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Realistic modeling of high-resolution earth radiation signals in the visible-thermal spectral domain remains difficult, due to the complex radiation interdependence induced by the heterogeneous and rugged features of land surface. To find the trade-off between accuracy and efficiency for image simulation, this paper established a unified simulation framework for the entire visible-thermal spectral domain, based on the energy balance between solar-reflected and thermal radiation components over rugged surfaces. Considering the joint contributions of atmospheric and topographic adjacency effects, three spatial–spectral convolution kernels were uniformly designed to quantify the topographic irradiance, the trapping effect, and the atmospheric adjacency effect. Radiation signal simulation was implemented in three forms: land surface temperature (LST), bottom of atmosphere (BOA) radiance, and top of atmosphere (TOA) radiance. The accuracy was validated with onboard data from China's Gaofen-5 visual and infrared multispectral sensor (VIMS) over rugged desert. The simulation results demonstrate that the root mean square of relative deviations between the simulated and onboard TOA radiance are related to terrain, as 3–17% and 6–38% for the summer and winter scene, respectively. The evaluation of radiance components indicates the utility of the simulation framework to quantify the uncertainty associated with atmosphere and terrain coupling effects, in the sensor design and operation stages. [ABSTRACT FROM AUTHOR]

In this paper, we present a challenging stereo-inertial dataset collected onboard a sports utility vehicle (SUV) for the tasks of visual-inertial odometry (VIO), simultaneous localization and mapping (SLAM), autonomous driving, object detection, and other computer vision techniques. We recorded a large set of time-synchronized stereo image sequences (2 × 1280 × 720 @ 30 fps RGB) and corresponding inertial measurement unit (IMU) readings (400 Hz) from a Stereolabs ZED2 camera, along with centimeter-level-accurate six-degree-of-freedom ground truth (100 Hz) from a u-blox GNSS-IMU navigation device with real-time kinematic correction signals. The dataset comprises 34 sequences recorded during November 2020 in Wuhan, the largest city of Central China. Further, the dataset contains abundant unique urban scenes and features of a complex modern metropolis, which have rarely appeared in previously released benchmarks. Results from milestone VIO/SLAM algorithms reveal that methods exhibiting excellent performance on established datasets such as KITTI and EuRoC perform unsatisfactorily when moved outside the laboratory to the real world. We expect our dataset to reduce this limitation by providing more challenging and diverse scenarios to the research community. The full dataset with raw and calibrated data is publicly available along with a lightweight MATLAB/Python toolbox for preprocessing and evaluation. The dataset can be downloaded in its entirety from the uniform resource locator (URL) we provide in the main text. [ABSTRACT FROM AUTHOR]

The Zhurong rover of China's Tianwen-1 mission started its inspection tour on Mars in May 2021. As a major scientific payload onboard the Zhurong rover, the Mars Surface Composition Detector (MarSCoDe) instrument adopts laser-induced breakdown spectroscopy (LIBS) to detect and analyze the chemical composition of Martian materials. This paper introduces an experimental platform capable of establishing a simulated Martian atmospheric environment, in which a duplicate model of the MarSCoDe flight model is placed. In the simulated environment, the limit vacuum degree can reach 10−5 Pa level, the temperature can change from −190 °C to +180 °C, and different gases can be filled and mixed according to desired proportion. Moreover, the sample stage can move along a track inside the vacuum chamber, enabling the detection distance to vary from 1.5 m to 7 m. Preliminary experimental results indicate that this platform is able to simulate the scenario of MarSCoDe in situ LIBS detection on Mars well. [ABSTRACT FROM AUTHOR]

The southwest mountainous area of China is one of the areas with the most landslides in the world. In this paper, we used Ya'an City and Garzê Tibetan Autonomous Prefecture in Sichuan Province as the research areas to explore the identification application effects of large-area potential landslides using synthetic aperture radar (SAR) data with different wavelength types (Sentinel-1, ALOS-2), different processing methods (SBAS-InSAR, Stacking-InSAR), and different geological environmental conditions. The results show the following: (1) The effect of identifying landslides with different slope directions is largely affected by the satellite orbit direction; when we identify landslide hazards across a large area, the joint monitoring mode of ascending and descending orbit data is required. (2) The period of monitoring affects the identification effect of potential landslides when landslide identification is carried out in southwestern China; the InSAR monitoring period is recommended to be more than 2 years. (3) In different geological environmental regions, SBAS technology and Stacking technology have their own advantages; Stacking technology identifies more potential landslides, and SBAS technology identifies potential landslides with higher accuracy; (4) the degree of vegetation coverage has a great impact on the landslide identification effect of different SAR data sources. In low-density vegetation coverage areas, the landslide identification result using Sentinel-1 data seems to be better than the result using ALOS-2 data. In high-density vegetation coverage areas, the landslide identification result using ALOS-2 data is better than that using Sentinel-1 data. [ABSTRACT FROM AUTHOR]

Net ecosystem productivity (NEP) plays an important role in understanding ecosystem function and the global carbon cycle. In this paper, the key parameters of the Carnegie Ames Stanford Approach (CASA) model, maximum light use efficiency (εmax), was optimized by using vegetation classification data. Then, the NEP was estimated by coupling the optimized CASA model, geostatistical model of soil respiration (GSMSR) and the soil respiration–soil heterotrophic respiration (Rs-Rh) relationship model. The ground observations from ChinaFLUX were used to verify the NEP estimation accuracy. The results showed that the R2 of the optimized CASA model increased from 0.411 to 0.774, and RMSE decreased from 21.425 gC·m−2·month−1 to 12.045 gC·m−2·month−1, indicating that optimizing CASA model by vegetation classification data was an effective method to improve the estimation accuracy of NEP. On this basis, the spatial and temporal distribution of NEP in China was analyzed. The research indicated that the monthly variation of NEP in China was a single peak curve with summer as the peak, which generally presented the pattern of southern region > northern region > Qinghai–Tibet region > northwest region. Furthermore, from 2001 to 2016, most regions of China showed a non-significant level upward trend, but main cropland (e.g., North China Plain and Northeast Plain) and some grassland (e.g., Ngari in Qinghai–Tibet Plateau and Xilin Gol League in Inner Mongolia) showed a non-significant-level downward trend. The study can deepen the understanding of the distribution of carbon sources/sinks in China, and provide a reference for regional carbon cycle research. [ABSTRACT FROM AUTHOR]

Land use/cover change has become an indispensable part of global eco-environmental change research. The Tarim River Basin is the largest inland river basin in China. It is also one of the most ecologically fragile areas in the country, with greening and desertification processes coexisting. This paper analyzes the evolution of land-use/cover change in the Tarim River Basin over the past 30 years based on remote sensing data. The research also explores the contribution of conversion between different land types to the ecological environment by selecting methods, such as transfer matrix and ecological contribution rate. Results indicate that grassland and barren land are the main land types in the region, accounting for 72.46% and 18.87% of the basin area, respectively. From 1990 to 2019, cropland area increased from 33,585.89 km2 to 52,436.40 km2, an increase of 56.13%, while barren land areas decreased from 781,380.57 km2 to 760,783.29 km2. Most of the land-use conversion was grassland to other land types and other land types to barren land. Since 1990, the conversion of barren land to grassland and cropland in the basin has led to ecological improvement, whereas the conversion of grassland to cropland has caused deterioration, but with a generally improving trend. It is anticipated that, over the next decade, changes in land types will involve increases in grassland and woodland area, decreases in barren land and cropland, and an overall improvement in the ecological environment in the watershed. Since agriculture and animal husbandry are the main industries in the Tarim River Basin and the land-use structure is dominated by cropland and grassland, several key measures should be implemented. These include improving land use, rationalizing the use of water and soil resources, slowing down the expansion of cropland, and alleviating the contradiction between humans and land, with the ultimate aim of achieving sustainable development of the social economy and ecological environment. [ABSTRACT FROM AUTHOR]

On 23 November 2021, the Gaofen-3-02 (GF3-02) satellite was successfully launched in the Jiuquan Satellite Launch Center of China. The primary payload is C-band Synthetic Aperture Radar (SAR), with a maximum resolution of 1 m, and includes 12 imaging modes such as Spotlight, Strip, and TOPSAR, which will play an essential role in marine environment monitoring. As an important marine environmental parameter, the wind speed accuracy retrieved by GF3-02 SAR directly reflects its performance and effectiveness as an operational product. Therefore, based on the wind data of buoys of the National Data Buoy Center (NDBC), ECMWF reanalysis V5 (ERA5), and HY-2B Scatterometer (SCA), a preliminary accuracy assessment of the wind speed retrieved by GF3-02 SAR is carried out in this paper. The wind speed retrieval accuracy of GF3-02 SAR in the co-polarization (HH+VV) data under different Geophysical Model Functions (GMFs) is discussed by using 478 level-1A Single Look Complex (SLC) ocean products acquired in Quad-Polarization Strip I (QPSI) and produced by the National Satellite Ocean Application Service (NSOAS) from January to March 2022. The results show that the optimal root mean square errors (RMSE) are 1.40 m/s, 1.18 m/s, and 1.24 m/s for the VV polarization and 1.39 m/s, 1.19 m/s, and 1.52 m/s for the HH polarization compared to the NDBC wind speed, the ERA5 wind speed, and the HY-2B SCA wind speed, respectively. The preliminary results show that GF3-02 SAR has good wind speed retrieval ability and can meet the needs of operational products. [ABSTRACT FROM AUTHOR]

