Heat extremes become increasingly frequent and severe, posing adverse risks to public health and environment. Previous research on extreme heat mostly used meteorological observations or reanalysis data, which cannot well capture detailed spatial patterns. This study developed a seamless air temperature (Ta) dataset from remote sensing data to characterize the spatio-temporal variations of heat extremes in the Yangtze River Delta (YRD) from 2001 to 2023. First, the daily maximum Ta of cloud-free pixels was estimated through machine learning algorithms from MODIS land surface temperature (LST) and other remote sensing data. Then, gaps in the estimated Ta caused by cloud cover were filled using the Temporal Fourier Analysis (TFA) method, generating a seamless daily maximum Ta dataset. The remotely sensed Ta achieved an overall MAE of 1.11 °C. Based on the remotely sensed Ta, six heat indices were calculated to characterize heat extremes, including heat days (HTD), effective accumulated high temperature (EAHT), heatwave frequency (HWF), cumulative heatwave days (HWD), maximum heatwave duration (HWMD) and average heatwave duration (HWAD). Heat extremes occurred frequently in the YRD, with obvious spatial variability. Southern basins experienced intense heat with high frequency and duration, while southern mountains and northern areas experienced weaker heat extremes. Urban areas have substantially more intense heat events than suburbs, attributed to urban heat island effect. 2022 recorded the most severe heat, with notable events also in 2013 and 2003. This study provides valuable insights into heat events in the YRD and serves as a reference for remote sensing research on heat events.

As an important greenhouse gas (GHG) in the atmosphere, carbon dioxide (CO2) has a great impact on global climate change. Accurate knowledge of the spatiotemporal variations of CO2 is of great significance for understanding the carbon cycle and evaluating the effectiveness of carbon emission reduction. In recent years, several satellites with CO2 sensors have been launched and a series of atmospheric CO2 concentration products have been developed using different retrieval algorithms. This study validated nine satellite XCO2 products derived from Greenhouse gases Observing SATellite (GOSAT), GOSAT-2, Orbiting Carbon Observatory-2 (OCO-2), and OCO-3: including ACOS-GOSAT, NIES-GOSAT, BESD-GOSAT, OCFP-GOSAT, SRFP-GOSAT, EMMA, GOSAT-2, OCO-2, and OCO-3 XCO2. The remotely sensed XCO2 products were compared with the XCO2 observations from six Total Carbon Column Observing Network (TCCON) stations in East Asia for validation. The results showed that the OCO-2 XCO2 product outperformed other products, with the highest R2 of 0.94 and the lowest MAE of 1.24 ppm. The ACOS-GOSAT and EMMA-GOSAT XCO2 products also showed favorable accuracies, both achieving R2 of 0.93 and corresponding MAE values of 1.29 and 1.31 ppm, respectively. The GOSAT-2 XCO2 product showed the poorest accuracy, with an R2 of 0.77 and a mean absolute error of 3.28 ppm. There was a significant overestimation of the bias-uncorrected GOSAT-2 XCO2 product in East Asia, and it indicated that bias correction must be performed for this XCO2 product. The accuracy of TCCON XCO2 was not consistent with remotely sensed XCO2 at different stations. The RJ, JS, AN, and TK TCCON stations generally showed better agreements between satellite estimates and TCCON observations, except for the GOSAT-2 XCO2 product.

Land cover is an important variable for climate, hydrology, and ecology studies. With the availability of various high-resolution global land cover (GLC) products, conducting a comprehensive assessment on their accuracy and consistency is important. In this study, we compared the performance of three latest 10-m-resolution GLC products, which include FROMGLC10 in 2017, ESA's Worldcover10 in 2020, and ESRIGLC10 in 2020, and three latest 30-m-resolution GLC products, which include FROMGLC30 in 2017, GLC_FCS30 in 2020, and Globeland30 in 2020, in China. The consistency of these products was investigated in terms of spatial consistency and area consistency. Though the six GLC products demonstrate similar overall distribution patterns, their detailed spatial distributions are quite different, especially for the three 10-m-resolution products. Evidently, the cropland, forest, grassland, and bareland exhibited high inconsistencies than the other types. The classification accuracy of the six GLC products was also quantitatively assessed based on a visual-interpretation-based reference dataset. FROMGLC10 exhibits the highest overall accuracy of 65.57%, followed by FROMGLC30 (64.96%) and Worldcover10 (62.74%). ESRIGLC10 (49.79%) exhibits the lowest accuracy. The accuracies of shrubland, wetland, and tundra were relatively low. This study provides a valuable reference for selecting appropriate GLC products for potential users.

