Search

The use of remote sensing technique to monitor atmospheric water vapor is significant for weather and climate studies. However, the general methods of retrieving precipitable water vapor (PWV) with high precision and high resolution using remote sensing satellite has hardly been investigated, which becomes the focus of this paper. A general remote sensing PWV retrieval (GRPR) method that uses level-1 near-infrared (NIR) data sympathized by a global navigation satellite system (GNSS) is proposed. In this method, the atmospheric transmittance coefficients are determined by combining the radiative transmission model and the central wavelength interpolation method instead of directly using the traditional empirical values. Next, the PWV derived from remote sensing NIR data is obtained using the adaptive seasonal exponent model rather than the traditional transmittance–water vapor lookup table method. Furthermore, the accuracy of remote sensing NIR PWV is further calibrated by establishing the seasonal relationship between PWV residual and elevation. The corresponding NIR data from the Fengyun-3A (FY3A) satellite over the period of 2013–2015 in China are selected to validate the proposed method. Statistical results show the good performance of GRPR method for internal and external accuracies with root mean square (RMS) improvement rates of 76.8% and 72.4%, respectively, compared with the FY3A level-2 PWV products. In addition, the proposed method has good robustness and is almost unaffected by the PWV magnitude at different seasons. Proposed GRPR method is also applied for PWV retrieval at different time scales, further showing its superiority of retrieving PWV with high precision and high resolution. These results indicate the good application prospect of the GRPR method proposed in this study for generating remote sensing PWV products. • The optimal atmospheric transmittance coefficients are determined in this study. • An adaptive seasonal exponent model is developed to retrieve remote sensing PWV. • The accuracy of remote sensing NIR PWV is further calibrated using DEM data. • GRPR method is applied for remote sensing PWV retrieval at different time scales. [ABSTRACT FROM AUTHOR]

A catastrophic landslide occurred on 21 July 2020, 30 km from Enshi city, in Mazhe County of Hubei province, China. In this paper, we aimed to investigate the kinematic evolution and volumetric change related to this landslide using multi-source remote sensing measurements from synthetic aperture radar (SAR) and optical data. C-band Sentinel-1 and X-band TerraSAR-X SAR data are analyzed using several multi-temporal interferometry (MTI) time-series techniques including Persistent Scatterer Interferometry (PSI), Small Baseline subset (SBAS), and Combined eigenvalue maximum likelihood Phase Linking (CPL). The spatial pattern of surface deformation resulting from the interferometric analysis is then statistically analyzed to retrieve the pre-failure and post-failure displacements. Co-failure motions are analyzed using an image correlation technique applied to both the Planetscope and Sentinel-2 images. Moreover, 4 pairs of bistatic TerraSAR-X/TanDEM-X (TDX) data are utilized to generate high-precision digital elevation models (DEMs) and estimate the volumetric change related to the main slope failure. The pre-failure ground deformation analysis results suggest that the landslide was already active before the July 2020 failure, with the seasonality and hydraulic diffusivity being characteristics of a slow-moving landslide. Among the three different MTI methods applied, the CPL method results in a greater measurement points (MPs) density than the PSI and SBAS method when estimating the pre-failure movement of the Shaziba landslide. The July 2020 Shaziba disaster is divided into three main parts: (1) slightly horizontal deformation of 0.5–1.5 m within the northern part with ground and house cracks, (2) less collapse in the eastern part with horizontal motions reaching 30 m and (3) a highly eroded western part where vegetation was wholly lost in the main event, resulting in an collapse volume of approximately 4.98 million m3, out of which approximately around 3.4 million m3 was deposited and approximately 1.58 million m3 was washed away into the Qing River. After the failure, the marginal scrap of the main failure body, above crown of landslide and eastern part showed instability with rates of 20–30 mm/yr, suggesting that the failure zone may continue to expand. • Multi-sensor data integration to quantify kinematics and life cycle of the landslide. • Improvement in increasing density of scatterers using Combined eigenvalue maximum likelihood Phase Linking (CPL). • Analysis of the time lag between triggering factors and changes in background velocity. • Geodetic quantification of erosion and deposition during the main slope failure using time-series of Tandem-X data. [ABSTRACT FROM AUTHOR]

