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Spatiotemporal data fusion is a methodology to generate images with both high spatial and temporal resolution. Most spatiotemporal data fusion methods generate the fused image at a prediction date based on pairs of input images from other dates. The performance of spatiotemporal data fusion is greatly affected by the selection of the input image pair. There are two criteria for selecting the input image pair: the “similarity” criterion, in which the image at the base date should be as similar as possible to that at the prediction date, and the “consistency” criterion, in which the coarse and fine images at the base date should be consistent in terms of their radiometric characteristics and imaging geometry. Unfortunately, the “consistency” criterion has not been quantitatively considered by previous selection strategies. We thus develop a novel method (called “cross-fusion”) to address the issue of the determination of the base image pair. The new method first chooses several candidate input image pairs according to the “similarity” criterion and then takes the “consistency” criterion into account by employing all of the candidate input image pairs to implement spatiotemporal data fusion between them. We applied the new method to MODIS–Landsat Normalized Difference Vegetation Index (NDVI) data fusion. The results show that the cross-fusion method performs better than four other selection strategies, with lower average absolute difference (AAD) values and higher correlation coefficients in various vegetated regions including a deciduous forest in Northeast China, an evergreen forest in South China, cropland in North China Plain, and grassland in the Tibetan Plateau. We simulated scenarios for the inconsistency between MODIS and Landsat data and found that the simulated inconsistency is successfully quantified by the new method. In addition, the cross-fusion method is less affected by cloud omission errors. The fused NDVI time-series data generated by the new method tracked various vegetation growth trajectories better than previous selection strategies. We expect that the cross-fusion method can advance practical applications of spatiotemporal data fusion technology. [ABSTRACT FROM AUTHOR]

Atmospheric disturbance, sensor malfunctions, and other factors can cause serious gap pixels in the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference snow index (NDSI) products. In this article, MODIS NDSI gap pixels are reconstructed in a highly heterogeneous area with drastic snow accumulation and melting changes using a long short-term memory (LSTM) network. Three LSTM-based MODIS NDSI gap pixel reconstruction schemes, i.e., forward, backward, and bidirectional LSTM networks that separately use earlier, subsequent, and integrated earlier and subsequent timestamp information, are developed. NDSI information for the gap pixel is restored using the long-term spatiotemporal information for this pixel and its adjacent pixels. A case study of NDSI reconstruction in the source area of the Yellow River, northwestern China, during the 2018–2019 snow season, demonstrates that all three LSTM-based schemes can reliably generate spatiotemporally continuous NDSI data with an accuracy comparable to that of the original MODIS NDSI products under clear-sky conditions. The bidirectional LSTM-based scheme, which has the best performance, can achieve a desirable overall accuracy of 89.93%, with an omission error of 3.82% and a commission error of 6.25%, in terms of dichotomous evaluation based on in situ snow depth observations. The $R^{2}$ , average RMSE, overestimation error, and underestimation error are 0.95, 5.13%, 5.39, and 6.40%, respectively, in terms of the continuous value assessment based on the gap pixels assumption. Our results demonstrate the reliability and feasibility of the LSTM-based schemes in recovering the missing values in MODIS NDSI products by deeply excavating the spatial continuity and long-time series dependence of the snow cover. [ABSTRACT FROM AUTHOR]

Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (SNPP-VIIRS) data are valuable records of nighttime lights (NTLs) in analyzing socioeconomic development. However, inconsistencies between these data have severely restricted long time-series analyses. Published time-series NTL data sets are not widely available or accurate because the DMSP-OLS calibration is inadequate and some missing data in the SNPP-VIIRS data are seldom considered for patching. To address these issues, we calibrated DMSP-OLS data (1992–2013) by using a quadratic model based on a “pseudo-invariant pixel” method. Thereafter, an exponential smoothing model was used to predict and patch missing data in the monthly SNPP-VIIRS data (2013–2019). Outliers and noise were also removed from the annual data. In addition, a sigmoid model was employed to generate improved simulated DMSP-OLS (SDMSP-OLS) data (2013–2019), which were appended with the calibrated DMSP-OLS data (1992–2013) to develop improved DMSP-OLS-like data (1992–2019) in China. Finally, we qualitatively and quantitatively compared these data with published NTL data to examine data availability. Results showed that choosing invariant pixels to calibrate DMSP-OLS data can minimize discontinuity. The correlation between the SNPP-VIIRS data synthesized by the patched monthly SNPP-VIIRS data and the official annual SNPP-VIIRS data in 2015 ($R^{2} =0.931$) and 2016 ($R^{2} =0.930$) was higher than those of the two existing correction methods with $R^{2}$ values below 0.90. Spatial patterns of pixels in the improved SDMSP-OLS data in 2013 were more similar with the DMSP-OLS data than those in the published data. Strong correlations likewise existed between the total (average) pixel values of the improved SDMSP-OLS data (2013–2019) and the DMSP-OLS data in 2012. We also found that the improved DMSP-OLS-like data held strong linear correlations with different statistics, the average $R^{2}$ values of which were 0.931 and 0.654 at the national and provincial levels, respectively. Meanwhile, the average regression $R^{2}$ values between the two published data sets and statistics were 0.858/0.506 and 0.911/0.611, respectively. Our study has proven that the improved DMSP-OLS-like data (1992–2019) have immense potential to effectively evaluate socioeconomic development and anthropic activities. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) products from the second generation of China’s geostationary meteorological satellite Fengyun-4A (FY-4A) have the advantage of high spatiotemporal resolution and can play an increasingly important role in the study of atmosphere and climate. Using the radiosonde, the global navigation satellite system (GNSS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis 5 (ERA5) reanalysis data, this study presented a comprehensive evaluation of PWV products from FY-4A for a one-year period from January 2019 to January 2020. Results indicated that FY-4A PWV data have a good agreement with radiosonde and GNSS measured ones with the same correlation coefficient of 0.976, and the root mean square errors (RMSEs) are 3.95 and 3.73 mm, respectively. Compared with the radiosonde and GNSS, the FY-4A Advanced Geostationary Radiation Imager (AGRI) was found to underestimate the water vapor during humid conditions when the PWV is greater than 50 mm. The magnitude of underestimation increases with the growth in water vapor content. In terms of spatial variability, the RMSE of FY-4A PWV decreases with the increase in latitude, while the relative RMSE (R-RMSE) displays an opposite pattern. RMSE from the comparison between FY-4A and ERA5 PWV varies from 0 to 6 mm depending upon the location. Statistics showed that 55.08%, 59.79%, and 83.13% RMSE values are less than 4 mm in the evaluation by radiosonde, GNSS, and ERA5, respectively. The seasonal and diurnal variations of RMSE showed that: 1) summer exhibited larger RMSE than winter and 2) daytime obtained slightly worse performance than nighttime. [ABSTRACT FROM AUTHOR]

A comparative experiment was carried out to discuss the detectability and influence factors on heterogeneous distributions of the stress field for underground panels in North China. The panels are situated in the center and the eastern margin of the North China plate, having similar layouts and stress states. Seismic surveys were performed before coal extractions, and travel-time topographies for refracted P-waves from stiff roofs were computed with a ray-based inversion. The tomographic distributions of P-wave velocity were interpreted by a K-means clustering to categorize the stress fields. The estimated high-stress anomalies show strong correlations with the monitored dynamic roof ruptures and high-energy microseisms. Besides, the relationship among high-stress areas, geological factors, and mined-out voids is also discussed through feature comparisons, Monte–Carlo simulations, and point process statistics. Our results show that the curvature variations and the mined-out voids are dominant factors that affect the heterogeneous distribution of the stress field in the panels. However, the leading causes of the curvature variations in the small-scale panels are different, but they do follow the large-scale regional patterns of the geological regime. [ABSTRACT FROM AUTHOR]