Natural disaster response and assessment are key elements of natural hazard monitoring and risk management. Currently, the existing systems are not able to meet the specific needs of many regional stakeholders worldwide; traditional approaches with field surveys are labor-intensive, time-consuming, and expensive, especially for severe disasters that affect a large geographic area. Recent studies have demonstrated that Earth observation (EO) data and technologies provide powerful support for the natural disaster emergency response. However, challenges still exist in support of the entire disaster lifecycle—preparedness, response, and recovery—which build the gaps between the disaster Spatial Data Infrastructure (SDI) already-in-place requirements and the EO capabilities. In order to tackle some of the above challenges, this paper demonstrates how to facilitate typhoon-triggered flood disaster-ready information delivery using an SDI services approach, and proposes a web-based remote sensing disaster decision support system to facilitate natural disaster response and impact assessment, which implements on-demand disaster resource acquisition, on-the-fly analysis, automatic thematic mapping, and decision report release. The system has been implemented with open specifications to facilitate interoperability. The typhoons and floods in Hainan Province, China, are used as typical scenarios to verify the system's applicability and effectiveness. The system improves the automation level of the natural disaster emergency response service, and provides technical support for regional remote-sensing-based disaster mitigation in China. [ABSTRACT FROM AUTHOR]

Landslides, a major natural geohazard, obstruct municipal constructions and may destroy villages and towns, at worst causing significant casualties and economic losses. Interferometric Synthetic Aperture Radar (InSAR) technique offers distinct advantages on landslide detection and monitoring. In this paper, a more systematic workflow is designed for InSAR study of landslides, in terms of three levels: (i) early detection on regional scale, (ii) three-dimensional (3D) surface displacement rates estimation on detailed scale, and (iii) time series analysis on long-term temporal scale. The proposed workflow is applied for landslide research on the Xiaojiang River Basin, China, using ascending and descending Sentinel-1 images acquired from March 2017 to May 2019. First, the landslide inventory has been mapped and updated using InSAR stacking method, supporting geohazard prevention on a regional scale. A total of 22 active landslides are identified, ranging from medium to super large scale. Compared with the existing inventory, three unrecorded landslides are newly detected by our approach, and five recorded landslides are detected significant expansion of their boundaries. Then, specific to a detected landslide, Baobao landslide, a Total Least Squares–Kalman Filter-based approach is presented. Two outcomes are provided for further spatial-temporal pattern analysis: 3D displacement rates, providing an intuitive insight on the spatial characteristics and sliding direction of landslide, which are analyzed to deep the understanding of its kinematic mechanism, and long-term time series, which contribute to deduce the dynamic evolution of landslide, presenting benefits in landslide forecasting. [ABSTRACT FROM AUTHOR]

Extraterrestrial solar radiation (ESR) is the essential basic background for solar radiation, which determines the occurrence of the weather and atmospheric phenomena. Since the influence of ESR variation on actual rugged terrain is a diverse, complex, and dynamic process, simulating ESR over a large spatial-temporal span, especially with a high-resolution digital elevation model (DEM), is a significant challenge. In this paper, we developed a new scheme for simulating ESR over the entirety of China using a DEM with a resolution of 30 m. To fully consider regional terrain status, the feature variables used were elevation, slope, and aspects of the located grid and the surrounding four grids to reveal the topography. In addition, latitude was used as a feature variable to consider the geographical location, and the month number was used to consider the duration. On the basis of different geographical locations, the training dataset was established from 20,000 grids. With the feature variable composition and training dataset, a backpropagation artificial neural network (BP ANN) was found to have the best performance compared with the other three machine learning methods in simulating ESR for a DEM. In terms of the proposed scheme and BP ANN, we drew an ESR map of China with a resolution of 30 m. The determination coefficient of the simulation result achieved 0.99 and the root-mean-square error was less than 50 MJ/m2 in all sample areas, confirming its remarkable accuracy. In terms of efficiency, the time consumption of ESR simulated using the proposed scheme shrinks over 150 times in all sample areas compared to that simulated via the theoretical model. Simultaneously, the developed scheme was also used to simulate an ESR for a DEM with a resolution of 90 m to verify the universality and robustness of the developed scheme. In addition, we used the proposed scheme to derive the direct solar radiation and global solar radiation, thereby further proving the reliability and applicability of our study. Overall, our work convincingly proved that the proposed scheme is a potential and effective approach for quickly simulating ESR with high accuracy. This study provides the basis for different solar radiation inversions of long time series and large spatial scales, offering additional insights for simulating ESR on a large spatial-temporal span. [ABSTRACT FROM AUTHOR]

The GF-7 satellite is China's first civil sub-meter resolution stereo mapping satellite, aiming at 1:10,000-scale mapping. To achieve this goal, apart from the stereo optical cameras that reach sub-meter resolution, the GF-7 satellite is equipped with a laser altimetry system capable of obtaining three-dimensional laser points (LPs) with high elevation accuracy. However, the combination of laser altimetry data and optical stereo images has not been thoroughly studied. In this paper, we exploit the images recorded by the highly integrated laser footprint cameras and propose a hierarchical phase correlation method based on a geographic pyramid for the registration of laser altimetry data and high-resolution optical stereo images, which lays a solid foundation for the following combined adjustment. Experiments show that the proposed registration method can automatically locate the LPs on high-resolution stereo images and meet the requirements of bundle adjustment. A series of bundle adjustment experiments were carried out, showing that laser altimetry data can significantly enhance the vertical accuracy of optical image stereo mapping and that elevation accuracy can reach roughly 1.0 m (RSME) without ground control points. Therefore, this study could be a good guide for global high-precision DSM acquisition with the GF-7 satellite. [ABSTRACT FROM AUTHOR]

Gross floor area is defined as the product of number of building stories and its base area. Gross floor area acquisition is the core problem to estimate floor area ratio, which is an important indicator for many geographical analyses. High data acquisition cost or inherent defect of methods for existing gross floor area acquisition methods limit their applications in a wide range. In this paper we proposed three instance-wise gross floor area estimation methods in various degrees of end-to-end learning from monocular optical images based on the NoS R-CNN, which is a deep convolutional neural network to estimate the number of building stories. To the best of our knowledge, this is the first attempt to estimate instance-wise gross floor area from monocular optical satellite images. For comparing the performance of the proposed three methods, experiments on our dataset from nine cities in China were carried out, and the results were analyzed in detail in order to explore the reasons for the performance gap between the different methods. The results show that there is an inverse relationship between the model performance and the degree of end-to-end learning for base area estimation task and gross floor area estimation task. The quantitative and qualitative evaluations of the proposed methods indicate that the performances of proposed methods for accurate GFA estimation are promising for potential applications using large-scale remote sensing images. The proposed methods provide a new perspective for gross floor area/floor area ratio estimation and downstream tasks such as population estimation, living conditions assessment, etc. [ABSTRACT FROM AUTHOR]

A unified height datum is essential for global geographic information resource construction, ecological environment protection, and scientific research. The goal of this paper is to derive the geopotential value for the Chinese height datum (CNHD) in order to realize the height datum unification in China. The estimation of height datum geopotential value usually depends on high-precision global gravity field models (GFMs). The satellite gravity missions of the Gravity Recovery and Climate Experiment (GRACE) and Gravity field and steady-state Ocean Circulation Exploration (GOCE) provide high-accuracy, medium–long-wavelength gravity field spectra, but satellite-only GFMs are limited to medium–long wavelengths, which will involve omission errors. To compensate for the omission errors in satellite-only GFMs, a spectral expansion approach is used to obtain the refined gravity field models using the EGM2008 (Earth Gravitational Model 2008) and residual terrain model (RTM) technique. The refined GFMs are evaluated by using high-quality GNSS/leveling data, the results show that the quasi-geoid accuracy of the refined DIR_R6_EGM2008_RTM model in China has optimal accuracy and, compared with the EGM2008 model and the DIR_R6 model, this refined model in China is improved by 9.6 cm and 21.8 cm, and the improvement ranges are 35.7% and 55.8%, respectively. Finally, the geopotential value of the Chinese height datum is estimated to be equal to 62,636,853.29 m2s−2 with respect to the global reference level defined by W0 = 62,636,853.4 m2s−2 by utilizing the refined DIR_R6_EGM2008_RTM model and 1908 high-quality GNSS/leveling datapoints. [ABSTRACT FROM AUTHOR]

