Monitoring Of Land Surface Temperature Research Articles

Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery

In dry regions, gardens and trees within the urban space are of considerable significance. These gardens are facing harsh weather conditions and environmental stresses; on the other hand, due to the high value of land in urban areas, they are constantly subject to destruction and land use change. Therefore, the identification and monitoring of gardens in urban areas in dry regions and their impact on the ecosystem are the aims of this study. The data utilized are aerial and Sentinel-2 images (2018–2022) for Yazd Township in Iran. Several satellite and aerial image fusion methods were employed and compared. The root mean square error (RMSE) of horizontal shortcut connections (HSC) and color normalization (CN) were the highest compared to other methods with values of 18.37 and 17.5, respectively, while the Ehlers method showed the highest accuracy with a RMSE value of 12.3. The normalized difference vegetation index (NDVI) was then calculated using the images with 15 cm spatial resolution retrieved from the fusion. Aerial images were classified by NDVI and digital surface model (DSM) using object-oriented methods. Different object-oriented classification methods were investigated, including support vector machine (SVM), Bayes, random forest (RF), and k-nearest neighbor (KNN). SVM showed the greatest accuracy with overall accuracy (OA) and kappa of 86.2 and 0.89, respectively, followed by RF with OA and kappa of 83.1 and 0.87, respectively. Separating the gardens using NDVI, DSM, and aerial images from 2018, the images were fused in 2022, and the current status of the gardens and associated changes were classified into completely dried, drying, acceptable, and desirable conditions. It was found that gardens with a small area were more prone to destruction, and 120 buildings were built in the existing gardens in the region during 2018–2022. Moreover, the monitoring of land surface temperature (LST) showed an increase of 14 °C in the areas that were changed from gardens to buildings.

A Multi-Pixel Split-Window Approach to Sea Surface Temperature Retrieval from Thermal Imagers with Relatively High Radiometric Noise: Preliminary Studies

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA), Land Surface Temperature Monitoring (LSTM) and NASA-JPL/ASI Surface Biology and Geology Thermal (SBG) missions or the secondary payload on board the ESA Earth Explorer 10 Harmony. The instruments on board these missions are expected to be characterized by an NEdT of ⪆0.1 K. In order to reduce the impact of radiometric noise on the retrieved sea surface temperature (SST), this study investigates the possibility of applying a multi-pixel atmospheric correction based on the hypotheses that (i) the spatial variability scales of radiatively active atmospheric variables are, on average, larger than those of the SST and (ii) the effect of atmosphere is accounted for via the split window (SW) difference. Based on 32 Sentinel 3 SLSTR case studies selected in oceanic regions where SST features are mainly driven by meso to sub-mesoscale turbulence (e.g., corresponding to major western boundary currents), this study documents that the local spatial variability of the SW difference term on the scale of ≃3 × 3 km2 is comparable with the noise associated with the SW difference. Similarly, the power spectra of the SW term are shown to have, for small scales, the behavior of white noise spectra. On this basis, we suggest to average the SW term and to use it for the atmospheric correction procedure to reduce the impact of radiometric noise. In principle, this methodology can be applied on proper scales that can be dynamically defined for each pixel. The applicability of our findings to high-resolution TIR missions is discussed and an example of an application to ECOSTRESS data is reported.

Synergistic Use of Multispectral Data and Crop Growth Modelling for Spatial and Temporal Evapotranspiration Estimations