The Chinese GF-1 satellite, the first satellite of the China High-resolution Earth Observation System launched in 2013, can be used to help estimate evapotranspiration (LE), which is important for myriad hydroclimatic and ecosystem science and applications. We propose a novel approach to use the GF-1 visible and near-infrared (VNIR) measurements at 16 m and 4-day resolutions to estimate LE. The NIR (near-infrared)–red spectral-domain (NRSD) model is coupled to a perpendicular soil moisture index (PSI) and a perpendicular vegetation index (PVI). We applied the model to the Huailai agricultural region of China with 55 scenes of GF-1 imagery during 2013–2017 and validated using ground measurements with footprint models for two eddy-covariance (EC) flux tower sites and one large aperture scintillometer (LAS) site. The results illustrate that the terrestrial daily LE can be estimated with squared correlation coefficients (R2) of 0.77–0.84 (p < 0.01) and root-mean-square error (RMSE) values of 17.9–21.5 W/m2 among all three sites. The site-calibrated statistics are improved by 0.14–0.25 for R2 and decreased by 4.2–8.3 W/m2 for RMSE as compared to the commonly used universal PT-JPL model. A satisfactory performance is achieved across all experimental conditions, encouraging the application of the NRSD model to estimate LE for other broad regions. [ABSTRACT FROM AUTHOR]

Accurate land use/land cover (LULC) mapping over a large area is essential to environmentally sustainable development. Recently, the Chinese government established a new national economic zone called the Xiongan New Area, and along with the upcoming large-scale urban construction, this area will inevitably experience a dramatic LULC change, which will threaten the local ecological balance. In this article, we proposed a two-stage approach for LULC mapping in the Xiongan New Area ahead of the forthcoming dense urban construction. The first stage is to obtain base-class maps through a supervised imagery classification. Specifically, we designed a new object-based framework consisting of automatic image segmentation, pixel-based probabilistic estimation, and area-weighted probability statistics for Sentinel-2 multiresolution imagery classification. The second stage is an LULC map refinement process in which the temporal features of each land use category are extracted to refine the LULC classification. Through the implementation of the proposed two-stage approach, an LULC map containing permanent water, temporal water, natural vegetation, barren land, built-up land, and cropland categories can be produced. Through an accuracy assessment, the proposed multiresolution imagery classification method achieved the highest overall accuracy of 88.58% and an average accuracy (AA) of 87.78% compared with conventional classification methods. After obtaining the refined LULC map, we find that the current Xiongan New Area is in a less developed state, that is, cropland accounts for the highest proportion of 51.59%, which is followed by natural vegetation (22.38%) and built-up land (15.69%). [ABSTRACT FROM AUTHOR]

Except for its known aspects of positioning, navigation, and timing (PNT), the Global Navigation Satellite System (GNSS) has extended its application to the rainfall forecasting. GNSS-derived zenith total delay (ZTD) or precipitable water vapor (PWV) has been used as a single factor to predict the occurrence of rainfall; however, the rainfall is highly correlated with myriad atmospheric parameters, which cannot be perfectly reflected by a single predictor. In this article, an improved rainfall forecasting model (IRFM) is proposed to forecast the rainfall. The IRFM considers five predictors: monthly PWV value, seasonal PWV/ZTD variations, and their first derivatives: it can forecast rainfall using a single predicator or an arbitrary combination of those predicators. The merit of IRFM is reducing the false forecasted rainfall (FFR) events and missed detected rainfall (MDR) events as much as possible while guaranteeing the true detected rainfall (TDR) events. An optimized selecting principle of predictors’ threshold has been determined using the percentile method. The test experiment has been performed using five GNSS stations derived from continuously operating reference system (CORS) network of Zhejiang province, China. The analysis reveals that the IRFM considering five predictors provides a better performance than that only using a single predictor or a combination of arbitrary predictors. The statistical result shows the average TDR value of more than 95%, FFR value of less than 30%, and MDR of less than 5%, respectively. Compared to the existing rainfall forecasting methods using ZTD or PWV, the IRFM reduces the FFR and MDR, respectively, with the lowest values, while the TDR value is the highest. [ABSTRACT FROM AUTHOR]