The key to simulating soil erosion is to calculate the vegetation cover (C) factor. Methods that apply remote sensing to calculate the C factor at a regional scale cannot directly use the C factor formula. That is because the C factor formula is obtained by experiments, and needs the coverage ratio data of croplands, woodlands, and grasslands at a standard plot scale. In this paper, we present a C factor conversion method from a standard plot to a km-sized grid based on large sample theory and multi-scale remote sensing. The results show that: (1) Compared with the existing C factor formula, our method is based on the coverage ratio of croplands, woodlands, and grasslands on a km-sized grid, and takes the C factor formula obtained from the standard plot experiment and applies it to a regional scale. This method improves the applicability of the C factor formula, and can satisfy the need to simulate soil erosion in large areas; (2) The vegetation coverage obtained by remote sensing interpretation is significantly consistent (paired samples t-test, t = −0.03, df = 0.12, 2-tail significance p < 0.05) and significantly correlated with the measured vegetation coverage; (3) The C factor of the study area is smaller in the middle, southern, and northern regions, and larger in the eastern and western regions. The main reason for that is the distribution of woodlands, the Hunshandake and Horqin sandy lands, and the valleys affected by human activities; (4) The method presented in this paper is more meticulous than the C factor method based on the vegetation index, improves the applicability of the C factor formula, and can be used to simulate soil erosion on a large scale and provide strong support for regional soil and water conservation planning. [ABSTRACT FROM AUTHOR]

The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS DNB) and the Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer products (MODIS NDVI). A Back Propagation (BP) neural network based one-class classification method, the Present-Unlabeled Learning (PUL) algorithm, is employed to classify images into urban and non-urban areas. Experiments are conducted in mainland China (excluding surrounding islands) to detect urban areas in 2012. Results show that the proposed model can successfully map urban area with a kappa of 0.842 on the pixel level. Most of the urban areas are identified with a producer's accuracy of 79.63%, and only 10.42% the generated urban areas are misclassified with a user's accuracy of 89.58%. At the city level, among 647 cities, only four county-level cities are omitted. To evaluate the effectiveness of the proposed scheme, three contrastive analyses are conducted: (1) comparing the urban map obtained in this paper with that generated by the Defense Meteorological Satellite Program/Operational Linescan System Nighttime Light Data (DMSP/OLS NLD) and MODIS NDVI and with that extracted from MCD12Q1 in MODIS products; (2) comparing the performance of the integration of NPP-VIIRS DNB and MODIS NDVI with single input data; and (3) comparing the classification method used in this paper (PUL) with a linear method (Large-scale Impervious Surface Index (LISI)). According to our analyses, the proposed classification scheme shows great potential to map regional urban extents in an effective and efficient manner. [ABSTRACT FROM AUTHOR]

In the past decades, continuous efforts have been made at a national level to reduce Nitrogen Dioxide (NO2) emissions in the atmosphere over China. However, public concern and related research mostly deal with tropospheric NO2 columns rather than ground-level NO2 concentrations, but actually ground-level NO2 concentrations are more closely related to anthropogenic emissions, and directly affect human health. This paper presents one method to derive the ground-level NO2 concentrations using the total column of NO2 observed from the Ozone Monitoring Instrument (OMI) and the simulations from the Community Multi-scale Air Quality (CMAQ) model in China. One year's worth of data from 2014 was processed and the results compared with ground-based NO2 measurements from a network of China's National Environmental Monitoring Centre (CNEMC). The standard deviation between ground-level NO2 concentrations over China, the CMAQ simulated measurements and in-situ measurements by CNEMC for January was 21.79 μg/m3, which was improved to a standard deviation of 18.90 μg/m3 between our method and CNEMC data. Correlation coefficients between the CMAQ simulation and in-situ measurements were 0.75 for January and July, and they were improved to 0.80 and 0.78, respectively. Our results revealed that the method presented in this paper can be used to better measure ground-level NO2 concentrations over China. [ABSTRACT FROM AUTHOR]

Coal fires that are induced by natural spontaneous combustion or result from human activities occurring on the surface and in underground coal seams destroy coal resources and cause serious environmental degradation. Thermal infrared image data, which directly measure surface temperature, can be an important tool to map coal fires over large areas. As the first of two parts introducing our coal fire detection method, this paper proposes a self-adaptive threshold-based approach for coal fire detection using ASTER thermal infrared data: the self-adaptive gradient-based thresholding method (SAGBT). This method is based on an assumption that the attenuation of temperature along the coal fire's boundaries generates considerable numbers of spots with extremely high gradient values. The SAGBT method applied mathematical morphology thinning to skeletonize the potential high gradient buffers into the extremely high gradient lines, which provides a self-adaptive mechanism to generate thresholds according to the thermal spatial patterns of the images. The final threshold was defined as an average temperature value reading from the high temperature buffers (segmented by 1.0 σ from the mean) and along a sequence of extremely high gradient lines (thinned from the potential high gradient buffers and segmented within the lower bounds, ranging from 0.5 σ to 1.5 σ and with an upper bound of 3.2 σ, where σ is the standard deviation), marking the coal fire areas. The SAGBT method used the basic outer boundary of the coal-bearing strata to simply exclude false alarms. The intermediate thresholds reduced the coupling with the temperature and converged by changing the potential high gradient buffers. This simple approach can be economical and accurate in identifying coal fire areas. In addition, it allows for the identification of thresholds using multiple ASTER TIR scenes in a consistent and uniform manner, and supports long-term coal fire change analyses using historical images in local areas. This paper focuses on the introduction of the methodology. Furthermore, an improvement to SAGBT is proposed. In a subsequent paper, subtitled "Part 2, Validation and Sensitivity Analysis," we address satellite-field simultaneous observations and report comparisons between the retrieved thermal anomalies and field measurements in different aspects to prove that the coal fires are separable by the SAGBT method. These comparisons allowed us to estimate the accuracy and biases of the SAGBT method. As an application of the SAGBT, a relationship between coal fires' decadal variation and coal production was also examined. Our work documented a total area increase in the beginning of 2003, which correlates with increased mining activities and the rapid increase of energy consumption in China during the decade (2001-2011). Additionally, a decrease in the total coal fire area is consistent with the nationally sponsored fire suppression efforts during 2007-2008. It demonstrated the applicability of SAGBT method for long-term change detection with multi-temporal images. [ABSTRACT FROM AUTHOR]

Regional economic inequality is a persistent problem for all nations. Meanwhile, satellite-derived night-time light (NTL) data have been extensively used as an efficient proxy measure for economic activity. This study firstly proposes a new method for correction of the NTL data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite and then applies the corrected NTL data to estimate gross domestic product (GDP) at a multi-scale level in China from 2014 to 2017. Secondly, incorporating the two-stage nested Theil decomposition method, multi-scale level regional inequalities are investigated. Finally, by using scatter plots, this paper identifies the relationship between the regional inequality and the level of economic development. The results indicate that: (1) after correction, the NPP-VIIRS NTL data show a statistically positive correlation with GDP, which proves that our correction method is scientifically effective; (2) from 2014 to 2017, overall inequality, within-province inequality, and between-region inequality all declined, However, between-province inequality increased slightly. As for the contributions to overall regional inequality, the within-province inequality was the highest, while the between-province inequality was the lowest; (3) further analysis of within-province inequality reveals that economic inequalities in coastal provinces in China are smaller than in inland provinces; (4) China's economic development plays an important role in affecting regional inequality, and the extent of influence of economic development on regional inequality is varied across provinces. [ABSTRACT FROM AUTHOR]

Land use and land cover changes (LUCC) are thought to be amongst the most important impacts exerted by humans on climate. However, relatively little research has been carried out so far on the effects of LUCC on extreme climate change other than on regional temperatures and precipitation. In this paper, we apply a regional weather research and forecasting (WRF) climate model using LUCC data from Heilongjiang Province, that was collected between the 1900s and 2010s, to explore how changes in forest cover influence extreme temperature indexes. Our selection of extreme high, low, and daily temperature indexes for analysis in this study enables the calculation of a five-year numerical integration trail with changing forest space. Results indicate that the total forested area of Heilongjiang Province decreased by 28% between the 1900s and 2010s. This decrease is most marked in the western, southwestern, and northeastern parts of the province. Our results also reveal a remarkable correlation between change in forested area and extreme high and low temperature indexes. Further analysis enabled us to determine that the key factor explaining increases in extreme high temperature indexes (i.e., calculated using the number of warm days, warm nights, as well as tropical nights, and summer days) is decreasing forest area; data also showed that this factor caused a decrease in extreme low temperature indexes (i.e., calculated using the number of cold days and cold nights, as well as frost days, and ice days) and an increase in the maximum value of daily minimum temperature. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Positive correlations are also present between decreasing forest area, the more frequent occurrence of extreme high temperature events, and a rise in the maximum value of daily minimum temperature. At the same time, we found clear negative correlations between decreasing forest area and less frequent occurrence of extreme low temperature events. [ABSTRACT FROM AUTHOR]

