Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

Many application fields need land surface temperature (LST) with simultaneous high spatial and temporal resolution, which can be achieved through the disaggregation technique. Most published methods built an assumed scale-independent relationship between LST and predictor variables derived from coarse spatial resolution data. However, LST disaggregation in the heterogeneous areas, especially urban areas, is very difficult to achieve and there are few studies on it. In this article, we propose an adjusted stratified stepwise regression method for temperature disaggregation in urban areas. Landsat Enhanced Thematic Plus (ETM+) data from Shanghai, China, were used to construct remote-sensing indices that are related to LST variance and retrieve LST at 60 and 480 m spatial resolution, respectively. Different stepwise regression models at 480 m resolution were built for two stratified regions according to normalized difference vegetation index (NDVI) distribution, and then each independent variable at 60 m resolution was adjusted to calculate disaggregated LST by considering its relationship with the 480 m resolution image. By using LST retrieved directly from ETM+ band 6 at 60 m spatial resolution as the reference, the proposed method comprising resampling disaggregation, the thermal data sharpening model (TsHARP)/disaggregation procedure for radiometric surface temperature (DisTrad) technique, and the LST-principal component analysis (PCA) regression algorithm were verified and compared. The results show that the temperature distribution estimated using the proposed method is most consistent with that of the reference LST in this heterogeneous study area, and that the precision improves significantly, especially for the low vegetation fraction region.

The sky view factor (SVF) is a commonly used indicator in research fields of urban microclimate such as the urban heat island (UHI). Analytical methods have been widely adapted in SVF estimation with the development of accurate three dimensional (3D) data, in which, however, the sky radiance is usually assumed to be isotropic. In this study, we discuss the sky temperature distribution and its influence on the SVF calculation based on its definition and differential geometry, and then according to the nocturnal net outgoing radiation ratio of cloudy skies to that of clear skies, the effective SVF (ESVF) is presented and defined to explain UHI intensity variance. The results of numerical computation combined with calculations from true 3D building data for Adelaide, Australia, demonstrate that the sky temperature variance has little effect on SVF estimation. While for totally overcast or cloudy sky conditions, T_s is less different from the sky temperature than for clear skies, the proposed ESVF is sensitive to the sky temperature distribution and cloud cover fraction, providing the potential ability to analyse UHI intensity for various cloudy conditions. Copyright © 2013 Royal Meteorological Society

Understanding the net ecosystem productivity (NEP) is essential for understanding ecosystem functioning and the global carbon cycle. Utilizing meteorological and The Advanced Very High Resolution Radiometer (AVHRR) remote sensing data, this study employed the Carnegie–Ames–Stanford Approach (CASA) and the Geostatistical Model of Soil Respiration (GSMSR) to map a monthly vegetation NEP in China from 1982 to 2020. Then, we examined the spatiotemporal trends of NEP and identified the drivers of NEP changes using the Geodetector model. The mean NEP over the 39-year period amounted to 265.38 gC·m⁻². Additionally, the average annual carbon sequestration amounted to 1.89 PgC, indicating a large carbon sink effect. From 1982 to 2020, there was a general fluctuating increasing trend observed in the annual mean NEP, exhibiting an overall average growth rate of 4.69 gC·m⁻²·a⁻¹. The analysis revealed that the majority of the vegetation region in China, accounting for 93.45% of the entirety, exhibited increasing trends in NEP. According to the Geodetector analysis, precipitation change rate, solar radiation change rate, and altitude were the key driving factors in NEP change rate. Furthermore, the interaction between the precipitation change rate and altitude demonstrated the most significant effect.

Accurate information on the spatial distribution of poverty is of great significance to the formulation and implementation of the government’s targeted poverty alleviation policy. Traditional poverty mapping is mainly based on household survey data and statistical data, which cannot describe the spatial distribution of poverty well. This paper presents a study of mapping the integrated poverty index (IPI) in the Dian-Gui-Qian contiguous extremely poor area of southwest China. Based on multiple independent spatial variables extracted from NPP/VIIRS nighttime light (NTL) remote sensing data, digital elevation model (DEM), land cover information, open street map, and city accessibility data, eight algorithms were employed and compared to determine the optimal model for IPI estimation. Among these machine learning algorithms, traditional multiple linear regression had the lowest accuracy compared with the other seven machine learning algorithms and XGBoost showed the best performance. Feature selection was performed to reduce overfitting and five variables were finally selected. The final developed XGBoost model achieved an MAE of 0.0454 and an R2 of 0.68. The IPI map derived from the developed XGBoost model characterized the spatial pattern of poverty in the Dian-Gui-Qian contiguous extremely poor area well, which provided a good reference for the poverty alleviation work and public resources allocation in the study area. This study can also serve as a template for poverty mapping in other areas using remote sensing data.