The Tibetan Plateau (TP), the Third Pole of the world, has experienced significant warming over the past several decades. Previous studies have mostly relied on station-observed air temperature (Ta), reanalysis data, and remotely sensed land surface temperature (LST) to analyze the warming trend over the TP. However, the uneven distribution of stations, the poor spatial resolution of reanalysis data, and the differences between LST and Ta may lead to biased warming rates. This paper first maps Ta over the TP from 2001 to 2020 based on multi-source remote sensing data, and then quantifies the spatio-temporal variations of remotely sensed Ta and elevation dependent warming (EDW) of this region. The monthly mean Ta is estimated using machine learning (ML) method year by year, and its accuracy is validated based on station-observed Ta. The coefficient of determination (R2 ranges from 0.97 to 0.98 and the mean absolute error (MAE) ranges from 1.01 to1.04 °C. The remotely sensed Ta is used to analysis warming trend and EDW over the TP. The overall warming trend of the TP during 2001–2020 is 0.17 ℃/10a, and warming mainly distributed in the eastern TP, central TP and western Kunlun Mountains. Among the four seasons, autumn shows the most significant warming, tripling the annual warming rate. Winter shows a significant cooling trend, with the warming rate of -0.18 ℃/10a. The study also reveales the existence of EDW at both the annual and seasonal scales. This paper suggests the potential of remotely sensed Ta in global warming study, and also provides an improved understanding of climate warming over the TP.

Thermal infrared remotely sensed near-surface air temperature (Ta) can provide gridded temperature information at relatively high spatial and temporal resolutions, and such measurements are thus widely used as an essential environmental parameter in numerous fields. However, data gaps caused by clouds highly restrict the applicability of remotely sensed Ta. Only a few studies have explored the production of seamless remotely sensed Ta products, and all of them estimated Ta from the gap-filled land surface temperature (LST). This study first estimates the daily minimum, average and maximum Ta of clear-sky pixels from remote sensing data over the Yangtze River Delta (YRD) and the Ningxia Autonomous Region (NAR), China, during 2016–2020, and then applies five gap-filling methods, including spatial, temporal, spatiotemporal and two multisource fusion-based gap-filling methods, to fill the data gaps in the remotely sensed Ta data. The performances of these methods under different cloud, terrain and landscape conditions are also assessed. The validation results indicate that the Temporal Fourier analysis (TFA) method exhibits high accuracy, good robustness under various cloud and surface conditions, and good ability to describe spatial details of Ta of cloud-cover areas. It is the most suitable method to fill data gaps in remotely sensed Ta. This study provides a valuable reference for selecting appropriate methods to develop seamless remotely sensed Ta products.

Fine inhalable particulate matter (PM2.5) is one of the major air pollutants that affect human health and the environment. Detailed knowledge of the spatial distribution of PM2.5 is meaningful for the prevention and control of air pollution. Satellite remote sensing has become an effective way to observe PM2.5 concentrations. However, most studies have focused on mapping PM2.5 concentrations from satellite-derived daytime aerosol optical depth (AOD), which cannot effectively depict the nighttime atmospheric environment. This paper aims to develop a method to derive nighttime PM2.5 concentrations in Nanjing, China, using the National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light remote sensing data during September-December 2020. The relationship between the satellite at-sensor radiance, PM2.5 concentrations and other environmental factors was first explored based on the nighttime radiative transfer equation. Taking into account the pixel direct radiation and the background scattered radiation, the spatial independent variables for estimating nighttime PM2.5 were determined. Five machine learning algorithms and multiple linear regression (MLR) were employed to develop models to estimate nighttime PM2.5 concentrations. The results showed that the MLR model had obviously lower accuracy than the machine learning models, and the RF model outperformed the other models, with a coefficient of determination (R-2) of 0.81 and a mean absolute error (MAE) of 7.85 mu g.m(-3). Then, the developed model was applied to map the nighttime PM2.5 concentrations over Nanjing, which well characterized the nighttime atmospheric environment at a fine resolution. This paper proposes a method to map nighttime PM2.5 concentrations from nighttime light remote sensing data and provides references for monitoring nighttime atmospheric environments in other regions.