As the latest L-band mission to date, evaluation of the Soil Moisture Active Passive (SMAP) products is one of its post-launch objectives. However, almost all previous studies have been conducted at the core validation sites (CVS) of the SMAP mission. This paper presents an evaluation of the SMAP soil moisture Level 3 (L3) and Level 4 (L4) products under different vegetation types at multiple tempo-spatial scales over the upper reach of the Heihe River Watershed, a topographically complex mountainous area in Northwest China. This was done through comparisons of the L3 and L4 products with ground-based observations from a sparse in situ network of permanent and temporary stations from 1 April 2015 to 22 June 2017. Results show that, compared with in situ observations at point scale, both the L3 and L4 products represent the temporal trends of the in situ observations in the study area well, with R values of 0.601 and 0.538 for the L3 ascending and descending products, respectively, and ranging from 0.353 to 0.410 for the L4 product at eight overpassing moments. However, because of the uncertainties of brightness temperatureTBp and effective temperature Teff as well as their propagations in the inversion algorithm, both products did not achieve the accuracy of 0.04 m³/m³ in mountainous area. These uncertainties also result in the "dry bias" of the SMAP products in almost all the evaluations to date. Compared with areal average values at the watershed scale, the L3 product is far beyond the accuracy of 0.04 m³/m³ and the L4 product basically achieves the accuracy. In vegetation-covered land, the suitability and the variability of the coefficient bp result in both products performing best in cropland, then coniferous forest, sparse grassland, dense grassland, and alpine meadow, and worst in shrub. In barren land, the errors in estimating surface roughness h caused by the complex topography lead to poor performance of the SMAP products. With the relative errors of the SMAP brightness temperature observations and the corresponding land model forecast in the assimilation; the L3 and L4 products show different performance at both temporal and spatial scales; and the L3 product provides more reliable soil moisture estimates in the study area. Based on the results of this study, we propose: quantifying the uncertainties in estimating brightness temperature TBp and effective temperature Teff; determine coefficient bp and surface roughness h factor under various conditions; improving Goddard Earth Observing Model System Version 5 (GEOS-5) model; and deriving the SMAP-only climatology to improve the SMAP soil moisture estimates in the future. [ABSTRACT FROM AUTHOR]

Detailed studies on the spatiotemporal patterns of urban agglomeration in the Middle Yangtze River Basin (MYRB) are rare. This paper analyzed the spatiotemporal patterns of urbanization in the MYRB using multi-temporal remote sensing data circa 2000, 2008 and 2016 integrated with geographic information system (GIS) techniques and landscape analysis approaches. A multi-level analysis of the rate and intensity, type as well as the landscape changes of urban expansion at regional, prefectural and inner-city levels was performed. Results show that the MYRB experienced rapid urban expansion with an annual expansion rate of 3.199%, especially in the Chang-Zhu-Tan and Poyang Lake metropolitan areas. The small and medium cities presented faster urban expansion than the larger cities with annual growth rates three times the average level. Urban expansion within the three capital cities was further analyzed in detail. It is found that outlying expansion and edge-expansion were the dominant growth patterns at all the three levels. Although urbanization in the MYRB has a remarkable increase in the past sixteen years, its annual growth rate of urban land expansion has fallen behind the three other large urban agglomerations in China as a result. Finally, the spatial evolution of the socioeconomic structure of the MYRB was further explored. It indicated that urban land was distributed mainly along the "northwest-southeast" direction and that the economic spatial interactions among cities showed a pattern of "multi-polarization and fragmentation", which illustrates the weak radiative driving forces of the central cities. The MYRB urban agglomeration faces a great challenge to manage trades-offs between narrowing the intra-regional disparity and maintaining synergetic development among cities. [ABSTRACT FROM AUTHOR]

Nighttime light data can characterize urbanization, economic development, population density, energy consumption and other human activities. Additionally, carbon dioxide (CO2) emissions are closely related to the scope and intensity of human activities. In this study, we assess the utility of nighttime light data as a powerful tool to reflect CO2 emissions from energy consumption, analyze the uncertainty associated with different nighttime light data for modeling CO2 emissions, and provide guidance and a reference for modeling CO2 emissions based on nighttime light data. In this paper, Mainland China was taken as a case study, and nighttime light datasets (the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light data and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data) as well as a global gridded CO2 emissions dataset (PKU-CO2) were used to perform simple regressions at provincial, prefectural and 0.1° × 0.1° grid levels, respectively. The analyses are aimed at exploring the accuracy and uncertainty of DMSP-OLS and NPP-VIIRS nighttime light data in modeling CO2 emissions at different spatial scales. The improvement of nighttime light index and the potential factors influencing the effects of modeling CO2 emissions based on nighttime light datasets were also explored. The results show that DMSP-OLS is superior to NPP-VIIRS in modeling CO2 emissions at all spatial scales, and the bigger the scale, the more evident the advantages of DMSP-OLS. When modeling CO2 emissions with nighttime light datasets, not only the total amount of lights within a given statistical unit but also the agglomeration degree of lights should be taken into account. Furthermore, the geographical location and socio-economic conditions at the study site, such as gross regional product per capita (GRP per capita), population, and urbanization were shown to have an impact on the regression effect of the nighttime lights-CO2 emissions model. The regression effect was found to be better at higher latitude and longitude areas with higher GRP per capita and higher urbanization, while population showed little effect on the regression effect of the nighttime lights - CO2 emissions model. The limitation of this study is that the thresholds of potential factors are unclear and the quantitative guidance is insufficient. [ABSTRACT FROM AUTHOR]

Social media texts spontaneously produced and uploaded by the public contain a wealth of disaster information. As a supplementary data source for remote sensing, they have played an important role in disaster reduction and emergency response in recent years. However, social media also has certain flaws, such as insufficient location information, etc. This affects the efficiency of combining these data with remote sensing data. For flood disasters in particular, extensively flooded areas limit the distribution of social media data, which makes it difficult for these data to function as they should. In this paper, we propose a disaster reduction framework to solve these problems. We first used an approach that was based on search engine and lexical rules to automatically extract disaster-related location information from social media texts. Then, we combined the extracted information with the upload location of social media itself to construct location-pointing relationships. These relationships were used to build a new social network, which can be used in combination with remote sensing images for disaster analysis. The analysis integrated the advantages of social media and remote sensing. It can not only provide macro disaster information in the study area but can also assist in evaluating the disaster situation in different flooded areas from the perspective of public observation. In addition, the timeliness of social media data also improved the continuity and situational awareness of flood monitoring. A case study of the flood disaster in the Yangtze River Basin in China in 2020 was used to verify the effectiveness of the method described in this paper. [ABSTRACT FROM AUTHOR]

Due to the country's rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, R2. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated R2 of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city. [ABSTRACT FROM AUTHOR]

Interests in synthetic aperture radar (SAR) data analysis is driven by the constantly increased spatial resolutions of the acquired images, where the geometries of scene objects can be better defined than in lower resolution data. This paper addresses the problem of the built-up areas extraction in high-resolution (HR) SAR images, which can provide a wealth of information to characterize urban environments. Strong backscattering behavior is one of the distinct characteristics of built-up areas in a SAR image. However, in practical applications, only a small portion of pixels characterizing the built-up areas appears bright. Thus, specific texture measures should be considered for identifying these areas. This paper presents a novel texture measure by combining the proposed labeled co-occurrence matrix technique with the specific spatial variability structure of the considered land-cover type in the fuzzy set theory. The spatial variability is analyzed by means of variogram, which reflects the spatial correlation or non-similarity associated with a particular terrain surface. The derived parameters from the variograms are used to establish fuzzy functions to characterize the built-up class and non built-up class, separately. The proposed technique was tested on TerraSAR-X images acquired of Nanjing (China) and Barcelona (Spain), and on a COSMO-SkyMed image acquired of Hangzhou (China). The obtained classification accuracies point out the effectiveness of the proposed technique in identifying and detecting built-up areas. [ABSTRACT FROM AUTHOR]