The aim of this research is to explore the analysis of methods allowing a synergetic use of information exchange between Earth Observation (EO) data and growth models in order to provide high spatial and temporal resolution actual evapotranspiration predictions. An assimilation method based on the Ensemble Kalman Filter algorithm allows for combining Sentinel-2 data with a new version of Simple Algorithm For Yield (SAFY_swb) that considers the effect of the water balance on yield and estimates the daily trend of evapotranspiration (ET). Our study is relevant in the context of demonstrating the effectiveness and necessity of satellite missions such as Land Surface Temperature Monitoring (LSTM), to provide high spatial and temporal resolution data for agriculture. The proposed method addresses the problem both from a spatial point of view, providing maps of the areas of interest of the main biophysical quantities of vegetation (LAI, biomass, yield and actual Evapotranspiration), and from a temporal point of view, providing a simulation on a daily basis of the aforementioned variables. The assimilation efficiency was initially evaluated with a synthetic, large and heterogeneous dataset, reaching values of 70% even for high measurement errors of the assimilated variable. Subsequently, the method was tested in a case study in central Italy, allowing estimates of the daily Actual Evapotranspiration with a relative RMSE of 18%. The novelty of this research is in proposing a solution that partially solves the main problems related to the synergistic use of EO data with crop growth models, such as the difficult calibration of initial parameters, the lack of frequent high-resolution data or the high computational cost of data assimilation methods. It opens the way to future developments, such as the use of simultaneous assimilation of multiple variables, to deeper investigations using more specific datasets and exploiting the advanced tools.

Editorial for the Special Issue “Remote Sensing Monitoring of Land Surface Temperature”

The combination of the state-of-the-art in the thermal infrared (TIR) domain [...]

Monitoring of land surface temperature of post-industrial areas and industrial sites in Kyiv using remote sensing data

Within the urban heat island that exists over Kyiv, the temperature distribution strongly depends on the landscape-functional structure of the city, namely on the degree of heating and radiation of the land cover, their relationships, proportion and changes over time. Using the thermal ranges of the satellite data of the Landsat mission, we have considered why, and where exactly, the land surface temperature changes (LST) occur from 1986 to 2018. The spatio-temporal analysis of LST is performed using profiles that cross industrial and post-industrial territories of Kyiv. It’s been shown that such territories have higher level of land surface temperature according to artificial covering surface density increasing. The Shulyavsk and Svyatoshinsk industrial zones that historically exists in central and west part of the city have been taken, as example. The post-industrial transformation of such, territories today unfortunately, does not meet the requirements of the revitalization in spite of their transformation and a decreasing in the production load. The most intense temperature increases are recorded on the outskirts of the city through new housing construction and destruction of natural landscapes, which significantly expanded the boundaries of the urban thermal island. Analysis of the surface temperatures by seasons showed that the thermal anomalies exist over industrial areas, but the LST oscillation amplitude reaches 15оС in summer between production and forest-park areas.

Eight-Day Daytime Land Surface Temperature Pattern over Peninsular Malaysia

The monitoring of land surface temperature (LST) can assist scientists to better understand the possible effect of climate change on land cover types and in conducting appropriate analysis. The earth's annual temperature has moved up and down a few degrees Celsius, including Malaysia, over the past few million years. The location of Malaysia near the equator line and experiences tropical monsoon season were important to take into consideration to the LST changes. In this paper, the eight-day LST time series data for 14-year period (Jan 2003 – Dec 2016) was downloaded from Moderate Resolution Imaging Spectroradiometer (MODIS) website with two types of satellites which are Terra and Aqua. This study focused on two gridded areas in Peninsular Malaysia; the first one which is exposed to North East monsoon, namely super region 21 and another one to South West monsoon, which is super region 26. These two areas are selected due to they being the largest land area covered within the grid relative to other grids. The objective of this study is to compare the eight- day daytime LST pattern between two monsoons with different land cover types for both satellites. Analysis such as cubic spline function with the annual periodic boundary condition and weighted least square (WLS) regression were performed to extract annual seasonal trend for the areas covered. The results of LST showed most of the gridded areas experience similar seasonal pattern for each year but different pattern were discovered when both monsoons were considered. Moreover, the LST trends also changed according to the land cover types over the study period.

