Surface Temperature Retrieval Research Articles

Retrieval and validation of the dawn-dusk land surface temperature from FY-3E MERSI-LL

ABSTRACT Land surface temperature (LST) is an important physical parameter that reflects changes in surface processes. Remote sensing makes it possible to obtain global or regional LSTs with high spatial and temporal resolution. Feng-Yun 3E (FY-3E) is the world’s first civilian dawn-dusk orbiting meteorological satellite on board the Medium Resolution Spectral Imager – Low-Light (MERSI-LL) sensor capable of acquiring LST at 250 m spatial resolution at dawn and dusk. By complementing with existing polar-orbiting satellites, FY-3E can fill the satellite observation gap within the 6-hour assimilation window and further enhance the observation capability of LST. To retrieve accurate LST from MERSI-LL, the generalized split-window (GSW) method was developed. The GSW coefficients were derived from the simulation dataset produced by Moderate Resolution Transmittance Code 5.2 with an atmospheric profile database. As a key input to the GSW algorithm, the land surface emissivity was dynamically estimated by the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset in combination with the fractional vegetation cover. The water vapour content is calculated in real time by the split-window covariance/variance ratio method. The comparison between retrieved LSTs and in situ LSTs shows that the GSW algorithm can effectively retrieve the LSTs of vegetation cover surfaces. The root mean square error ranged from 0.79 to 1.79 K at dawn and from 1.32 to 1.92 K at dusk.

Adapting an Existing Empirical Algorithm for Microwave Land Surface Temperature Retrieval in China for AMSR2 Data

To extend the time span of the microwave (MW) land surface temperature (LST) dataset in China, this study proposed an optimized empirical algorithm for Advanced Microwave Scanning Radiometer 2 (AMSR2) LST retrieval based on the algorithm for its predecessor, the AMSR-Earth Observing System (AMSR-E). A modified comprehensive classification system of environmental variables (CCSEV) that considered the impact of landform, landcover, atmospheric conditions, and solar radiation on the variation of LST was first constructed, and the LST for each class in the CCSEV was then retrieved through stepwise regression using the brightness temperature in different AMSR2 channels. The results indicate that the annual RMSE of the AMSR2 LST, compared to the reference Moderate Resolution Imaging Spectroradiometer (MODIS) LST from 2012 to 2020, varies between 3.26 K and 3.61 K in the daytime and 2.76 K and 2.96 K in the nighttime, respectively. The RMSE of the AMSR2 LST compared to the field measurements at the sites of the Beidahe river basin and Naqu regions varies between 4.16 K and 5.26 K in the daytime and 2.4 K and 5.17 K in the nighttime. The accuracy is relatively low in the warmer months and daytime due to the stronger solar radiation, and is also relatively low in western China due to the dominate highly fluctuating topography and barren and arid landcover. Generally, the accuracy of the AMSR2 LST is comparable with that of the AMSR-E LST retrieved by the predecessor algorithm, which facilitates coherent long-term applications using AMSR series LST datasets.

Retrieval of cloudy land surface temperature from AMSR2 brightness temperature, precipitable water vapour and cloud liquid water

ABSTRACT Land surface temperature (LST) is an important parameter in many studies including hydrology, meteorology, and ecology. In addition to thermal infrared remote sensing, passive microwave remote sensing is an effective alternative for LST retrieval, particularly under cloudy conditions. In this study, we analysed the impact of cloud on the transfer of microwave radiation in simulated database and presented a parameterization method to compensate the impact. On this basis, we put forward an improved method for LST retrieval from Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature at the 18.7 and 23.8 GHz channels with precipitable water vapour (PWV) and cloud liquid water (CLW) as supplementary input. The method is built on the basis of microwave radiative transfer equation and the assumption that 1) surface emissivity at two adjacent channels is linear and 2) the main factor causing brightness temperature difference at two channels is the absorption difference of PWV and CLW. The LST retrieval method was tested in simulated database, with root mean square error (RMSE) of 3.1 K. The method was then applied to AMSR2 brightness temperature and GFS reanalysis data to estimate global LST in 2019–2020, and the accuracy was validated using in situ LST measurements collected from 52 sites. The RMSE is approximately 4.7 K and 3.7 K for daytime and night-time, respectively. The improved LST retrieval method is promising in long-temporal and large-area applications.