Knowledge of the spatial distribution of populations at finer spatial scales is of significant value and fundamental to many applications such as environmental change, urbanization, regional planning, public health, and disaster management. However, detailed assessment of the population distribution data of countries that have large populations (such as China) and significant variation in distribution requires improved data processing methods and spatialization models. This paper described the construction of a novel population spatialization method by combining land use/cover data and night-light data. Based on the analysis of data characteristics, the method used partial correlation analysis and geographically weighted regression to improve the distribution accuracy and reduce regional errors. China's census data for the years 1990, 2000, and 2010 were assessed. The results showed that the method was better at population spatialization than methods that use only night-light data or land use/cover data and global linear regression. Evaluation of overall accuracies revealed that the coefficient of correlation R-square was >0.90 and increased by >0.13 in the years 1990, 2000, and 2010. Moreover, the local R-square of over 90% of the samples (counties) was higher than the adjusted R-square of the general linear regression model. Furthermore, the gridded population density datasets obtained by this method can be used to analyse spatial-temporal patterns of population density and provide population distribution information with increased accuracy and precision compared to conventional models. [ABSTRACT FROM AUTHOR]

With the increasing problem of eutrophication, lacustrine ecosystem can undergo complex changes, often resulting in a shift from a clear macrophyte-dominated state to a turbid phytoplankton-dominated state. However, it's not clear how lake transitions occur at regional and global scales. This is due to a lack of long-term monitoring and an effective algorithm that can distinguish floating/emergent aquatic vegetation (FEAV), submerged aquatic vegetation (SAV) and algal bloom (AB). This paper presents a novel three-step classification algorithm based on Landsat imagery, (ie., vegetation and bloom indices (VBI) algorithm): (1) to distinguish between aquatic vegetation (AV) and non-aquatic vegetation (non-AV) extents by an aquatic vegetation index (AVI) derived from tasseled cap transformation; (2) to identify FEAV and SAV within AV extent by using the normalized difference vegetation index (NDVI), and (3) to extract AB from non-AV extent with floating algae index (FAI). The performance of the VBI algorithm was validated using 1307 field samples collected across 22 lakes in the middle and lower reaches of the Yangtze River (MLY) in China, with a pooled overall accuracy of 84.49%. Finally, the VBI algorithm was applied to Landsat data from 1985 to 2021 to quantify the temporal changes of FEAV, SAV and AB in large lakes (i.e., > 50 km2) along the MLY. Within these lakes, AV showed a significant decrease over the past 37 years, mainly due to the decrease of SAV; while AB occurred with higher frequency and in more lakes. The transition from a macrophyte-dominated state to a phytoplankton-dominated aquatic systems is still ongoing in the MLY area. This study shows outstanding innovation in the construction of a new classification index (i.e., AVI), and the method of calculating the adaptive threshold, which makes VBI algorithm have great potential in mapping long-term changes and capturing transformation between lake communities on a global scale. • A novel Landsat-based algorithm was developed for quantifying aquatic communities. • An index for aquatic vegetation (AV) was proposed to classify AV and non-AV. • Long-term algal blooms and AV (floating/emergent/submerged) in MLY lakes were mapped. • AV decreased while algal blooms increased in most MLY lakes from 1985 to 2021. • Shift from macrophyte- to phytoplankton-dominated state is ongoing in MLY lakes. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key parameter when monitoring land surface processes. However, cloud contamination and the tradeoff between the spatial and temporal resolutions greatly impede the access to high-quality thermal infrared (TIR) remote sensing data. Despite the massive efforts made to solve these dilemmas, it is still difficult to generate LST estimates with concurrent spatial completeness and a high spatio-temporal resolution. Land surface models (LSMs) can be used to simulate gapless LST with a high temporal resolution, but this usually comes with a low spatial resolution. In this paper, we present an integrated temperature fusion framework for satellite-observed and LSM-simulated LST data to map gapless LST at a 60-m spatial resolution and half-hourly temporal resolution. The global linear model (GloLM) model and the diurnal land surface temperature cycle (DTC) model are respectively performed as preprocessing steps for sensor and temporal normalization between the different LST data. The Landsat LST, Moderate Resolution Imaging Spectroradiometer (MODIS) LST, and Community Land Model Version 5.0 (CLM 5.0)-simulated LST are then fused using a filter-based spatio-temporal integrated fusion model. Evaluations were implemented in an urban-dominated region (the city of Wuhan in China) and a natural-dominated region (the Heihe River Basin in China), in terms of accuracy, spatial variability, and diurnal temporal dynamics. Results indicate that the fused LST under all-weather conditions is highly consistent with actual Landsat LST data (in situ LST measurements), in terms of a Pearson correlation coefficient of 0.94 (0.96–0.99), a mean absolute error of 0.71–0.98 K (0.82–3.34 K), and a root-mean-square error of 0.97–1.26 K (1.09–4.36 K). The generated diurnal Landsat-like LSTs under all weather conditions are able to support diurnal dynamic studies that are the most relevant to human activities, such as the study of urban heat islands (UHIs) and water resource management at the field scale. • Gapless LSTs with a high spatio-temporal resolution were generated. • The first time to integrally fuse multi-source satellite-observed and model-simulated data. • Improvement in the generalization ability of spatio-temporal LST fusion. • Highly accurate fused results under all-weather conditions based on extensive validation. [ABSTRACT FROM AUTHOR]