Monitoring of Land Surface Temperature in Krasnoyarsk and its Suburban Area Based on Landsat 8 Satellite Data

Monitoring of Land Surface Temperature in Krasnoyarsk and its Suburban Area Based on Landsat 8 Satellite Data

Relation between urban biophysical composition and dynamics of land surface temperature in the Kolkata metropolitan area: a GIS and statistical based analysis for sustainable planning

The major environmental impacts of urbanization have changed urban biophysical components which ultimately promoted land surface temperature (LST) as well as urban heat island (UHI). This study explores the upshot of land use land cover (LULC) and resultant effect on biophysical components to understand the heat island mechanism in the Kolkata Metropolitan Area (KMA) for four selected time period of 1991, 2001, 2011 and 2017. Six satellite-derived biophysical components were selected for the present analysis: NDBI, NDVI, NDWI, MNDWI, NDBaI and SAVI. Selected bands of Landsat-5 TM and OLI-8 were used for this purpose. The result shows that the built-up area has been increased from 322.68 km2 in 1991 to 982.86 km2 in 2017 and accordingly, LST also rises from 18.47 °C mean LST of 1991 to 23.30 °C mean LST of 2017. The correlation coefficient among the biophysical parameters and LST shows that the highest continuous increasing positive relationship between NDBI and LST (R = 0.71). Moreover, multiple linear regression model (MLR) is adapted to the prediction on LST with the variation of biophysical parameters. Finally, we produced hot spot maps using Getis-Ord-Gi* statistics for the selected year to highlights the hot spot and cold spot area in KMA. The methodology presented in this paper can be broadly applied for the planning purposes because LST monitoring is an important parameter of sustainable urban planning.

Spatiotemporal monitoring of land surface temperature and estimated radiation using remote sensing: human health implications for East London, South Africa

Increase in land surface temperature (LST) and area radiation (R) has serious health implications on human life. Therefore, investigating LST and R changes is fundamental in providing crucial information for human survival. Remote sensing was used to assess the LST and estimated radiation of East London city in Eastern Cape Province of South Africa from Landsat thematic mapper images for 1986, 1996, 2006, and operational land imager, thermal infrared sensor for 2016 spanning a period of 30 years. Rapid urbanization and land cover changes in this area have contributed significantly to this drastic change in the natural surface characteristics (increased land surface temperature and surface solar radiation). For instance, vegetation cover declined by about 358.812 km2 and built-up increased built-up area by 175.473 km2 during this period, which correlates with the area thermal characteristics changes. Also, the radiation increased over the years with values exceeding the global solar radiation index. Exposure to increased ultraviolet radiation possesses risks of heat stroke, skin cancer, and heart disease to the local population. Consequently, this study provides pertinent information for human health sustainability and epidemiological cases management. Further investigation is needed in established case-specific causes. This study also epitomises the significance of remote sensing climate variability studies.

Correlation and Monitoring of Land Surface Temperature, Urban Heat Island with Land use-land cover of Dhaka City using Satellite imageries

Correlation and Monitoring of Land Surface Temperature, Urban Heat Island with Land use-land cover of Dhaka City using Satellite imageries

Downscaling Meteosat Land Surface Temperature over a Heterogeneous Landscape Using a Data Assimilation Approach

A wide range of environmental applications require the monitoring of land surface temperature (LST) at frequent intervals and fine spatial resolutions, but these conditions are not offered nowadays by the available space sensors. To overcome these shortcomings, LST downscaling methods have been developed to derive higher resolution LST from the available satellite data. This research concerns the application of a data assimilation (DA) downscaling approach, the genetic particle smoother (GPS), to disaggregate Meteosat 8 LST time series (3 km × 5 km) at finer spatial resolutions. The methodology was applied over the Crau-Camargue region in Southeastern France for seven months in 2009. The evaluation of the downscaled LSTs has been performed at a moderate resolution using a set of coincident clear-sky MODIS LST images from Aqua and Terra platforms (1 km × 1 km) and at a higher resolution using Landsat 7 data (60 m × 60 m). The performance of the downscaling has been assessed in terms of reduction of the biases and the root mean square errors (RMSE) compared to prior model-simulated LSTs. The results showed that GPS allows downscaling the Meteosat LST product from 3 × 5 km2 to 1 × 1 km2 scales with a RMSE less than 2.7 K. Finer scale downscaling at Landsat 7 resolution showed larger errors (RMSE around 5 K) explained by land cover errors and inter-calibration issues between sensors. Further methodology improvements are finally suggested.