Retrieval of oceanic chlorophyll concentration from GOES-R Advanced Baseline Imager using deep learning

Data of chlorophyll (Chl-a) concentration obtained from polar-orbiting ocean color satellite sensors are subject to major spatial and temporal gaps in data coverage owing to the intrinsically low sampling frequency that coincides with adverse conditions such as cloud obstruction. Such gaps can be minimized with geostationary satellites owing to their higher scanning frequency over a given region, allowing more sampling opportunities as clouds move. However, geostationary ocean color missions have not been available for most regions of the globe. The Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite R series (GOES-R) is a potential alternative for the U.S. owing to improved signal-to-noise ratio compared with its predecessors. Nonetheless, retrieving [Chl-a] from ABI measurements is challenging; the number, placement, and width of its spectral bands are suboptimal and an “ocean-grade” atmospheric correction scheme is lacking. Here we describe the construction of a deep learning-based algorithm to derive [Chl-a] from GOES-16 ABI L1B radiance and ancillary data without an explicit atmospheric correction algorithm. A large (∼22 million examples) training dataset of paired ABI observations and [Chl-a] estimates derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) were used to train, test and validate a 10-layer deep neural network model to retrieve [Chl-a] from ABI data. Preliminary results show that without atmospheric correction but with the aid of deep learning, it is possible to detect open ocean features with strong contrast such as across eddies and fronts, but there are still significant issues in coastal waters and occasionally in the open oceans. In general, the ABI-derived [Chl-a] agrees reasonably well with VIIRS-derived values in the open ocean during 3–5 h of the day. The location of oceanographic features such as fronts and eddies in the ABI-derived [Chl-a] maps are also coincident with those obtained from traditional ocean color and sea surface temperature retrievals. The findings of this study demonstrate that deep learning is a powerful tool for capturing intricate patterns that are too subtle for traditional algorithms developed by humans, Furthermore, the utilization of GOES-R ABI has the potential to complement polar-orbiting satellite data and mitigate data gaps.

Improving sea surface temperature in a regional ocean model through refined sea surface temperature assimilation

Abstract. Infrared (IR) and passive microwave (PMW) satellite sea surface temperature (SST) retrievals are valuable to assimilate into high-resolution regional ocean forecast models. Still, there are issues related to these SSTs that need to be addressed to achieve improved ocean forecasts. Firstly, satellite SST products tend to be biased. Assimilating SSTs from different providers can thus cause the ocean model to receive inconsistent information. Secondly, while PMW SSTs are valuable for constraining models during cloudy conditions, the spatial resolution of these retrievals is rather coarse. Assimilating PMW SSTs into high-resolution ocean models will spatially smooth the modeled SST and consequently remove finer SST structures. In this study, we implement a bias correction scheme that corrects satellite SSTs before assimilation. We also introduce a special observation operator, called the supermod operator, into the Regional Ocean Modeling System (ROMS) four-dimensional variational data assimilation algorithm. This supermod operator handles the resolution mismatch between the coarse observations and the finer model. We test the bias correction scheme and the supermod operator using a setup of ROMS covering the shelf seas and shelf break off Norway. The results show that the validation statistics in the modeled SST improve if we apply the bias correction scheme. We also find improvements in the validation statistics when we assimilate PMW SSTs in conjunction with the IR SSTs. However, our supermod operator must be activated to avoid smoothing the modeled SST structures on spatial scales smaller than twice the PMW SST footprint. Both the bias correction scheme and the supermod operator are easy to apply, and the supermod operator can easily be adapted for other observation variables.

Towards a new generation of artificial‐intelligence‐based infrared atmospheric sounding interferometer retrievals of surface temperature: Part II – Assessment

AbstractWhen using neural networks (NNs), the lack of input information characterizing the radiative transfer can result in regional biases, especially when retrieving surface properties. In the Part I companion article we explored localization techniques in an attempt to help the NN adjust its behaviour to local conditions. In this article we analyze results from an image‐processing approach, the novel localized convolutional NN (CNN) model for the retrieval of surface temperature (TS) over a fixed domain using infrared atmospheric sounding interferometer (IASI) observations. An in‐depth evaluation is performed. The localized‐CNN architecture is a promising artificial intelligence solution that provides retrievals similar to, if not better than, those of the European Organisation for the Exploitation of Meteorological Satellites' PWLR3 retrieval algorithm that also uses IASI observations, collocated with microwave data too. This shows the benefits of localizing the CNN retrieval. This image‐processing retrieval scheme allows interpolation of the TS below the clouds, and thus a preliminary analysis of the cloud impact on the TS is performed. The possibility to estimate retrieval uncertainties is investigated, and a practical solution, based on the binning of the input space, is proposed for CNN architectures. The best strategy for a global‐scale retrieval is yet to be found for such an image‐processing scheme, but potential solutions and their respective advantages and disadvantages are discussed.