Historically, change detection reviews have examined and categorized algorithms based on their techniques for the remote sensing community. Here, we synthesize urban land change algorithms by the types of information they provide to a diverse and growing set of user communities. Two goals of the paper are first to synthesize past and current change detection studies to examine urban land change to help users of remote sensing algorithms understand and navigate the vast variety of available methods, and second to identify gaps in knowledge for the urban remote sensing community. We analyzed 644 peer-reviewed research papers published in English-language journals and conducted a systematic review of urban land change algorithms. All papers included in our study focus on urban land change ; studies that concentrate on single-date urban classification or mapping for one point in time without an explicit urban land change component are not included in this analysis. The review showed five key results and knowledge gaps. First, most urban change detection algorithms are being developed and applied for only a few regions, with 75% of studies focused on high or upper-middle-income countries and the majority of these on China or the United States. This suggests a major gap in geographic coverage as well as the need for more studies on cities in low and lower-middle-income countries. Second, the results show that 41% of the algorithms have been developed or applied for cities of over 5 million inhabitants. This focus on large cities is problematic given that only 11% of the world's urban population lives in cities with populations greater than 5 million and that most future urban growth will occur in small- and medium-sized towns and cities with populations of fewer than 1 million people. Third, our analysis shows that 62% of the studies use three or fewer time points to measure urban land change with an average study length of 17 years. Since rapidly growing urban areas are highly dynamic, this suggests that existing algorithms using only a few time points are likely missing urban transitions that can only be captured with high temporal frequency analysis. Fourth, we find that urban expansion is the most commonly monitored type of urban land change. Comparatively fewer studies characterized intra-urban change or three-dimensional structural change. Fifth and finally, the results show that an overwhelming majority—87%—of all studies identify only one urban class, highlighting a need for more studies that distinguish intra-urban variation and differentiate multiple urban classes. Our analysis shows that it is very difficult—nearly impossible—to compare across algorithms. Thus, for users of urban land change information, it is difficult to navigate the literature and know which algorithms are most appropriate for a particular use. Taken together, this points to the need for improved reproducibility, replicability, and comparability of studies in order to help harmonize urban land change information across regions and countries. This is especially important given the growing user communities of urban land change products and analysis, especially from policy and practice. • We conduct a systematic review of the urban land change algorithm literature. • Most studies monitor urban change at low temporal frequencies. • The majority of urban land change is characterized using only one urban class. • Over 75% of studies focus on high or upper-middle level income countries. • Key knowledge gaps include in geographic coverage, city size, and land transitions. [ABSTRACT FROM AUTHOR]

