Nighttime light (NTL) remote sensing data has been widely used in various fields, such as human activity analysis, urbanization studies, and economic evaluation. However, Earth’s nighttime environment is very complex so that the NTL images are seriously affected by atmospheric effect and moonlight. This complexity primarily stems from the numerous atmospheric scattering and absorption, as well as the incoming moonlight, which can significantly distort and contaminate nighttime light observed by the satellite and consequently reduce the precision and stability of NTL data. In order to improve the quantitatively quality of the NTL data, this paper proposes an innovative atmospheric correction algorithm that leverages the nighttime radiative transfer model (nRTM) considering both atmospheric effect and moonlight effect to get ground radiance of artificial lights from satellite nighttime light images. This model takes into account the complex interactions between light and the atmosphere. By simulating these processes, the algorithm is able to separate the contributions of atmospheric scattering and absorption from the original NTL images. To demonstrate the effectiveness of the proposed algorithm, this paper takes the SDGSAT-1 NTL image of Beijing as a representative case study of atmospheric correction. By comparing the corrected and uncorrected images, it is evident that the atmospheric correction significantly improves the quality of the NTL data and the ground nighttime lighting information becomes clearer and more accurate, effectively removing noise interference and enhancing data reliability. Moreover, it also found that the high-pressure sodium (HPS) lamps and LED lamps in the NTL images presented different radiance values and spectral shapes that can be helpful for classifying different lamps.

The estimation of Sea Surface Temperature (SST) from ocean satellites with large observation angles must account for the angular effects on SST. This study developed an angle-dependent non-linear split-window algorithm (A-NLSW) to retrieve SST from Chinese ocean satellite HY-1D thermal infrared data. The algorithm coefficients were obtained based on the simulated dataset and grouped by initial SSTs, total atmospheric column water vapor content (TCWV), and satellite zenith angle (SZA). The A-NLSW algorithm is validated and re-calibrated using the bulk temperature collected by the iQuam in-situ dataset. After re-calibration, the accuracy of the SST was improved from 1.53 K to 0.87 K for SZA ranging from 0 to 70.5 ° . Nearly 60% of the validation points achieved an accuracy of 0.5 K and over 90% achieved an accuracy of 1.0 K. These findings highlight the robustness of the A-NLSW algorithm in reliably retrieving SST from HY-1D satellite images, even when observations are made at large SZA.

Meteorological geostationary satellite data provide a data source for the study of the directional apparent reflectance of land surface because it collects surface information in fixed direction but with various solar illumination direction in one day or several days. In this paper, the reflectance values of water body pixels were investigated with respect to the observation geometry using Himawari-8 NC data, and the original zenith and azimuth angles were converted to relative angles RA and RAA, and the linear relationship between the reflectance and the relative angles was constructed through regression analysis. Results show that there is a good statistical relationship between apparent reflectance and relative angles RA/RAA. The method in this paper provide a fast angular correction on the apparent reflectance for meteorological geostationary satellite data.

Night-time light remote sensing has been widely used in various fields, including human activity analysis, urbanization studies, and economic research. However, Earth’s nighttime environment is very complex so that nighttime light images are seriously affected by atmospheric elements and moonlight. To address this issue, this paper proposed an atmospheric correction algorithm on basis of a newly developed radiative transfer model (RTM) that aims to derive the ground radiance of artificial lights from satellite nighttime light images. The SDGSAT-1 night-time light image of Beijing is used as a case study to demonstrate the effectiveness of the proposed algorithm. Results show that the new algorithm can effectively removes the atmospheric effects and improves the data quality of nighttime light images.

The daytime and nighttime lunar surface temperatures (LSTs) are crucial for investigating lunar surface environment and lunar mineral composition. The Diviner sensor provides global lunar surface observation in seven thermal infrared (TIR) channels from 8 to$400~\mu $m, but the existing LST retrieval methods are more suitable for daytime pixels with high temperature rather than the nighttime or shadowed pixels with low temperature. This letter develops a new method, called as TES-GBR, by combining the conventional temperature-emissivity separation (TES) and gradient boosting regression (GBR) method, to retrieve low LST (e.g., nighttime or shadowed regions) from Diviner’s four longwave infrared channel data. The new method used three emissivity curve shape parameters, maximum-minimum apparent emissivity difference (MMD), maximum-minimum ratio (MMR), and emissivity variance (VAR), to establish their relationship with the minimum emissivity ($\varepsilon _{\mathbf {min}}$). Results indicate that the TES-GBR method can reduce the emissivity error to 0.005 from 0.029 obtained by the conventional TES method and get a general retrieval accuracy of 1.0 K for the low LST. Finally, the TES-GBR method was applied to retrieve the nighttime LST of the year 2015, and it found that there was a period variation in the nighttime temperature.