Thermal Discharge Temperature Retrieval and Monitoring of NPPs Based on SDGSAT-1 Images

High-accuracy sea surface temperature (SST) retrieval near nuclear power plants (NPPs) is one of the most significant indicators for evaluating marine ecological environment quality, monitoring the real-time situation of thermal discharge, and supporting planning decisions. However, complex computations, the inaccessible real-time vertical profile of the atmosphere, and the uncertainty of atmospheric profile data increase the error of SST retrieval. Additionally, influenced by their low spatial resolution, the widely used AVHRR/MODIS remote sensing images (RSIs) are unable to retrieve the detailed distribution of SST in small scale regions such as coastal NPPs. In this paper, we propose a simplified split-window-based temperature retrieval method (the SW method) suitable for SDGSAT-1 30 m thermal infrared spectrometer (TIS) RSIs. Specially, this method only needs atmospheric transmittance and surface emissivity by counteracting the average atmospheric temperature to monitor the thermal discharge of offshore NPPs. First, the geometric and radiometric calibrated thermal infrared and multi-spectral cloudless data of the target regions are selected to obtain the corresponding apparent radiance of the RSIs. Second, in accordance with the red and near-infrared (NIR) bands of multi-spectral RSIs, the surface emissivity is calculated to distinguish water from land. Next, we determine the atmospheric profile parameters from the weather conditions of the target region at the imaging time. Finally, according to the theory of surface-atmosphere radiative transfer, the SST of target regions is retrieved with the proposed SW method, and the results are compared with those of the conventional radiative transfer equation (RTE), mono-window (MW), and the nonlinear sea surface temperature (NLSST) algorithms. The experimental results indicate that the SST retrieved from the split-window algorithms (i.e., SW and NLSST) are generally higher than those of the single-channel algorithms (i.e., RTE and MW). The SST difference between the SW algorithm and the NLSST algorithm is within 0.5 °C. In addition, SDGSAT-1 can monitor the seasonal detailed variation of the thermal discharge near coastal NPPs. This article is the first to attempt to quantitative small-scale SST retrieval based on thermal infrared and multi-spectral images obtained from the SDGSAT-1 TIS and a multispectral imager (MII), and therefore, provide an effective reference for marine environment monitoring.

Towards a new generation of artificial‐intelligence‐based infrared atmospheric sounding interferometer retrievals of surface temperature: Part I – Methodology

AbstractMultilayer perceptrons have been popular in the remote‐sensing community for the last 30 years, in particular for the infrared atmospheric sounding interferometer instrument. However, for coarse‐resolution infrared instruments such as the infrared atmospheric sounding interferometer, these algorithms are currently used at the pixel level and are trained at a global scale. This can result in regional biases that not only affect the quality of the retrieval but can potentially also propagate into model forecasts if these results were to be assimilated. To help reduce these biases, we try to help the neural network (NN) adjusting its behaviour to local conditions; we call this “localization”. We investigate how to localize traditional multilayer perceptrons applied at the pixel scale and compare this with novel artificial intelligence image‐processing techniques, more particularly convolutional NNs and so‐called “localized convolutional NNs”. These approaches are tested for the retrieval of surface temperature over a fixed domain. Different techniques are proposed to localize both approaches. Further evaluation of the retrieval methods will be covered in a part II companion paper.