Fusing high-level passive microwave (PMW) LST and thermal infrared (TIR) LST products is a promising means of generating high-quality, all-weather land surface temperature (LST) data. In this paper, we propose a new fusion strategy for generating high-quality, all-weather LST data based on cumulative distribution function (CDF) matching and multiresolution Kalman filtering (MKF). CDF matching was employed to improve the quality of the high-resolution (0.01°) LST data downscaled with the retrieved coarse-resolution (0.1°) PMW LST from the Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature (BT) data set. The MKF approach was employed to produce high-quality, all-weather LST data by fusing the retrieved coarse-resolution AMSR2 LST and high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) LST data sets filled with CDF-improved LST data. The spatial patterns in the high-resolution and coarse-resolution LST data were consistent after MKF fusion. The accuracy of coarse-resolution LST data was considerably improved. The biases, root mean square errors (RMSEs) and determination coefficients (R2) were 0.79–1.47 K, 3.0–3.82 K, 0.88–0.96, respectively. The RMSE of the fused coarse-resolution LST data decreased by 1.51 K/1.82 K and 1.81 K/1.39 K in daytime/nighttime in the Tibetan Plateau (TP) and Heihe River Basin (HRB) verification regions compared to the RMSE of the retrieved AMSR2 LST data. The accuracy of high-resolution LST data remained unchanged, but the spatial coverage was complete with exceptions at the orbital gaps. The bias, RMSE and R2 values of high-resolution LST data were − 0.91- -0.81 K, 3.02–3.32 K, and 0.93–0.97 under all-sky conditions in the TP and HRB verification regions, respectively. Compared to methods in the existing LST fusion studies, the proposed strategy has advantages in terms of LST data quality and validation accuracy. When both coarse-resolution and high-resolution LST data are missing, the spatial coverages of fused all-weather LST data are incomplete. We attempted to fill the vacancies in PMW LST data with the DINEOF method in advance and subsequently obtained spatially complete all-weather LST data. • We propose a new strategy for fusing thermal infrared LST and microwave LST • This strategy combines the merits of CDF matching and MKF approaches. • The fused LST data have a good validation accuracy • This strategy is used to generate spatial complete all-weather LST data over China landmass. [ABSTRACT FROM AUTHOR]

Underground coal mining with high groundwater levels causes many environmental problems, one of the main ones being subsidence waterlogging. After the subsidence waterlogging, the land faces many problems, such as insufficient land development and difficult and costly land restoration. Therefore, it is necessary to monitor the spatial range and temporal trajectory pattern of surface subsidence in a timely manner. When information about underground mining is lacking, it is difficult by using remote sensing alone to identify and distinguish (i) natural water, (ii) engineering water, and (iii) subsidence waterlogging. In this study, we used the Google Earth Engine platform to develop a method to detect subsidence waterlogging area and the disturbance year. The method includes pixel-based trajectory extraction and object-based water type recognition. First, LandTrendr is used to extract the change water (engineering water and subsidence waterlogging) pixels and its disturbance year. Then, the morphological method to further eliminate engineering water patches, so as to extract subsidence waterlogging. We selected the Panxie mining area in Huainan, China as the study area. Using 33 years of Landsat time-series data to generate values of the annual water frequency index, maps of the year of water accumulation caused by underground coal mining and the year of restoration during 1989–2016 are drawn with accuracies of 86.5% and 80.7%, respectively. The results show that from 1989 to 2016, the accumulated area of subsidence waterlogging was 7715.25 ha, accounting for 14.5% of the total area of the study area, of which 75.8% occurred from 2008 to 2016. Furthermore, the accumulated area of restoration was 207.18 ha, which occurred after 2007 and accounts for 2.7% of the total area of subsidence waterlogging. Based on the analysis results of the changes of waterlogging types, the best time window for restoration of the waterlogging area is 3y after water accumulation. The main innovation of this paper is to make use of the temporal heterogeneity (Number of year in patches, Y_num) and morphological index (Landscape shape index in patches, LSI) of the change water to distinguish engineering water and subsidence waterlogging. • Map of subsidence waterlogging caused by mining coal using Landsat time-series. • The annual water frequency index is capable to capture the water variability. • A spatial-temporal algorithm identifies the timing of subsidence waterlogging • The algorithm identifies the timing of backfill after subsidence waterlogging. • The algorithm is applicable to other mining areas too. [ABSTRACT FROM AUTHOR]