The Tibetan Plateau has experienced a rapid climate rise in recent years, which is a hot issue in the study of global change. However, the meteorological stations are sparsely and unevenly distributed on the Tibetan Plateau, which negatively influences the spatial representativeness of meteorological data on the climate change of the whole plateau. Satellite remote sensing provides a new approach to the large-scale research on the climate change. First, we extracted the monthly daytime land surface temperature, monthly nighttime land surface temperature, monthly clear sky days, monthly surface albedo, monthly NDVI, monthly NDSI, altitude, astronomical radiation radiance and CTI from MODIS remote sensing data, DEM data and CTI datasets. Then, we employed the Cubist algorithm to develop models for estimating monthly average, maximum and minimum air temperature through cross-validation and parameter tuning. Finally, we obtained dataset of MODIS-based monthly air temperature with a spatial resolution of 1 km on the Tibetan Plateau from 2001 to 2020. The dataset is helpful for further understanding the climate change on the Tibetan Plateau, and provides important database for the climate change research on the Tibetan Plateau.

地表温度是表征局地热环境的关键地表参数.无人机热红外遥感具有高空间分辨率的优点,为获取高分辨率局地地表温度提供了数据支撑,基于无人机热红外遥感数据的地表温度反演已吸引越来越多的关注.本文系统探索了基于同步大气温湿度廓线的无人机热红外遥感地表温度反演方法,以南京信息工程大学中苑校园及周边地区为研究区,利用无人机搭载WIRIS Pro Sc热像仪和温湿度观测系统同时获取热红外影像和大气温湿度廓线数据,在消除大气影响的基础上,结合地物比辐射率反演得到高精度的地表温度,利用修正后的实测地面点温度数据对地表温度反演结果进行验证,基于反演得到的地表温度分析其空间分布特征.结果表明,基于温湿度传感器获取的同步大气温湿度廓线,可以有效去除大气影响得到准确的离地辐射亮度,反演得到的地表温度与实测地表温度的温度差为0.06～4.96 K之间,R2为0.91,地表温度空间差异明显,其空间分布与地表覆盖类型密切相关,反演结果较为准确.研究为无人机热红外遥感数据的地表温度反演研究提供了借鉴与参考,并能够为局地微热环境监测提供技术支撑.

PM2.5是大气的重要污染物,掌握其空间分布对于大气污染防控具有重要意义.目前,PM2.5遥感监测主要围绕卫星反演的日间AOD数据开展,无法反映夜间大气污染的空间格局.以2019年9-12月NPP/VIIRS夜间灯光影像和空气质量站点PM2.5观测数据对江苏省淮安市夜间PM2.5浓度进行估算研究.基于辐射传输方程分析夜间灯光辐射与PM2.5浓度之间的关系,在此基础上综合考虑灯光辐射直接衰减和散射补偿确定了计算夜间PM2.5浓度的空间自变量,运用多元线性回归模型(MLR)、随机森林(RF)、Cubist、极端梯度提升树(XGBoost)、神经网络(NNet)、支持向量机(SVM)及最近邻法(KNN)算法构建夜间PM2.5浓度遥感估算模型.结果表明,多元线性归回模型精度明显低于各个机器学习模型,所有模型中SVM模型精度最高,决定系数R2为0.77,平均绝对误差MAE为20.83 μg·m-3,均方根误差RMSE为32.05 μg·m3.基于建立的SVM模型估算了淮安市夜间PM2.5浓度,并对其空间分布特征进行了分析.本研究探索了利用夜间灯光遥感数据估算夜间PM2.5浓度的方法,为夜间大气环境监测与管理提供了参考.

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.

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.