Assimilation of surface‐sensitive bands' clear‐sky radiance data using retrieved surface temperatures from geostationary satellites

AbstractClear‐sky radiances (CSRs) derived from observations made by imager sensors on board geostationary satellites are widely used in most operational numerical weather prediction systems. CSRs have data on tropospheric water vapour and temperatures, and the products at water vapour bands are generally assimilated into global data assimilation systems. In another band, known as the CO2 band (13.3–13.4 μm), CSRs are not used widely yet, despite having a wealth of information about temperatures in the mid‐ and low troposphere. This is mainly because of the high surface sensitivity of this band, which makes it difficult to accurately simulate brightness temperatures when there are non‐negligible errors in the surface parameters in the models. This article quantitatively investigated the surface sensitivities of CO2 and water vapour bands, which have the sensitivity under dry atmospheric conditions, and developed retrieval of land surface temperature (LST) from window band CSRs to obtain a more accurate simulated brightness temperature. Additionally, it was discovered that the retrieved LST outperformed that from the model in short‐range forecasts for the low‐water‐vapour band (7.3 μm) CSR data assimilation, and throughout the forecasting period, especially in the tropics, for the CO2 band CSR data assimilation. We also examined an unexpected improvement in the low troposphere in the model's LST trials, and we concluded that it was related to the relationship between the LST and atmospheric temperature biases.

An extended SW-TES algorithm for land surface temperature and emissivity retrieval from ECOSTRESS thermal infrared data over urban areas

Land surface temperature (LST) over urban areas is an essential parameter in the monitoring of urban thermal environment. Urbanization has resulted in a more complex geometric structure of urban surface than natural surface. However, in the conventional LST retrieval algorithms, the impact of urban geometric structure is usually neglected. In this study, an extended algorithm that combines the split-window algorithm (SW) and the temperature and emissivity separation algorithm (TES) was proposed to retrieve LST and land surface emissivity (LSE) over urban areas simultaneously from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) thermal infrared (TIR) data. In the extended SW-TES algorithm with considering urban geometry effect, the brightness temperature at ground level was obtained using the SW algorithm, and the LST and LSE were retrieved simultaneously by using the TES algorithm. The results indicate that LST (LSE) retrieved by the extended SW-TES algorithm with urban geometry effect correction has generally lower (higher) values than that without urban geometry effect correction. The LST differences mostly vary from 0.3 K to 1.2 K, whereas the LSE differences are mainly concentrated between −0.01 and − 0.06. Furthermore, the root mean squared error (RMSE) between LST/LSE retrieved by the extended SW-TES algorithm with urban geometry effect correction and LST/LSE obtained from the ECOSTRESS level-2 LST/LSE product decrease with the increase of sky view factor (SVF). The largest RMSE is approximately 1.6 K for LST and 0.03 for LSE in the SVF of 0.1, which represents an urban pixel surrounded by high buildings within its adjacent pixels. There are no evident differences between LST/LSE retrieved by the extended SW-TES algorithm with urban geometry effect correction using five and three ECOSTRESS TIR bands. The LST differences are mainly concentrated between −0.2 K and 0.2 K, whereas the LSE differences mostly range from 0.001 to 0.004. The findings of this study indicate that it is necessary to consider urban geometry effect for accurate LST and LSE retrieval over urban areas.

Infrared measurement synthetic database for inverse thermography model based on deep learning

Infrared (IR) diagnostics are used to measure the surface temperature of plasma-facing components in fusion devices. However, the interpretation of such images and any quantitative analysis are very complex in all-reflective environments because of unknown emissivity and multiple reflections. This is an important issue for safe operation and understanding of plasma-wall interaction phenomena. Moreover, it can also benefit other industrial applications e.g., monitor heating on production lines, additive manufacturing devices, etc. Recently, a first demonstration of simulation-assisted machine learning method has proven to be effective in retrieving surface temperatures from IR measurements. Yet, the performances of such an approach has been evaluated on a tokamak prototype considering pure specular surfaces. This paper presents an optimised generation of training dataset based on a synthetic data generated by a deterministic ray tracer model. The obtained results shows that the proposed methodology enables very accurate simulations minimizing the computational burden associate to greedy models (i.e., Monte Carlo type) to generate training data for realistic surfaces.

Improving remote sensing of extreme events with machine learning: land surface temperature retrievals from IASI observations

Retrieving weather extremes from observations is critical for weather forecasting and climate impact studies. Statistical and machine learning methods are increasingly popular in the remote sensing community. However, these models act as regression tools when dealing with regression problems and as such, they are not always well-suited for the estimation of the extreme weather states. This study firstly introduces two error types that arise from such statistical methods: (a) ‘dampening’ refers to the reduction of the range of variability in the retrieved values, a natural behavior for regression models; (b) ‘inflating’ is the opposite effect (i.e. larger ranges) due to data pooling. We then introduce the concept of localization that intends to better take into account local conditions in the statistical model. Localization largely improves the retrievals of extreme states, and can be used both for retrieval at the pixel level or in image processing techniques. This approach is tested on the retrieval of land surface temperature using infrared atmospheric sounding interferometer observations: the dampening is reduced from 1.9 K to 1.6 K, and the inflating from 1.1 K to 0.5 K, respectively.