To prevent environmental degradation, China's central government launched the "Grain for Green Project" (GGP) in 1999. Since its beginning, the effects and influences of the GGP have been hotly debated among domestic and international scholars and policymakers. This paper is taking the County of Yanchuan in the Loess Plateau as a case study, examines the spatio-temporal patterns of cropland conversion in response to the GGP. This research is methodologically based on remote sensing (RS) and geographic information systems (GIS), and also employs personal interviews with local government officials and farmers. The results show that land use/cover patterns in Yanchuan County have changed dramatically after the implementation of GGP. Cropland has decreased remarkably, while orchard land and sparse forest has increased significantly: 23.84% of cropland was converted to orchard, and 22.25% to sparse forest. Simultaneously, the landscape has become more fragmented but also more diversified, forestland has become more dominant. A total of 61.19% of the total converted cropland was on slopes greater than 15 degrees, 64.85% of which was lower-grade land. The converted cropland is mostly located in more accessible areas for convenient management. Partially affected by farmers' self-willingness, sloping cropland was preferred to orchard (economic forest), and some gentle slope (less than 15 degrees) or higher-grade cropland were involved in the GGP. To maintain and reinforce the achievements of the GGP and further contribute to the GGP's sustainability and rural development, the paper recommends that the Chinese government should build a continuous compensation mechanism for the households who lost cropland for the GGP while improving the productivity of flat cropland. [ABSTRACT FROM AUTHOR]

High-quality training samples are essential for accurate land cover classification. Due to the difficulties in collecting a large number of training samples, it is of great significance to collect a high-quality sample dataset with a limited sample size but effective sample distribution. In this paper, we proposed an object-oriented sampling approach by segmenting image blocks expanded from systematically distributed seeds (object-oriented sampling approach) and carried out a rigorous comparison of seven sampling strategies, including random sampling, systematic sampling, stratified sampling (stratified sampling with the strata of land cover classes based on classification product, Latin hypercube sampling, and spatial Latin hypercube sampling), object-oriented sampling, and manual sampling, to explore the impact of training sample distribution on the accuracy of land cover classification when the samples are limited. Five study areas from different climate zones were selected along the China–Mongolia border. Our research identified the proposed object-oriented sampling approach as the first-choice sampling strategy in collecting training samples. This approach improved the diversity and completeness of the training sample set. Stratified sampling with strata defined by the combination of different attributes and stratified sampling with the strata of land cover classes had their limitations, and they performed well in specific situations when we have enough prior knowledge or high-accuracy product. Manual sampling was greatly influenced by the experience of interpreters. All these sampling strategies mentioned above outperformed random sampling and systematic sampling in this study. The results indicate that the sampling strategies of training datasets do have great impacts on the land cover classification accuracies when the sample size is limited. This paper will provide guidance for efficient training sample collection to increase classification accuracies. [ABSTRACT FROM AUTHOR]

Accurate data on gross domestic product (GDP) at pixel level are needed to understand the dynamics of regional economies. GDP spatialization is the basis of quantitative analysis on economic diversities of different administrative divisions and areas with different natural or humanistic attributes. Data from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar-orbiting Partnership (NPP) satellite, are capable of estimating GDP, but few studies have been conducted for mapping GDP at pixel level and further pattern analysis of economic differences in different regions using the VIIRS data. This paper produced a pixel-level (500 m 500 m) GDP map for South China in 2014 and quantitatively analyzed economic differences among diverse geomorphological types. Based on a regression analysis, the total nighttime light (TNL) of corrected VIIRS data were found to exhibit R2 values of 0.8935 and 0.9243 for prefecture GDP and county GDP, respectively. This demonstrated that TNL showed a more significant capability in reflecting economic status (R2 > 0.88) than other nighttime light indices (R2 < 0.52), and showed quadratic polynomial relationships with GDP rather than simple linear correlations at both prefecture and county levels. The corrected NPP-VIIRS data showed a better fit than the original data, and the estimation at the county level was better than at the prefecture level. The pixel-level GDP map indicated that: (a) economic development in coastal areas was higher than that in inland areas; (b) low altitude plains were the most developed areas, followed by low altitude platforms and low altitude hills; and (c) economic development in middle altitude areas, and low altitude hills and mountains remained to be strengthened. [ABSTRACT FROM AUTHOR]

Rapid urbanization has resulted in great changes in rural landscapes globally. Using remote sensing data to quantify the distribution of rural settlements and their changes has received increasing attention in the past three decades, but remains a challenge. Previous studies mostly focused on the residential changes within a grid or administrative boundary, but not at the individual village level. This paper presents a new change detection approach for rural residential settlements, which can identify different types of rural settlement changes at the individual village level by integrating remote sensing and Geographic Information System (GIS) analyses. Using multi-temporal Landsat TM image data, this approach classifies villages into five types: “no change”, “totally lost”, “shrinking”, “expanding”, and “merged”, in contrast to the commonly used “increase” and “decrease”. This approach was tested in the Beijing metropolitan area from 1984 to 2010. Additionally, the drivers of such changes were investigated using multinomial logistic regression models. The results revealed that: (1) 36% of the villages were lost, but the total area of developed lands in existing villages increased by 34%; (2) Changes were dominated by the type of ‘expansion’ in 1984–1990 (accounted for 43.42%) and 1990–2000 (56.21%). However, from 2000 to 2010, 49.73% of the villages remained unchanged; (3) Both topographical factors and distance factors had significant effects on whether the villages changed or not, but their impacts changed through time. The topographical driving factors showed decreasing effects on the loss of rural settlements, while distance factors had increasing impacts on settlement expansion and merging. This approach provides a useful tool for better understanding the changes in rural residential settlements and their associations with urbanization. [ABSTRACT FROM AUTHOR]

Accurate yield estimation at the field scale is essential for the development of precision agriculture management, whereas at the district level it can provide valuable information for supply chain management. In this paper, Huan Jing (HJ) satellite HJ1A/B and Landsat 8 Operational Land Imager (OLI) images were employed to retrieve leaf area index (LAI) and canopy cover (CC) in the Yangling area (Central China). These variables were then assimilated into two crop models, Aquacrop and simple algorithm for yield (SAFY), in order to compare their performances and practicalities. Due to the models’ specificities and computational constraints, different assimilation methods were used. For SAFY, the ensemble Kalman filter (EnKF) was applied using LAI as the observed variable, while for Aquacrop, particle swarm optimization (PSO) was used, using canopy cover (CC). These techniques were applied and validated both at the field and at the district scale. In the field application, the lowest relative root-mean-square error (RRMSE) value of 18% was obtained using EnKF with SAFY. On a district scale, both methods were able to provide production estimates in agreement with data provided by the official statistical offices. From an operational point of view, SAFY with the EnKF method was more suitable than Aquacrop with PSO, in a data assimilation context. [ABSTRACT FROM AUTHOR]

Multiple policy projects have changed land use and land cover (LULC) in China’s rural regions over the past years, resulting in two types of rural settlements: new-fashioned and old-fashioned. Precise extraction of and discrimination between these two settlement types are vital for sustainable land use development. It is difficult to identify these two types via remote sensing images due to their similarities in spectrum, texture, and geometry. This study attempts to discriminate different types of rural settlements by using a spatial contextual information extraction method based on Gaofen 2 (GF-2) images, which integrate hierarchical multi-scale segmentation and landscape analysis. A preliminary LULC map was derived by using only traditional spectral and geometrical features from a finer scale. Subsequently, a vertical connection was built between superobjects and subobjects, and landscape metrics were computed. The vertical connection was used for assigning landscape contextual information to subobjects. Finally, a classification phase was conducted, in which only multi-scale contextual information was adopted, to discriminate between new-fashioned and old-fashioned rural settlements. Compared with previous studies on multi-scale contextual information, this paper employs landscape metrics to quantify contextual characteristics, rather than traditional spectral, textural, and topological relationship information, from superobjects. Our findings indicate that this approach effectively identified and discriminated two types of rural settlements, with accuracies over 80% for both producers and users. A comparison with a conventional top-down hierarchical classification scheme showed that this novel approach improved accuracy, precision, and recall. Our results confirm that multi-scale contextual information with landscape metrics provides valuable spatial information for classification, and indicates the practicability, applicability, and effectiveness of this synthesized approach in distinguishing different types of rural settlements. [ABSTRACT FROM AUTHOR]