Urban land surface temperature (ULST) is one of the core parameters in monitoring the urban thermal environment, which has received extensive attention in several study and application areas. Thermal infrared (TIR) remote sensing technology can efficiently observe large-scale land surface thermal radiance information and is a critical approach used to obtain ULST quickly. Traditional LST retrieval algorithms are conducted using the classical radiance transfer equation (RTE) based on the assumption that the land surface is flat, which may be challenging to hold for complex urban landscapes. Moreover, with the improvement of the spatial resolution of remote sensing images, the influence caused by the geometric structure will be more obvious. Various urban thermal radiance transfer models have been proposed and successfully applied to TIR remote sensing images with tens of meters spatial resolutions, such as Landsat, ECOSTRESS, and Gaofen-5. Current airborne TIR sensors can observe remote sensing images with ultra-high spatial resolution (sub-meter). In this paper, using the ensemble learning method based on the ultra-high spatial resolution urban thermal radiance transfer model (UHURT), a new retrieval algorithm is developed to estimate the LST directly from the observed brightness temperature. The proposed new algorithm applies to ultra-high spatial resolution remote sensing images. It has the end-to-end advantage of not relying on atmospheric parameters or land surface emissivity, known as in traditional algorithms, thus avoiding the limitations due to the lack of available input data. Validation results based on the simulation dataset showed that the proposed algorithm has higher theoretical accuracy than the traditional split-window algorithm. As the sky view factor (SVF) decreases, the accuracy advantage becomes more pronounced, growing from 0.149 K (SVF = 1.0) to 1.085 K (SVF = 0.25). The application results in the remote sensing image also indicated that the results of the proposed algorithm (RMSE = 2.093 K) are more accurate than those of the SW algorithm (RMSE = 2.490 K), and the correlation between the resultant error and building density is lower, which can accurately reduce the geometric effect to obtain the ULST better.

Thermal infrared (TIR) remote sensing is an important technological tool for observing large-scale land surface thermal radiance and can obtain the spatially continuous land surface temperature (LST), a critical land surface physical parameter of great interest in several fields. After decades of development, various LST retrieval algorithms have been proposed. However, current studies indicated that the commonly used retrieval algorithms show a decrease in the accuracy of the results under humid atmospheric conditions, and the theoretical analysis of the phenomenon needs to be developed. This study derives the LST error as a function of atmospheric parameters (transmittance, upward radiance, and downward radiance) directly based on the TIR radiative transfer equation. Compared with the TIR channel, the mid-infrared (MIR) channel has less water vapor absorption, is more insensitive to water vapor, and has a larger transmittance, which is expected to improve the accuracy of LST retrieval under humid atmospheric conditions. In this study, a typical simulation dataset under various atmospheric and land surface conditions is constructed based on the MIR channels of MODIS remote sensing data. Analysis of the retrieval results based on the simulation datasets shows that the MIR channels have more minor errors for the same level of atmospheric errors. With the growth of column water vapor (CWV), the error of the split-window (SW) algorithm constructed based on the TIR channel increases. In contrast, the accuracy of the algorithm developed by MIR channels is more stable, and the advantage of the accuracy in humid atmospheric conditions is more prominent. Two SW algorithms are applied to nighttime TIR and MIR remote sensing images observed by Aqua MODIS, and the validation results obtained based on SURFRAD ground sites also showed that the two MIR SW algorithms achieved the accuracy advantage of 0.425 K (SW1_TIR: 2.582 K / SW1_MIR: 2.157 K) and 0.525 K (SW2_TIR: 2.624 K / SW2_MIR: 2.099 K), which indicated that the MIR-SW algorithms can more accurately retrieve the LST under humid atmospheric conditions.

The land surface temperature (LST) of urban is a key factor in the field of urban environmental monitoring, and thermal infrared (TIR) remote sensing is an efficient method to obtain it. An important assumption of the traditional thermal radiance transfer model is that the land surface is flat, which has now proven difficult to hold under the urban landscape. Most of the existing urban thermal radiance transfer models have been developed for remote sensing images with a spatial resolution of tens of meters. Currently, airborne TIR sensors have the observation capability to acquire remote sensing images with an ultra-high spatial resolution (1 cm to 1 m), and the model needs to be improved. This paper proposed a new ultra-high spatial resolution urban thermal radiance transfer model (UHURT) after analyzing the transfer processes within the urban canopy at ultra-high spatial resolution. Various radiance components, the emitted radiance, reflected atmospheric downward radiance, and adjacent radiance, were modeled separately. The results of the traditional model and the UHURT model were compared with the results of a ray-tracing computer simulation model, which showed that the new model successfully quantifies the multiple scattering and adjacent effects, and obtained images closer to the computer simulation images. Besides, the LST retrieval of the computer-simulated images was performed using the traditional model and the UHURT model, and the proposed model successfully reduced the errors of the retrieval results and weakened the spatial correlation between the residual distribution and the geometric characteristics of the urban landscape.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) onboard the International Space Station (ISS) provides standard land surface temperature (LST) products to meet community concerns on water stress and evapotranspiration. In 2019, an anomaly on the mass storage unit (MSU) changed the data acquisition mode of ECOSTRESS to a direct streaming one, eliminating three bands centered at 1.60, 8.29, and$9.20~\mu \text{m}$. This letter analyzes the feasibility of retrieving LST from ECOSTRESS data using split-window (SW) techniques which need only two thermal infrared (TIR) bands to serve as an alternative to produce LST products. Three different spilt-window algorithms have been developed and analyzed for all possible TIR band combinations using the simulated top-of-atmosphere (TOA) radiance dataset. Besides, the selected SW algorithms were validated using the intercomparison method with ECOSTRESS LST product data and the temperature-based method with surface radiation budget (SURFRAD) ground-based measurements. The result showed that SW algorithms provided higher accuracies than the temperature-emissivity separation (TES) algorithm, with the main advantage that the atmospheric profile data is not demanded prior.