DirecTES: A Direct Method for Land and Sea Surface Temperature and Emissivity Separation for Thermal Infrared Sensors—Application to TRISHNA and ECOSTRESS

The coming years will see the launch of several missions (TRISHNA, LSTM, SBG), which will acquire images in four or more spectral bands in thermal infrared (TIR) at high spatial resolution (~50–60 m) and with high temporal revisit (~2–3 days). The derivation of surface temperature and emissivity values from top-of-atmosphere radiances is not straightforward, as it is a non-deterministic process requiring additional information. In this paper, we propose the algorithm DirecTES to efficiently separate surface temperature and emissivity. This algorithm is based on the use of a comprehensive spectral database of emissivity, resulting in a well-posed deterministic problem while not assuming strong hypotheses. The algorithm can also benefit from non-TIR information, such as the acquisitions from the same satellite but in the visible and near-infrared domains, or exogenous data—land/sea mask or soil-occupation map. These would help identify the nature of the surface and therefore improve the temperature and emissivity retrievals. After the complete description of the method, we evaluate the performances of DirecTES on theoretical landscapes in TRISHNA’s context under a large range of atmospheric conditions. The retrievals of surface temperature reach RMSEs of 0.8 K over vegetation and 0.5 K over water, including both sensor and atmospheric uncertainties. We then evaluate DirecTES on ECOSTRESS images on sites where the ECOSTRESS Land Surface Temperature (LST) performance has been documented; DirecTES surface temperature retrievals are consistent with the ECOSTRESS LST product and the in-situ data.

Performance of the Large Field of View Airborne Infrared Scanner and its application potential in land surface temperature retrieval

Performance of the Large Field of View Airborne Infrared Scanner and its application potential in land surface temperature retrieval

CHANGING LANDSCAPE PATTERNS AND ITS EFFECT ON RISING LAND SURFACE TEMPERATURE OF LAHORE-PAKISTAN

The study intaimsto evaluate the impact of landscape (Land use land cover) changeson Land surface temperature (LST) by using GIS and Remote sensing (RS) techniques in Lahoreduring 2017–2021.Supervised Maximum Likelihood classification method was used to classify theland use and land cover classes. For retrieval of Land surface temperature, the Landsat 8 (Band 10)product level 2 was used. Barren land and built- up area were identified as the most leading LULCclasses respectively in the study area at the cost of vegetation cover and water bodies. The barren landclass increased from 55.9% to 63.81% ,while the Built-up class also increases from 13.5% to 18.46%during 2017-2021.Whereas , vegetation and water bodies both are exhibiting the decline. The declinein vegetation class was reported from 29.58% to 17.6% over the time 2017 to 2021.Overall, 0.3 %decrease is observed in water bodies. In 2017 the 3◦C decline is observed in land surface temperature(LST) value than 2021.Built-up and Vegetation classes can contribute a significant role in variation ofLST in comparison to the water bodies. These results will be very helpful to understand the LULCchanges and eventually it will assist the land management authorities, policy maker and town plannersas well.

LakeCoSTR: A tool to facilitate use of Landsat Collection 2 to estimate lake surface water temperatures

AbstractAlthough remote sensing of lake surface water temperature has been ongoing for decades and has led to important discoveries regarding the warming of lake surface temperature in some regions, the ability to access and use remote sensing data is still primarily limited to remote sensing experts. Effective use of remote sensing data involves technical skills in coding (often in multiple programming languages), application of appropriate atmospheric corrections, and integrating spatially heterogeneous remote sensing data with in situ data obtained at specific geographic locations. To improve access to remote sensing data and broaden the understanding of changes in lake surface water temperature over the past four decades, we created lakeCoSTR (lake Collection 2 Surface Temperature Retrieval), a user‐friendly, cloud‐based script that gives ecologists lacking specialized training in remote sensing the ability to access the Landsat Collection 2 temperature estimates for lakes with a surface area of at least 0.4 ha. Additionally, if in situ data are provided, a paired dataset can be created within the tool. To demonstrate lakeCoSTR, we retrieved surface water temperature data for a lake with a long monitoring history, Lake Sunapee, NH, USA, and compared long‐term surface temperature trends between data obtained via lakeCoSTR and in situ measurements. When compared with Landsat Collection 1 temperature estimates derived from a single‐channel algorithm, the Landsat Collection 2 data from lakeCoSTR required no calibration to in situ data. This suggests that lakeCoSTR can be used to document temporal trends in lakes and to reprocess analyses that relied on Collection 1 data. From 1983 until 2020, remotely sensed surface water temperature estimates at Lake Sunapee increased by 0.07–0.09°C year−1 during July and August, a trend that agreed with long‐term in situ record. As climate change continues to impact freshwater systems, a better understanding of long‐term temperature data will be vital. Remotely sensed data, like those acquired using lakeCoSTR, may provide a window into temperature trends for lakes.