Semantic segmentation of the remote sensing images (RSIs) has attracted increasing interest in recent years. However, large-area segmentation of the woodland presents challenges. The wide distribution and diverse tree species of the woodland make feature extraction difficult. For this reason, an infrared attention network (InfAttNet) is proposed to extract woodland from multispectral RSIs. InfAttNet has an extra infrared spectral encoder which makes use of the sensitivity of vegetation to near-infrared and red edge spectrums. This extra encoder applies learning about vegetation to improve woodland segmentation. Several attention blocks are designed to enhance learning about vegetation features and so improve the performance. In addition, a new dataset is built, containing a large number of woodland RSIs and covering several typical woodland distribution regions in China. The experimental results demonstrate that compared with other networks, InfAttNet has the highest accuracy and is capable of rapid extraction of the woodland in RSIs. [ABSTRACT FROM AUTHOR]

As the successors of the Huanjing Jianzai-1 (HJ-1) series satellites in the Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two Huanjing Jianzai-2 (HJ-2) A/B satellites have been successfully launched on September 27, 2020. The polarized scanning atmospheric corrector (PSAC) sensors, onboard the HJ-2 A/B satellites, are served as the synchronously atmospheric correction instrument requiring a high-speed and accurate aerosol optical depth (AOD) algorithm. For this purpose, we proposed a neural network-based AOD retrieval model (named the AODNet) that takes full advantage of the multispectral measurements of PSAC for AOD retrieval at a high speed. The training of AODNet is conducted by the simulated observation data (currently applicable for the China region) from the forward calculation using the radiative transfer model. In this way, the land surface reflectance (LSR) is no need for our well-trained model. It is expected to be one of the effective ways to solve the ill-pose problem in the decoupling of the atmosphere and surface information in AOD retrieval. Either the Sun-sky radiometer Observation NETwork (SONET) AOD or the AErosol RObotic NETwork (AERONET) AOD was used to validate the AODNet AOD. The correlation coefficient is higher than 0.85, and more than 60% of the AODNet AOD can fall into the expected error envelope of ±(0.05+20%). The cross-comparison shows that the AODNet has better accuracy than MODIS dark target (DT) and deep blue (DB) algorithm. The air pollution episode is well characterized by the AODNet AOD using PSAC data. [ABSTRACT FROM AUTHOR]

China is the largest coal consumer in the world. The massive exploitation and utilization of coal resources have resulted in serious problems of heavy metal pollution and environmental contamination, such as soil degradation, water pollution, crop damage, and even threatening human lives. Therefore, monitoring soil heavy metal pollution quickly and in real time is an urgent task at present. This research not only formulated a new preprocessing method enlightened by few-shot learning for soil hyperspectral data but also combined it with other soil-related auxiliary information to extract effective information from the soil hyperspectrum, at the end of which different regression methods were adopted to predict soil heavy metal contamination. This test used 168 actual soil samples from the Eastern Junggar coalfield in Xinjiang for verification. Since copper in the soil is a trace element and the corresponding spectral characteristics are affected by other impurities, improper use of hyperspectral preprocessing methods may introduce interference information or may delete useful information, which makes the model effect unsatisfied. To effectively address the above-mentioned problems, the preprocessing method of this experiment combined the second-order differential derivation, and the data enhancement (DA) method together with the addition of auxiliary information to allow more effective features to be entered into the model. Next, the attentive interpretable tabular learning (TabNet) model was improved in three different ways using the original TabNet model and three improved TabNet models to create regression models. One of the improved TabNet models had the best effect, with a list of the top 30 features according to the degree of importance. Meanwhile, the regression prediction of Cu content using four different convolutional neural networks (CNNs) revealed that the model with the residual block was the strongest and slightly outperformed the improved TabNet model, but lacked interpretation of the input data. Besides, this experiment also employed different preprocessing methods for regression prediction on various models and found that the traditional preprocessing methods performed best in traditional regression models [e.g., partial least square regression (PLSR)] and underperformed in deep learning models. The selected optimal model was compared with PLSR and CNN models. The results indicated that both the improved TabNet model and the improved CNN model had better performance using the new preprocessing approach proposed in this article, with improved TabNet yielding a coefficient of determination ($\text{R}^{2}$), root-mean-square error (RMSE), and the ratio of performance to interquartile range (RPIQ) of 0.94, 1.341, and 4.474, respectively. The improved CNN model had a coefficient of determination of 0.942, an RMSE of 1.324, and an interquartile range of 4.531 in the test dataset. [ABSTRACT FROM AUTHOR]