Data Validation and Case Studies using the TET-1 Thermal Infrared Satellite System

Abstract. Based on the DLR satellite system BIRD, launched and operated in the early 2000, the TET-1 satellite has been launched in 2012 as part of the FireBird satellite constellation. The constellation will consist of two satellites, the second one to be launched in the first half of 2016. Acquired imagery is processed and archived by DLR and will be publicly available. For this purpose, a processing chain has been implemented converting raw data (level 0 product) to geo-annotated at-sensor radiance (level 1b). Further data products can be derived, e.g. information on brightness temperature, fire radiative power, and surface emissivity. Other processing levels, such as atmospherically corrected reflectance, could also be produced. The sensitivity of a Thermal Infrared (TIR) sensor system depends on its spectral characteristics and its spatial resolution. Various methods for high temperature event (HTE) detection and quantification have been developed, which can be categorized into single- and multi-band algorithms. While single band methods rely on the robust demarcation of background pixels and higher temperature pixels, considered as being anomalous, the TET-1 system facilitates the application of the widely used bi-spectral algorithm approach introduced by Dozier (1981), using the mid-infrared and longwave-infrared channel. This approach takes advantage of the non-linear nature of the Planck’s curves to calculate temperatures and the HTE area on a sub-pixel basis. TIR remote sensing can make a significant contribution to the detection and, partly, the monitoring of land surface temperature (LST), HTE and of parameters describing the surface energy balance for specific areas.

Urban Surface Temperature Time Series Estimation at the Local Scale by Spatial-Spectral Unmixing of Satellite Observations

The study of urban climate requires frequent and accurate monitoring of land surface temperature (LST), at the local scale. Since currently, no space-borne sensor provides frequent thermal infrared imagery at high spatial resolution, the scientific community has focused on synergistic methods for retrieving LST that can be suitable for urban studies. Synergistic methods that combine the spatial structure of visible and near-infrared observations with the more frequent, but low-resolution surface temperature patterns derived by thermal infrared imagery provide excellent means for obtaining frequent LST estimates at the local scale in cities. In this study, a new approach based on spatial-spectral unmixing techniques was developed for improving the spatial resolution of thermal infrared observations and the subsequent LST estimation. The method was applied to an urban area in Crete, Greece, for the time period of one year. The results were evaluated against independent high-resolution LST datasets and found to be very promising, with RMSE less than 2 K in all cases. The developed approach has therefore a high potential to be operationally used in the near future, exploiting the Copernicus Sentinel (2 and 3) observations, to provide high spatio-temporal resolution LST estimates in cities.

Validation of the results of the satellite monitoring of land surface temperature

Considered are the methods for analyzing the reliability (validation) of the estimates of land surface temperature Ts retrieved from the data of polar orbiting and geostationary meteorological satellites. Given is a brief description of the satellite payload (scanning radiometers) and algorithms of remote determination of Ts from the satellite data. For validating the satellite estimates of Ts it is proposed to use the cross-validation procedure, i.e., the comparison with independent satellite estimates of Ts for which the error level is known. Presented are the results of cross-validation of two independent satellite estimates of retrieved Ts from the data of SEVIRI/Meteosat-10, MODIS/Terra, and Aqua instruments. Such procedure can be applied to validate the estimates of Ts which are planned to be retrieved from the data of Meteor-M and Elektro-L Russian meteorological satellites. Demonstrated is the limited suitability of standard in situ observations of soil temperature at the network of weather stations for validating the satellite estimates of Ts.

Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data

Land surface temperature (LST) is an important factor in global change studies, heat balance and as control for climate change. A comparative study of LST over parts of the Singhbhum Shear Zone in India was undertaken using various emissivity and temperature retrieval algorithms applied on visible and near infrared (VNIR), and thermal infrared (TIR) bands of high resolution Landsat-7 ETM+ imagery. LST results obtained from satellite data of October 26, 2001 and November 2, 2001 through various algorithms were validated with ground measurements collected during satellite overpass. In addition, LST products of MODIS and ASTER were compared with Landsat-7 ETM+ and ground truth data to explore the possibility of using multi-sensor approach in LST monitoring. An image-based dark object subtraction (DOS3) algorithm, which is yet to be tested for LST retrieval, was applied on VNIR bands to obtain atmospheric corrected surface reflectance images. Normalized difference vegetation index (NDVI) was estimated from VNIR reflectance image. Various surface emissivity retrieval algorithms based on NDVI and vegetation proportion were applied to ascertain emissivities of the various land cover categories in the study area in the spectral range of 10.4–12.5 μm. A minimum emissivity value of about 0.95 was observed over the reflective rock body with a maximum of about 0.99 over dense forest. A strong correlation was established between Landsat ETM+ reflectance band 3 and emissivity. Single channel based algorithms were adopted for surface radiance and brightness temperature. Finally, emissivity correction was applied on ‘brightness temperature’ to obtain LST. Estimated LST values obtained from various algorithms were compared with field ground measurements for different land cover categories. LST values obtained after using Valor’s emissivity and single channel equations were best correlated with ground truth temperature. Minimum LST is observed over dense forest as about 26 °C and maximum LST is observed over rock body of about 38 °C. The estimated LST showed that rock bodies, bare soils and built-up areas exhibit higher surface temperatures, while water bodies, agricultural croplands and dense vegetations have lower surface temperatures during the daytime. The accuracy of the estimated LST was within ±2 °C. LST comparison of ASTER and MODIS with Landsat has a maximum difference of 2 °C. Strong correlation was found between LST and spectral radiance of band 6 of Landsat-7 ETM+. Result corroborates the fact that surface temperatures over land use/land cover types are greatly influenced by the amount of vegetation present.

Reducing the Discrepancy Between ASTER and MODIS Land Surface Temperature Products.

Human-induced global warming has significantly increased the importance ofsatellite monitoring of land surface temperature (LST) on a global scale. The MODerate-resolution Imaging Spectroradiometer (MODIS) provides a 1-km resolution LST productwith almost daily coverage of the Earth, invaluable to both local and global change studies.The Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) provides aLST product with a high spatial resolution of 90-m and a 16-day recurrent cycle,simultaneously acquired at the same height and nadir view as MODIS. ASTER andMODIS are complementary in resolution, offering a unique opportunity for scale-relatedstudies. ASTER and MODIS LST have been widely used but the errors in LST were mostlydisregarded. Correction of ASTER-to-MODIS LST discrepancies is essential for studiesreliant upon the joint use of these sensors. In this study, we compared three correctionapproaches: the Wan et al.'s approach, the refined Wan et al.'s approach, and thegeneralized split window (GSW) algorithm based approach. The Wan et al.'s approachcorrects the MODIS 1-km LST using MODIS 5-km LST. The refined approach modifiesthe Wan et al.'s approach through incorporating ASTER emissivity and MODIS 5-km data.The GSW algorithm approach does not use MODIS 5-km but only ASTER emissivity data. We examined the case over a semi-arid terrain area for the part of the Loess Plateau of China. All the approaches reduced the ASTER-to-MODIS LST discrepancy effectively. With terrain correction, the original ASTER-to-MODIS LST difference reduced from 2.7±1.28 K to -0.1±1.87 K for the Wan et al.'s approach, 0.2±1.57 K for the refined approach, and 0.1±1.33 K for the GSW algorithm based approach. Among all the approaches, the GSW algorithm based approach performed best in terms of mean, standard deviation, root mean square root, and correlation coefficient.