Retrieval and validation of sea surface temperature from HY-1D COCTS

Retrieval and validation of sea surface temperature from HY-1D COCTS

Land Surface Temperature Retrieval From Landsat 9 TIRS-2 Data Using Radiance-Based Split-Window Algorithm

The thermal infrared sensor-2 (TIRS-2) carried on Landsat 9 is the newest thermal infrared (TIR) sensor for the Landsat project and provides two adjacent TIR bands, which greatly benefits the land surface temperature (LST) retrieval at high spatial resolution. In this article, a radiance based split window (RBSW) algorithm for retrieving LST from Landsat 9 TIRS-2 data was proposed. In addition, the split-window covariance-variance ratio (SWCVR) algorithm was improved and applied to Landsat 9 TIRS-2 data for estimating atmospheric water vapor (AWV) that is required for accurate LST retrieval. The performance of the proposed method was assessed using the simulation data and satellite observations. Results reveal that the retrieved LST using the RBSW algorithm has a bias of 0.06 K and root-mean-square error (RMSE) of 0.51 K based on validation with the simulation data. The sensitivity analysis exhibited a LST error of <1.75 K using the RBSW algorithm when the uncertainties in input parameters (i.e., AWV, emissivity, and at-sensor radiance) were considered. There is a marginal discrepancy between LST retrievals using the estimated AWV and moderate resolution imaging spectroradiometer AWV, with a difference in bias of −0.14 K and RMSE of 0.22 K, which indicates that the improved SWCVR method can provide an optional means to obtain AWV inputted in LST retrieval. With regard to the validation using the in situ measurements, the retrieved LST from Landsat 9 TIRS-2 data exhibits a bias of 0.44 K and RMSE of 1.98 K, respectively, showing a higher accuracy than the USGS Landsat LST product with bias of 1.21 and RMSE of 2.56 K. In conclusion, the proposed algorithm combining RBSW algorithm and improved SWCVR algorithm for LST retrieval from Landsat 9 has a good accuracy without dependence on external atmospheric data, and it is expected to be a reliable method for LST generation from Landsat 9 TIRS-2 data.

Lunar Surface Temperature and Emissivity Retrieval From SDGSAT-1 Thermal Imager Spectrometer

The lunar surface temperature is one of the important thermophysical parameters of the Moon, which helps to study the radiative properties of the lunar surface. Thermal infrared emission spectra is sensitive to the thermophysical properties of the lunar surface material, and the emissivity data can be used for lunar surface composition inversion. In the past, humans have conducted hundreds of lunar exploration missions, but only a small number have been conducted in the infrared band, typical examples include the Apollo program and the Diviner lunar radiometer experiment of the LRO satellite. Only part of the lunar surface has been explored by these missions. SDGSAT-1 carries out a complete observation of the lunar surface in three infrared wavelength bands (B1: 8-10.5μm; B2: 10.3-11.3μm; B3: 11.5-12.5μm) by its thermal imager as part of its mission. In this study, the temperature-emissivity separation (TES) algorithm is used to retrieve the lunar surface temperature and calculate the thermal infrared band emissivity using the radiometric data obtained by SDGSAT-1 thermal imager, the temperature distribution of full-disk Moon and the emissivities distribution of three bands are also mapped. The temperature retrieval error is verified less than 1K.

Urban Land Surface Temperature Retrieval From High Spatial Resolution Thermal Infrared Image Using a Modified Split-Window Algorithm

Urban Land Surface Temperature Retrieval From High Spatial Resolution Thermal Infrared Image Using a Modified Split-Window Algorithm