The Soil Moisture Active Passive (SMAP) satellite makes coincident global measurements of soil moisture using an L-band radar instrument and an L-band radiometer. It is crucial to evaluate the errors in the newest L-band SMAP satellite-derived soil moisture products, before they are routinely used in scientific research and applications. This study represents the first evaluation of the SMAP radiometer soil moisture product over China. In this paper, a preliminary evaluation was performed using sparse in situ measurements from 655 China Meteorological Administration (CMA) monitoring stations between 1 April 2015 and 31 August 2016. The SMAP radiometer-derived soil moisture product was evaluated against two schemes of original soil moisture and the soil moisture anomaly in different geographical zones and land cover types. Four performance metrics, i.e., bias, root mean square error (RMSE), unbiased root mean square error (ubRMSE), and the correlation coefficient (R), were used in the accuracy evaluation. The results indicated that the SMAP radiometer-derived soil moisture product agreed relatively well with the in situ measurements, with ubRMSE values of 0.058 cm3·cm-3 and 0.039 cm3·cm-3 based on original data and anomaly data, respectively. The values of the SMAP radiometer-based soil moisture product were overestimated in wet areas, especially in the Southwest China, South China, Southeast China, East China, and Central China zones. The accuracies over croplands and in Northeast China were the worst. Soil moisture, surface roughness, and vegetation are crucial factors contributing to the error in the soil moisture product. Moreover, radio frequency interference contributes to the overestimation over the northern portion of the East China zone. This study provides guidelines for the application of the SMAP-derived soil moisture product in China and acts as a reference for improving the retrieval algorithm. [ABSTRACT FROM AUTHOR]

An object-based method is proposed in this paper for change detection in urban areas with multi-sensor multispectral (MS) images. The co-registered bi-temporal images are resampled to match each other. By mapping the segmentation of one image to the other, a change map is generated by characterizing the change probability of image objects based on the proposed change feature analysis. The map is then used to separate the changes from unchanged areas by two threshold selection methods and k-means clustering (k = 2). In order to consider the multi-scale characteristics of ground objects, multi-scale fusion is implemented. The experimental results obtained with QuickBird and IKONOS images show the superiority of the proposed method in detecting urban changes in multi-sensor MS images. [ABSTRACT FROM AUTHOR]

PROBA-V is a new global vegetation monitoring satellite launched in the second quarter of 2013 that provides data with a 100 m to 1 km spatial resolution and a daily to 10-day temporal resolution in the visible and near infrared (VNIR) bands. A major mission of the PROBA-V satellite is global agriculture monitoring, in which the accuracy of crop mapping plays a key role. In countries such as China, crop fields are typically small, in assorted shapes and with various management approaches, which deem traditional methods of crop identification ineffective, and accuracy is highly dependent on image resolution and acquisition time. The five-day temporal and 100 m spatial resolution PROBA-V data make it possible to automatically identify crops using time series phenological information. This paper takes advantage of the improved spatial and temporal resolution of the PROBA-V data, to map crops at the Yucheng site in Shandong Province and the Hongxing farm in Heilongjiang province of China. First, the Swets filter algorithm was employed to eliminate noisy pixels and fill in data gaps on time series data during the growing season. Then, the crops are classified based on the Iterative Self-Organizing Data Analysis Technique (ISODATA) clustering, the maximum likelihood method (MLC) and similarity analysis. The mapping results were validated using field-collected crop type polygons and high resolution crop maps based on GaoFen-1 satellite (GF-1) data in 16 m resolution. Our study showed that, for the Yucheng site, the cropping system is simple, mainly dominated by winter wheat-maize rotation. The overall accuracy of crop identification was 73.39% which was slightly better than the result derived from MODIS data. For the Hongxing farm, the cropping system is more complex (i.e., more than three types of crops were planted). The overall accuracy of the crop mapping by PROBA-V was 73.29% which was significantly higher than the MODIS product (46.81%). This study demonstrates that time series PROBA-V data can serve as a useful source for reliable crop identification and area estimation. The high revisiting frequency and global coverage of the PROBA-V data show good potential for future global crop mapping and agricultural monitoring. [ABSTRACT FROM AUTHOR]

How to accurately determine the mechanical parameters of rockfill is one of the key issues of concrete-face rockfill dams. Parameter back-analysis using internal or external monitoring data has been proven to be an efficient way to solve this problem. However, traditional internal or external monitoring methods have limitations in efficiency and long-term monitoring. In this paper, the displacement of the Shuibuya concrete-face rockfill dam is monitored by the space-borne Interferometric Synthetic Aperture Radar (InSAR) time series method. Using the InSAR results and the finite element method, the back-analysis of the mechanical parameters of the rockfill dam is investigated, and the back-analysis results of InSAR and levelling are compared. A high correlation of 0.99 for the displacement results generated from InSAR and the levelling offers good agreement between the two methods. The agreement provides confidence that the external InSAR monitoring measurement allows producing a reliable back-analysis and captures the displacement properties of the dam. Based on the identified parameters from the InSAR results, the dam displacement is predicted. The prediction of the maximum settlement of the dam is 2.332 m by the end of 2020, according to the dam displacement characteristics, which agrees well with the results derived from the recorded internal monitoring data. Therefore, the external monitoring results from the InSAR observation can be used as a supplement for traditional monitoring methods to analyse the parameters of the dam. [ABSTRACT FROM AUTHOR]

The ancient plank road is a creative building in the history of Chinese ancient traffic through cliffs. In this paper, a virtual restoration approach for ancient plank road using mechanical analysis with precision 3D data of current heritage site is proposed. Firstly, an aero photogrammetry with multiple view images from Unmanned Aerial Vehicle (UAV) imaging system is presented to obtain the 3D point cloud of ancient plank roads, which adopts a density image matching and aerial triangulation processing. In addition, a terrestrial laser scanner is integrated to obtain detail 3D data of the plank road. Secondly, a mechanical analysis method based on the precision 3D data of the current plank roads is proposed to determine their forms and restore each of their components with detail sizes. Finally, all components and background scene were added to the existing model to obtain a virtual restoration model, which indicates that it is effective and feasible to achieve a three-dimensional digital and virtual restoration of ancient sites. The Chiya Plank Road is taken as a virtual restoration example with the proposed approach. The restored 3D model of the ancient plank can be widely used for digital management, research, and visualization of ancient plank roads. [ABSTRACT FROM AUTHOR]

The coordinated development of the Beijing-Tianjin-Hebei has become a national strategy with Beijing and Tianjin as twin engines driving the regional development. However, the Beijing-Tianjin-Hebei region has suffered dramatic ground subsidence during last two to three decades, mainly due to long-term groundwater withdrawal. Although, annual spirit leveling has been conducted routinely in some parts of Beijing and Tianjin, and InSAR technique has also been used to monitor ground subsidence in some local areas of the region, there is a lack of a complete survey of ground subsidence over the whole region. In this paper, we report a research on mapping ground subsidence in the Beijing-Tianjin-Hebei region over a long time span from 1992 to 2014. Three SAR datasets from four satellites are used: ERS-1/2 SAR images from 1992 to 2000, ENVISAT ASAR images from 2003 to 2010, and RADARSAT-2 images from 2012 to 2014. An improved multi-temporal InSAR method, namely "Multiple-master Coherent Target Small-Baseline InSAR" (MCTSB-InSAR), has been developed to process the datasets. A unique feature of MCTSB-InSAR is the adjustment process useful for wide area monitoring which provides an integrated solution for both calibration of InSAR-derived deformation and the harmonization of the deformation estimates from overlapping SAR frames. Three maps of the subsidence rate corresponding to the three periods over the wide Beijing-Tianjin-Hebei region are generated, with respective accuracy of 8.7 mm/year (1992-2000), 4.7 mm/year (2003-2010), and 5.4 mm/year (2012-2014) validated by more than 120 leveling measurements. The spatial-temporal characteristics of the development of ground subsidence in Beijing and Tianjin are analyzed. This research represents a first-ever effort on mapping ground subsidence over very large area and over long time span in China. The result is of significance to serve the decision-making on ground subsidence mitigation in the Beijing-Tianjin-Hebei region. [ABSTRACT FROM AUTHOR]

Wetlands are important ecosystems as they are known as the "kidney of the earth". Particularly, urban wetlands play an important role in providing both natural and social beneficial services. However, urban wetlands are suffering from various human impacts, such as excessive land use conversion, air and water pollution, especially those in developing countries undergoing rapid industrialization and urbanization. Therefore, it is of great necessity to derive timely biomass information for optimal design, management and protection of urban wetlands. In this paper, we develop a set of models for estimating above ground biomass (AGB) in Xixi National Wetland Park in Hangzhou, China by using optical images and Synthetic Aperture Radar (SAR) images. A series of vegetation indices (VIs) derived from optical data is introduced along with spectral data. The modeling methods consist of (1) curve estimation; (2) linear regression for multivariable model; (3) Back Propagation Neural Network (BPNN) modeling. Curve estimation is a combination of linear and nonlinear regressions. It is applied to generate AGB models from a single variable including Normalized Difference Vegetation Index (NDVI) and radar backscatter coefficient. The models are then compared via three accuracy metrics. According to the results, SAR models generally show better accuracy than optical models and BPNN models show the greatest accuracy among all the models. The BPNN model from the combination of Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and European Remote-Sensing Satellite-2 (ERS-2) SAR (Synthetic Aperture Radar) image has the least root mean square (RMSE, 0.396 kg/m2), least mean absolute error (MAE, 0.256 kg/m2) and the greatest correlation coefficient (0.974). The results indicate that AGB can be estimated by integrating optical and SAR imagery. Four maps of AGB are derived to illustrate the distribution of AGB in the study area. The total AGB in the study area is estimated to be between 165,000 and 210,000 kg/m2. [ABSTRACT FROM AUTHOR]

