone exhibits a higher average LST than urban area, resulting in a mean negative SUHI, typical of arid cities, while a slight positive SUHI (mean intensity of 0.4 °C) during winter daytime is present. An evident positive SUHI is found only during summer (1.8 °C) and winter nighttime (3.2 °C). The negative SUHI intensity is due to the low vegetation presence in the rural area, dominated by croplands turning into bare land surfaces during the pre-monsoon summer season. Higher LST values in the rural area than in the urban area are also confirmed by the field campaign, with an average difference of about 5 °C. Therefore, the impact of the rural LST in biasing the SUHI is evident, and a careful biophysical interpretation is needed. For instance, within the urban area, the yearly intensity of the summer daytime SUHI is not correlated with the evapotranspiration, while the correspondent summer daytime LST exhibits a significant negative correlation (-0.73) with evapotranspiration. Furthermore, despite the city growth across the years, the urban area does not generally reveal a temporal increase of the magnitude of the heat island, but an enlargement of its spatial footprint
Satellite air temperature estimation for monitoring the canopy layer heat island of Milan
No full textIn this work, satellite maps of the urban heat island of Milan are produced using satellite-based infrared sensor dataFor this aim, we developed suitable algorithms employing satellite brightness temperatures for the direct air temperature estimation 2 m above the surface (canopy layer), showing accuracies below 2KThe air temperatures measured by ground-based weather stations were properly matched with brightness temperatures observed by the Moderate-resolution Imaging Spectroradiometer (MODIS) on board of both Terra and Aqua satellitesIn total, 931 daytime and nighttime scenes taken between 2007 and 2010 were processedAnalysis of the canopy layer heat island (CLHI) maps during summer months reveals an average heat island effect of 3-4K during nighttime (with some peaks around 5K) and a weak CLHI intensity during daytimeIn addition, the satellite maps reveal a well defined island shape across the city center from June to September confirming that, in Milan, urban heating is not an occasional phenomenonFurthermore, this study shows the utility of space missions to monitor the metropolis heat islands if they are able to provide nighttime observations when CLHI peaks are generally significant. © 2012 Elsevier Inc
A response of snow cover to the climate in the northwest himalaya (Nwh) using satellite products
No full textThe Himalayan region is one of the most crucial mountain systems across the globe, which has significant importance in terms of the largest depository of snow and glaciers for fresh water supply, river runoff, hydropower, rich biodiversity, climate, and many more socioeconomic developments. This region directly or indirectly affects millions of lives and their livelihoods but has been considered one of the most climatically sensitive parts of the world. This study investigates the spatiotemporal variation in maximum extent of snow cover area (SCA) and its response to temperature, precipitation, and elevation over the northwest Himalaya (NWH) during 2000–2019. The analysis uses Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra 8‐day composite snow Cover product (MOD10A2), MODIS/Terra/V6 daily land surface temperature product (MOD11A1), Climate Hazards Infrared Precipitation with Station data (CHIRPS) precipitation product, and Shuttle Radar Topography Mission (SRTM) DEM product for the investigation. Modified Mann‐Kendall (mMK) test and Spearman’s correlation methods were employed to examine the trends and the interrelationships between SCA and climatic parameters. Results indicate a significant increasing trend in annual mean SCA (663.88 km2/year) between 2000 and 2019. The seasonal and monthly analyses were also carried out for the study region. The Zone‐wise analysis showed that the lower Himalaya (184.5 km2/year) and the middle Himalaya (232.1 km2/year) revealed significant increasing mean annual SCA trends. In contrast, the upper Himalaya showed no trend during the study period over the NWH region. Statistically significant negative correlation (−0.81) was observed between annual SCA and temperature, whereas a nonsignificant positive correlation (0.47) existed between annual SCA and precipitation in the past 20 years. It was also noticed that the SCA variability over the past 20 years has mainly been driven by temperature, whereas the influence of precipitation has been limited. A decline in average annual temperature (−0.039 °C/year) and a rise in precipitation (24.56 mm/year) was detected over the region. The results indicate that climate plays a vital role in controlling the SCA over the NWH region. The maximum and minimum snow cover frequency (SCF) was observed during the winter (74.42%) and monsoon (46.01%) season, respectively, while the average SCF was recorded to be 59.11% during the study period. Of the SCA, 54.81% had a SCF above 60% and could be considered as the perennial snow. The elevation‐based analysis showed that 84% of the upper Himalaya (UH) experienced perennial snow, while the seasonal snow mostly dominated over the lower Himalaya (LH) and the middle Himalaya (MH)
Satellite and ground-based sensors for the Urban Heat Island analysis in the city of Rome
Full text linkIn this work, the trend of the Urban Heat Island (UHI) of Rome is analyzed by both ground-based weather stations and a satellite-based infrared sensor. First, we have developed a suitable algorithm employing satellite brightness temperatures for the estimation of the air temperature belonging to the layer of air closest to the surface. UHI spatial characteristics have been assessed using air temperatures measured by both weather stations and brightness temperature maps from the Advanced Along Track Scanning Radiometer (AATSR) on board ENVISAT polar-orbiting satellite. In total, 634 daytime and nighttime scenes taken between 2003 and 2006 have been processed. Analysis of the Canopy Layer Heat Island (CLHI) during summer months reveals a mean growth in magnitude of 3-4 K during nighttime and a negative or almost zero CLHI intensity during daytime, confirmed by the weather stations. © 2010 by the authors; licensee MDPI, Basel, Switzerland
Monitoring of atmospheric water around precipitation events using a scanning ground-based microwave radiometer
No full textFeasibility of a satellite microwave radiometer to mitigate tropospheric effects in SAR interferometry
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