Gravity measurement is an important geophysical prospecting method for mineral exploration. With most of the shallow ore deposits in China being exploited, the future targets for exploration will be aimed at the deep mineral resources about 4-km underground. To improve the resolving ability in the vertical direction of the 3-D density inversion to find deep-source minerals, we propose to achieve the inversion of gravity anomalies by combining power-spectrum derived adaptive weighting and cross-gradient regularization. First, spectral analysis is utilized to conduct the source-depth separation, and the adaptive weighting function is designed depending on the slope of the radial logarithmic power spectrum of the gravity anomaly, which can enhance the correspondence for the field sources with different depths to improve the vertical resolution relatively. Thus, each separated source with different depths could be given an adaptive weighting coefficient in each separated inversion. Second, the cross-gradient technique is introduced as a structural constraint in the objective function to further constrain the separated inversion process. By performing two-cuboid-source cases, it can be seen that this combination calculated approach has great potential to improve the quality of the inversion results. When examining the inversion of the actual gravity data from certain ore district in the west of Zhen’an, South Qinling, we speculate that a potential deep ore body would exist in the deposit via utilizing the recovered density model obtained by the proposed method. [ABSTRACT FROM AUTHOR]

Short-term quantitative precipitation forecasting (SQPF) using weather radar is an important but challenging problem as one must cope with inherent nonlinearity and spatiotemporal correlation in the data. In this article, we propose a novel deep learning model, named Inductive spatiotemporal Graph Convolutional Networks (InstGCNs), to overcome these issues in SQPF. The proposed InstGCN can learn a nonlinear mapping from historical radar reflectivity to future rainfall amounts and extract informative spatiotemporal representations simultaneously. Specifically, we first provide a formal definition for formulating the SQPF problem from a graph perspective. Then, based on radar reflectivity and rain gauge observation, we propose a novel graph construction approach that utilizes a special elliptic structure to model the spatial dependence of precipitation areas. In addition, a new Node level Differential Block (Node-DB) is introduced to tackle the nonstationary temporal dependence. To execute inductive graph learning for unseen nodes, we design to decompose a whole graph into subgraphs. We conduct extensive experiments on three real-world datasets in East China and a public weather radar dataset in the southeastern parts of France. The experimental results confirm the advantages of InstGCN compared with several state of the arts. [ABSTRACT FROM AUTHOR]

Due to the continuous intensification of human activities in the ocean, the frequent outbreaks of red tide have caused great harm to the marine environment and ecology. Thus, the rapid detection and monitoring of red tide become particularly important. At present, the main monitoring methods depend on artificial and buoy data, as well as optical satellite remote sensing. However, these methods may not be able to effectively deal with the characteristics of red tide bloom, such as suddenness and unpredictability. The global navigation satellite system-reflectometry (GNSS-R) is an emerging technology that makes use of navigation signals as a remote sensing opportunity to obtain Earth surface information. GNSS-R has already been proved to be capable of retrieving sea surface parameters (e.g., dielectric constant and sea surface roughness) closely related to the outbreak of a red tide. In this article, we proposed a new method to estimate red tide density, which utilizes an all-new model associating GNSS-R observations with sea surface red tide density. This method can remove the weather influence and greatly decrease the revisit period, which is much longer for optical red tide remote sensing methods. The Landsat-8 near-infrared data and TechDemoSat-1 (TDS-1) GNSS-R data of a red tide outbreak in the sea off the Tsingtao coast in China are used to build and test the proposed method. The results demonstrate that the correlation coefficient is 0.73, and the root mean square error of retrieved red tide density is 2.84%, which shows that the GNSS-R technology shows great potential to perform the rapid and preliminary red tide monitoring and judgment. [ABSTRACT FROM AUTHOR]