Grasslands are one of the most important terrestrial ecosystems on the planet and have significant economic and ecological value. Accurate and rapid discrimination of grassland communities is critical to the conservation and utilization of grassland resources. Previous studies that explored grassland communities were mainly based on field surveys or airborne hyperspectral and high-resolution imagery. Limited by workload and cost, these methods are typically suitable for small areas. Spaceborne mid-resolution RS images (e.g., Sentinel, Landsat) have been widely used for large-scale vegetation observations owing to their large swath width. However, there still keep challenges in accurately distinguishing between different grassland communities using these images because of the strong spectral similarity of different communities and the suboptimal performance of models used for classification. To address this issue, this paper proposed a superpixel-based grassland community classification method using Genetic Programming (GP)-optimized classification model with Sentinel-2 multispectral bands, their derived vegetation indices (VIs) and textural features, and Sentinel-1 Synthetic Aperture Radar (SAR) bands and the derived textural features. The proposed method was evaluated in the Siziwang grassland of China. Our results showed that the addition of VIs and textures, as well as the use of GP-optimized classification models, can significantly contribute to distinguishing grassland communities, and the proposed approach classified the seven communities in Siziwang grassland with an overall accuracy of 84.21% and a kappa coefficient of 0.81. We concluded that the classification method proposed in this paper is capable of distinguishing grassland communities with high accuracy at a regional scale. [ABSTRACT FROM AUTHOR]

As one of the most devastating disasters to pine forests, pine wilt disease (PWD) has caused tremendous ecological and economic losses in China. An effective way to prevent large-scale PWD outbreaks is to detect and remove the damaged pine trees at the early stage of PWD infection. However, early infected pine trees do not show obvious changes in morphology or color in the visible wavelength range, making early detection of PWD tricky. Unmanned aerial vehicle (UAV)-based hyperspectral imagery (HI) has great potential for early detection of PWD. However, the commonly used methods, such as the two-dimensional convolutional neural network (2D-CNN), fail to simultaneously extract and fully utilize the spatial and spectral information, whereas the three-dimensional convolutional neural network (3D-CNN) is able to collect this information from raw hyperspectral data. In this paper, we applied the residual block to 3D-CNN and constructed a 3D-Res CNN model, the performance of which was then compared with that of 3D-CNN, 2D-CNN, and 2D-Res CNN in identifying PWD-infected pine trees from the hyperspectral images. The 3D-Res CNN model outperformed the other models, achieving an overall accuracy (OA) of 88.11% and an accuracy of 72.86% for detecting early infected pine trees (EIPs). Using only 20% of the training samples, the OA and EIP accuracy of 3D-Res CNN can still achieve 81.06% and 51.97%, which is superior to the state-of-the-art method in the early detection of PWD based on hyperspectral images. Collectively, 3D-Res CNN was more accurate and effective in early detection of PWD. In conclusion, 3D-Res CNN is proposed for early detection of PWD in this paper, making the prediction and control of PWD more accurate and effective. This model can also be applied to detect pine trees damaged by other diseases or insect pests in the forest. [ABSTRACT FROM AUTHOR]

Sea fog is a disastrous marine phenomenon for ship navigation. Sea fog reduces visibility at sea and has a great impact on the safety of ship navigation, which may lead to catastrophic accidents. Geostationary orbit satellites such as Himawari-8 make it possible to monitor sea fog over large areas of the sea. In this paper, a framework for marine navigation risk evaluation in fog seasons is developed based on Himawari-8 satellite data, which includes: (1) a sea fog identification method for Himawari-8 satellite data based on multilayer perceptron; (2) a navigation risk evaluation model based on the CRITIC objective weighting method, which, along with the sea fog identification method, allows us to obtain historical sea fog data and marine environmental data, such as properties related to wind, waves, ocean currents, and water depth to evaluate navigation risks; and (3) a way to determine shipping routes based on the Delaunay triangulation method to carry out risk analyses of specific navigation areas. This paper uses global information system mapping technology to get navigation risk maps in different seasons in Bohai Sea and its surrounding waters. The proposed sea fog identification method is verified by CALIPSO vertical feature mask data, and the navigation risk evaluation model is verified by historical accident data. The probability of detection is 81.48% for sea fog identification, and the accident matching rate of the navigation risk evaluation model is 80% in fog seasons. [ABSTRACT FROM AUTHOR]

An outbreak of Ulva prolifera poses a massive threat to coastal ecology in the Southern Yellow Sea, China (SYS). It is a necessity to extract its area and monitor its development accurately. At present, Ulva prolifera monitoring by remote sensing imagery is mostly based on a fixed threshold or artificial visual interpretation for threshold selection, which has large errors. In this paper, an adaptive threshold model based on Google Earth Engine (GEE) is proposed and applied to extract U. prolifera in the SYS. The model first applies the Floating Algae Index (FAI) or Normalized Difference Vegetation Index (NDVI) algorithm on the preprocessed remote sensing images and then uses the Canny Edge Filter and Otsu threshold segmentation algorithm to extract the threshold automatically. The model is applied to Landsat8/OLI and Sentinel-2/MSI images, and the confusion matrix and cross-sensor comparison are used to evaluate the accuracy and applicability of the model. The verification results show that the model extraction of U. prolifera based on the FAI algorithm has higher accuracy (R2 = 0.99, RMSE = 5.64) and better robustness. However, when the average cloud cover is more than 70% in the image (based on the statistical results of multi-year cloud cover information), the model based on the NDVI algorithm has better applicability and can extract the algae distributed at the edge of the cloud. When the model uses the FAI algorithm, it is named FAI-COM (model based on FAI, the Canny Edge Filter, and Otsu thresholding). And when the model uses the NDVI algorithm, it is named NDVI-COM (model based on NDVI, the Canny Edge Filter, and Otsu thresholding). Therefore, the final extraction results are generated by supplementing NDVI-COM results on the basis of FAI-COM extraction results in this paper. The F1-score of U. prolifera extracted results is above 0.85. The spatiotemporal distribution of U. prolifera in the South Yellow Sea from 2016 to 2020 is obtained through the model calculation. Overall, the coverage area of U. prolifera shows a decreasing trend over the five years. It is found that the delay in recovery time of Porphyra yezoensis culture facilities in the Northern Jiangsu Shoal and the manual salvage and cleaning-up of U. prolifera in May are among the reasons for the smaller interannual scale of algae in 2017 and 2018. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) Remote sensing (RS) has unique advantages over traditional satellite RS, including convenience, high resolution, affordability and fast acquisition speed, making it widely used in many fields. To provide an overview of the development of UAV RS applications during the past decade, we screened related publications from the Web of Science core database from 2010 to 2021, built co-author networks, a discipline interaction network, a keywords timeline view, a co-citation cluster, and detected burst citations using bibliometrics and social network analysis. Our results show that: (1) The number of UAV RS publications had an increasing trend, with explosive growth in the past five years. The number of papers published by China and the United States (US) is far ahead in this field; (2) The US has currently the greatest influence in this field through the largest number of international cooperations. Cooperation is mainly concentrated in countries and institutions with a large number of publications but is not widely distributed. (3) The application of UAV RS involves multiple interdisciplinary subjects, among which "Environmental Science and Ecology" ranks first; (4) Future research trends of UAV RS are expected to be related to artificial intelligence (e.g., artificial neural networks-based research). This paper provides a scientific basis and guidance for future developments of UAV RS applications, which can help the research community to better grasp the developments of this field. [ABSTRACT FROM AUTHOR]

