Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ($R^2=0.74$) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

The successful launch of Luojia 1-01 complements the existing nighttime light data with a high spatial resolution of 130 m. This paper is the first study to assess the potential of using Luojia 1-01 nighttime light imagery for investigating artificial light pollution. Eight Luojia 1-01 images were selected to conduct geometric correction. Then, the ability of Luojia 1-01 to detect artificial light pollution was assessed from three aspects, including the comparison between Luojia 1-01 and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), the source of artificial light pollution and the patterns of urban light pollution. Moreover, the advantages and limitations of Luojia 1-01 were discussed. The results showed the following: (1) Luojia 1-01 can detect a higher dynamic range and capture the finer spatial details of artificial nighttime light. (2) The averages of the artificial light brightness were different between various land use types. The brightness of the artificial light pollution of airports, streets, and commercial services is high, while dark areas include farmland and rivers. (3) The light pollution patterns of four cities decreased away from the urban core and the total light pollution is highly related to the economic development. Our findings confirm that Luojia 1-01 can be effectively used to investigate artificial light pollution. Some limitations of Luojia 1-01, including its spectral range, radiometric calibration and the effects of clouds and moonlight, should be researched in future studies.

Surface water mapping is essential for monitoring climate change, water resources, ecosystem services and the hydrological cycle. In this study, we adopt a multilayer perceptron (MLP) neural network to identify surface water in Landsat 8 satellite images. To evaluate the performance of the proposed method when extracting surface water, eight images of typical regions are collected, and a water index and support vector machine are employed for comparison. Through visual inspection and a quantitative index, the performance of the proposed algorithm in terms of the entire scene classification, various surface water types and noise suppression is comprehensively compared with those of the water index and support vector machine. Moreover, band optimization, image preprocessing and a training sample for the proposed algorithm are analyzed and discussed. We find that (1) based on the quantitative evaluation, the performance of the surface water extraction for the entire scene when using the MLP is better than that when using the water index or support vector machine. The overall accuracy of the MLP ranges from 98.25–100%, and the kappa coefficients of the MLP range from 0.965–1. (2) The MLP can precisely extract various surface water types and effectively suppress noise caused by shadows and ice/snow. (3) The 1–7-band composite provides a better band optimization strategy for the proposed algorithm, and image preprocessing and high-quality training samples can benefit from the accuracy of the classification. In future studies, the automation and universality of the proposed algorithm can be further enhanced with the generation of training samples based on newly-released global surface water products. Therefore, this method has the potential to map surface water based on Landsat series images or other high-resolution images and can be implemented for global surface water mapping, which will help us better understand our changing planet.

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environmental conditions. In recent decades, many efforts have been made to develop various methods for obtaining spatially continuous NSAT from gauge or station observations. This study compared three spatial interpolation (i.e., Kriging, Spline, and Inversion Distance Weighting (IDW)) and two regression analysis (i.e., Multiple Linear Regression (MLR) and Geographically Weighted Regression (GWR)) models for predicting monthly minimum, mean, and maximum NSAT in China, a domain with a large area, complex topography, and highly variable station density. This was conducted for a period of 12 months of 2010. The accuracy of the GWR model is better than the MLR model with an improvement of about 3 °C in the Root Mean Squared Error (RMSE), which indicates that the GWR model is more suitable for predicting monthly NSAT than the MLR model over a large scale. For three spatial interpolation models, the RMSEs of the predicted monthly NSAT are greater in the warmer months, and the mean RMSEs of the predicted monthly mean NSAT for 12 months in 2010 are 1.56 °C for the Kriging model, 1.74 °C for the IDW model, and 2.39 °C for the Spline model, respectively. The GWR model is better than the Kriging model in the warmer months, while the Kriging model is superior to the GWR model in the colder months. The total precision of the GWR model is slightly higher than the Kriging model. The assessment result indicated that the higher standard deviation and the lower mean of NSAT from sample data would be associated with a better performance of predicting monthly NSAT using spatial interpolation models.

Since the Chinese government carried out the reform and opening up policy, Xishuangbanna Dai Autonomous Prefecture has experienced rapid urbanization and dramatic land use change. This research aims at analyzing urban expansion in Xishuangbanna and its impact on the land use pattern using combined methods, including radar graph, the gradient-direction method and landscape metrics. Seven land use maps from 1976 to 2015 were generated and analyzed, respectively. The results showed that urban and rubber expanded rapidly, while forest decreased during the last 40 years. The city proper, the county town of Menghai and the county town of Mengla showed the most significant and fastest urban expansion rates. In response to rapid urban expansion, land use types outside urban areas changed dramatically. In Jinghong and Mengla, urban areas were usually surrounded by paddy, shrub, rubber and forest in 1976, while most areas were dominated by rubber by 2015. With the development of Xishuangbanna, landscape diversity increased along urban-rural gradients, but decreased in some key urban areas. Urban expansion slightly reduced the connectivity of forest and increased agglomeration of rubber at the same time. Based on the analyses above, we moved forward to discuss the consequences of urban expansion, rubber plantations and land fragmentation.

Land surface temperature (LST) is a critical parameter of surface energy fluxes and has become the focus of numerous studies. LST downscaling is an effective technique for supplementing the limitations of the coarse-resolution LST data. However, the relationship between LST and other land surface parameters tends to be nonlinear and spatially nonstationary, due to spatial heterogeneity. Nonlinearity and spatial nonstationarity have not been considered simultaneously in previous studies. To address this issue, we propose a multi-factor geographically weighted machine learning (MFGWML) algorithm. MFGWML utilizes three excellent machine learning (ML) algorithms, namely extreme gradient boosting (XGBoost), multivariate adaptive regression splines (MARS), and Bayesian ridge regression (BRR), as base learners to capture the nonlinear relationships. MFGWML uses geographically weighted regression (GWR), which allows for spatial nonstationarity, to fuse the three base learners’ predictions. This paper downscales the 30 m LST data retrieved from Landsat 8 images to 10 m LST data mainly based on Sentinel-2A images. The results show that MFGWML outperforms two classic algorithms, namely thermal image sharpening (TsHARP) and the high-resolution urban thermal sharpener (HUTS). We conclude that MFGWML combines the advantages of multiple regression, ML, and GWR, to capture the local heterogeneity and obtain reliable and robust downscaled LST data.