The Urban Canopy Multiple-scattering thermal Radiative Transfer (UCM-RT) model, incorporating the effects of urban geometry and adjacent thermal radiation from neighboring pixels, depicts the process of thermal radiation transfer on the urban surface, and therefore provided new opportunity to develop new retrieval algorithms for urban land surface temperature (ULST). This paper aims at developing an urban split-window (USW) algorithm for deriving ULST from high-spatial-resolution thermal infrared (TIR) data from the Visible and Infrared Multispectral Sensor (VIMS) onboard Chinese GaoFen-5 (GF-5) satellite. The VIMS provides 4-channel TIR image with a spatial resolution of 40 m. The coefficients of the USW algorithm were obtained based on several subranges of atmospheric column water vapors (CWV), emissivity and sky view factors (SVFs) under various land surface conditions, by removing the geometry, adjacent and atmospheric effects. Methods of estimating urban pixel emissivity and CWV in urban areas were also conducted. The sensitive analysis of instrument noise and uncertainty of CWV, pixel emissivity and SVFs demonstrated the reliability of the USW algorithm in ULST retrieval. The accuracy evaluation shows that the root-mean-square errors of the ULST results is less than 0.7 K in theory. Compared with the conventional SW algorithms and publicly released LST products, the USW algorithm obtained better results in estimating ULST, especially in high-density building areas. Finally, the USW algorithm is expected to be beneficial to the application of multiple thermal infrared sensor data, for example, the newly launched GF-5 No.2 satellite images.

Thermal infrared (TIR) remote sensing observation signal is influenced by both atmospheric and land surface conditions that are difficult to separate with conventional multichannel TIR data. Because of the advantage of channel wealth, hyperspectral TIR data can simultaneously estimate the land surface and atmospheric parameters using neural network models or integrating them with physical models. However, the commonly used neural network models do not fully explore the correlation between different channels by treating the input data as discrete features. Thus, this study aims to develop a new deep neural network (DNN) by combining the long short-term memory (LSTM) network and convolutional neural network (CNN) for estimating land surface temperature (LST), emissivity, atmospheric transmittance, upward radiance, and downward radiance more accurately. By applying on the thermal airborne hyperspectral imager (TASI) simulation dataset covering global atmospheric conditions with 32 channels in$8.0- 11.5\,\,\mu \text{m}$, the proposed model achieved results with the LST error of 0.95 K, the emissivity error of less than 0.012 for each channel, and the accuracy of three atmospheric parameters has also been improved compared with the current neural network models. Our model has been applied to a real TASI image, and its validity was further proved by the ground measurement validation data. Therefore, it can provide more reliable initial values for physical optimization models.

Land surface temperature (LST) is one of the key parameters in the process of energy exchange between the land surface and atmosphere, and thermal infrared (TIR) remote sensing is an important approach to efficiently obtain LST over a large area. Algorithms for retrieval of LST from TIR remote sensing data have been studied for decades, and the split-window (SW) algorithm can directly eliminate atmospheric effects by using the brightness temperature at the top of the atmosphere in two adjacent TIR channels and thus is widely applied. Landsat-9, the latest launch in the Landsat series of satellites, provides 2-channel TIR images with the same 100m spatial resolution as Landsat-8, and it is meaningful to develop the SW algorithm for LST retrieval using Landsat-9 data. In this paper, four SW algorithms were developed, and the accuracy and noise sensitivity of the results under different observation conditions were compared based on the simulation dataset to select the algorithm with the best performance. The ground measurement data under different land cover types and the global Landsat-9 LST products, produced by the single-channel algorithm, were selected to verify the accuracy of the proposed algorithm. The results show that the ground validation accuracy is about 1.574 K, better than the Landsat-9 existing LST product. Moreover, the retrieved LST images have similar spatial distribution to the Landsat-9 LST products, with RMSEs from 0.31 K to 2.87 K in various regions.

Land surface temperature (LST) is a crucial parameter in the energy and material balance of land surface system. The angle effect of LST makes the accuracy of LST restricted and limits the application of remote sensing LST product. In order to eliminate the influence of viewing angle, this study proposed a novel method to perform angular normalization by constructing a feature space of surface emission radiance and fractional vegetation coverage (Radiance-FVC space). The proposed approach is applied in Hetao Plain as an example. It is found that the Root Mean Square Error (RMSE) can reach 5.1K, and the angular normalization effect is more significant for pixels with larger viewing zenith angle.