The probe of China's first Mars exploration mission, Tianwen-1, has been successfully launched. It will carry out scientific exploration on the topography, soil characteristics, water ice, climate, ionosphere, and physical fields of Mars. Different from other rovers landing on the moon and Mars, the Zhurong rover is equipped with a full polarimetric subsurface penetrating radar (FP-SPR) system for the first time. The radar's mission is to depict the shallow subsurface structure of Mars and search for possible water ice. Therefore, in this paper, a 3D realistic structure model is established and numerically simulated based on the possible subsurface structure of Utopia Planitia (the landing area). Influencing factors such as topographical fluctuations, rocks, water ice, and the variation of dielectric constant of different layers are added to the model. The analysis of the acquired FP-SPR data set shows that the two-dimensional principal component analysis (2D-PCA) method can extract effective reflected signals from the radar data with noise interference and improve the data quality. These clearly imaged targets may be water ice blocks, so the application of 2D-PCA to FP-SPR data increases the imaging quality of suspected water ice targets. The results of this paper are the basis for future processing of the measured FP-SPR data on Mars, which will help to identify more details of subsurface structures. [ABSTRACT FROM AUTHOR]

This paper proposed a new remote sensing observation capability evaluation model (RSOCE) based on analytic hierarchy process to quantitatively evaluate the capability of multi-satellite cooperative remote sensing observation. The analytic hierarchical process model is a combination of qualitative and quantitative analysis of systematic decision analysis method. According to the objective of the remote sensing cooperative observation mission, we decompose the complex problem into several levels and a number of factors, compare and calculate various factors in pairs, and obtain the combination weights of different schemes. The model can be used to evaluate the observation capability of resource satellites. Taking the optical remote sensing satellites, such as China's resource satellite series and GF-4, as examples, this paper verifies and evaluates the model for three typical tasks: point target observation, regional target observation, and moving target continuous observation. The results show that the model can provide quantitative reference and model support for comprehensive evaluation of the collaborative observation capability of remote sensing satellites. [ABSTRACT FROM AUTHOR]

Earth Observation (EO) data is a critical information source for mapping and monitoring water resources over large inaccessible regions where hydrological in-situ networks are sparse. In this paper, we present a simple yet robust method for fusing optical and Synthetic Aperture Radar (SAR) data for mapping surface water dynamics over mainland China. This method uses a multivariate logistic regression model to estimate monthly surface water extent over a four-year period (2017 to 2020) from the combined usages of Sentinel-1, Sentinel-2 and Landsat-8 imagery. Multi-seasonal high-resolution images from the Chinese Gaofen satellites are used as a reference for an independent validation showing a high degree of agreement (overall accuracy 94%) across a diversity of climatic and physiographic regions demonstrating potential scalability beyond China. Through inter-comparison with similar global scale products, this paper further shows how this new mapping technique provides improved spatio-temporal characterization of inland water bodies, and for better capturing smaller water bodies (< 0.81 ha in size). The relevance of the results is discussed, and we find this new enhanced monitoring approach has the potential to advance the use of Earth observation for water resource management, planning and reporting. [ABSTRACT FROM AUTHOR]

Although the mapping of spatiotemporal patterns of urban expansion has been widely studied, relatively little attention has been paid to detailed comparative studies on spatiotemporal patterns of urban growth at the regional level over a relatively longer timeframe. This paper was based on multi-sensor remote sensing image data and employs several landscape metrics and the centroid shift model to conduct a multi-angle quantitative analysis on urban expansion in Beijing, Tianjin and Tangshan (Jing-Jin-Tang) in the period from 1970-2013. In addition, the impact analysis of urban growth on land use was adopted in this research. The results showed that Beijing, Tianjin and Tangshan all experienced rapid urbanization, with an average annual urban growth rate of 7.28%, 3.9%, and 0.97%, respectively. Beijing has especially presented a single choropleth map pattern, whereas Tianjin and Tangshan have presented a double surface network pattern in orientation analysis. Furthermore, urban expansion in Beijing was mainly concentrated in Ring 4 to Ring 6 in the northwest and southeast directions, whereas the major expansion was observed in the southeast in Tianjin, primarily affected by dramatic development of Binhai New Area and Tianjin South Railway Station. Naturally, the urban expansion in Tangshan was significantly influenced by the expansion of Beijing and was primarily southwestward. The hot-zones of urbanization were observed within the ranges of 7-25 km, 6-18 km, and 0-15 km, accounting for 93.49%, 89.44% and 72.44% of the total expansion area in Beijing, Tianjin and Tangshan, respectively. The majority of the newly developed urban land was converted from cultivated land and integrated from other built-up land over the past four decades. Of all new urban land in the Beijing, Tianjin and Tangshan, more than 50% was converted from cultivated land, and there was a general tendency for smaller cities to have higher percentages of converted land, accounting for 50.84%, 51.19%, and 51.58%, respectively. The study revealed significant details of the temporal and spatial distributions of urban expansion in Beijing, Tianjin and Tangshan and provided scientific support for the collaborative development of the Beijing, Tianjin and Hebei (Jing-Jin-Ji) regions. [ABSTRACT FROM AUTHOR]

GPS has become a very effective tool to remotely sense precipitable water vapor (PWV) information, which is important for weather forecasting and nowcasting. The number of geodetic GNSS stations set up in China has substantially increased over the last few decades. However, GPS PWV derivation requires surface pressure to calculate the precise zenith hydrostatic delay and weighted mean temperature to map the zenith wet delay to precipitable water vapor. GPS stations without collocated meteorological sensors can retrieve water vapor using standard atmosphere parameters, which lead to a decrease in accuracy. In this paper, a method of interpolating NWP reanalysis data to site locations for generating corresponding meteorological elements is explored over China. The NCEP FNL dataset provided by the NCEP (National Centers for Environmental Prediction) and over 600 observed stations from different sources was selected to assess the quality of the results. A one-year experiment was performed in our study. The types of stations selected include meteorological sites, GPS stations, radio sounding stations, and a sun photometer station. Compared with real surface measurements, the accuracy of the interpolated surface pressure and air temperature both meet the requirements of GPS PWV derivation in most areas; however, the interpolated surface air temperature exhibits lower precision than the interpolated surface pressure. At more than 96% of selected stations, PWV differences caused by the differences between the interpolation results and real measurements were less than 1.0 mm. Our study also indicates that relief amplitude exerts great influence on the accuracy of the interpolation approach. Unsatisfactory interpolation results always occurred in areas of strong relief. GPS PWV data generated from interpolated meteorological parameters are consistent with other PWV products (radio soundings, the NWP reanalysis dataset, and sun photometer PWV data). The differences between them were approximately 1~3 mm at most at our selected stations, and GPS data processing is the main factor influencing the agreement of the GPS PWV results with those of other methods. [ABSTRACT FROM AUTHOR]

Supervised classification is the commonly used method for extracting ground information from images. However, for supervised classification, the selection and labelling of training samples is an expensive and time-consuming task. Recently, automatic information indexes have achieved satisfactory results for indicating different land-cover classes, which makes it possible to develop an automatic method for labelling the training samples instead of manual interpretation. In this paper, we propose a method for the automatic selection and labelling of training samples for high-resolution image classification. In this way, the initial candidate training samples can be provided by the information indexes and open-source geographical information system (GIS) data, referring to the representative land-cover classes: buildings, roads, soil, water, shadow, and vegetation. Several operations are then applied to refine the initial samples, including removing overlaps, removing borders, and semantic constraints. The proposed sampling method is evaluated on a series of high-resolution remote sensing images over urban areas, and is compared to classification with manually labeled training samples. It is found that the proposed method is able to provide and label a large number of reliable samples, and can achieve satisfactory results for different classifiers. In addition, our experiments show that active learning can further enhance the classification performance, as active learning is used to choose the most informative samples from the automatically labeled samples. [ABSTRACT FROM AUTHOR]

Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. This paper focuses on the sampling methods used to validate the coarse-resolution fractional vegetation cover (FVC) product in the Heihe River Basin, where the patterns of spatial variations in and between land cover types vary significantly in the different growth stages of vegetation. A sampling method, called the mean of surface with non-homogeneity (MSN) method, and three other sampling methods are examined with real-world data obtained in 2012. A series of 15-m-resolution fractional vegetation cover reference maps were generated using the regressions of field-measured and satellite data. The sampling methods were tested using the 15-m-resolution normalized difference vegetation index (NDVI) and land cover maps over a complete period of vegetation growth. Two scenes were selected to represent the situations in which sampling locations were sparsely and densely distributed. The results show that the FVCs estimated using the MSN method have errors of approximately less than 0.03 in the two selected scenes. The validation accuracy of the sampling methods varies with variations in the stratified non-homogeneity in the different growing stages of the vegetation. The MSN method, which considers both heterogeneity and autocorrelations between strata, is recommended for use in the determination of samplings prior to the design of an experimental campaign. In addition, the slight scaling bias caused by the non-linear relationship between NDVI and FVC samples is discussed. The positive or negative trend of the biases predicted using a Taylor expansion is found to be consistent with that of the real biases. [ABSTRACT FROM AUTHOR]