Engineering geophysical prospecting is used to identify anomalous underground bodies, which are typically identified by inversion results. In recent years, machine learning has been applied to many geophysics studies, including geophysical data processing and fault prediction. Machine learning methods can also be used to identify complex anomalous bodies as direct identification can be difficult due to the complexity of local anomalous bodies. We propose a U-Net network to extract anomalous bodies from inverse results. Our model uses the regional similarity of the stratigraphic structure around the anomalous bodies, and then, the neural network extracts the regional background from the geophysical inversion results. Finally, the local anomalous bodies can be subtracted from the predicted regional results and the original inversion results. We designated a large number of datasets for training and designed triangular models for testing. Resistivity data for contaminated soil in a crane storage yard in Shanghai, China, were then processed and the resistivity range was calculated. We processed resistivity and polarizability data from Yantong Mountain, Heibei, China, to determine the degree of mineralization in the area. [ABSTRACT FROM AUTHOR]

Hyperspectral/multispectral and panchromatic of optical remote sensing images are commonly used for multisensor image fusion, which has been applied in various applications of Earth observation. However, the utilization of optical remote sensing data suffers from the limitation of bad weather and cloud contamination. To address aforementioned issue and enhance spatial details of polarimetric synthetic aperture radar (PolSAR) image, a novel dual-domain super-resolution image fusion method is proposed by combining improved spherically invariant random vector (ISIRV) model with generalized adaptive linear combination approximation (GALCA) technology in this study. The proposed method decomposes the task of image fusion into polarimetric and texture domain fusion by integrating polarimetric components of PolSAR image and texture detail component of panchromatic image, which can significantly improve spatial resolutions of the PolSAR image while preserving polarimetric information. The data fusion experiment is implemented with three data sets including panchromatic images of Gaofen-1 (GF-1) and Gaofen-2 (GF-2) and the quad-pol SAR data of Gaofen-3 (GF-3) and Radarsat-2. Results show that the proposed dual-domain image fusion method provides a better performance compared with state-of-the-art multisensor fusion methods (BT, PCA, GS, indusion, and PRACS) regarding qualitative and quantitative evaluations. In addition, results of image fusion are applied to image classification over agricultural and urban areas of China, which shows that classification accuracy is significantly improved when compared with the result using only the original image. [ABSTRACT FROM AUTHOR]

Gridded precipitation products with fine resolutions and qualities are of great importance for understanding the global water–carbon-energy cycles at various spatiotemporal scales. Though continuous developments in Satellite Remote Sensing fields have been providing great strengths for measuring the precipitation from space, merging precipitation products from different sources, especially the gauge observations, is still the optimal way for obtaining high-quality precipitation data. Currently, the mainstream merging methods mainly focus on merging the rain rates without the considerations of rain events. In this study, we propose a new assumption that both rain events and rain rates should be considered in the merging procedures rather than only the rain rates. To meet our assumption, a morphology-based adaptive spatio-temporal merging algorithm (MASTMA) for combining various precipitation products is proposed, in which the morphology theory is first introduced to comprehensively consider the influences from both rain events and rain rates. The multisource and multiscale precipitation products including the gauge-based data (CPC-U, 0.5°, daily), the satellite-based data [Global Satellite Mapping of Precipitation by Moving Vector with Kalman (GSMaP-MVK), 0.1°, hourly; integrated multisatellite retrievals for global precipitation measurement late run (IMERG-LR), 0.1°, half-hourly], and the reanalysis data (ERA5-land, 0.1°, hourly), have been comprehensively considered in MASTMA for generating the final estimates (MASTMA-F, 0.1°, hourly) over the southeastern regions of the Mainland China in the periods from 2016 to 2019. The main conclusions include but are not limited to: 1) considerations on rain events contribute significantly to the final merged results, especially when eliminating false extreme values over the regions where precipitation is greatly overestimated; 2) the MASTMA could optimally integrate the advantages from multisource precipitation products with different resolutions, particularly from the perspective of the spatial distributions; and 3) the final merged estimates using MASTMA outperform the contemporary state-of-the-art precipitation products especially in terms of modified Kling–Gupta Efficiency (mKGE) and critical success index (CSI). Additionally, the results of this study suggest that MASMTA is a new promising merging approach with great robustness and applicability, and has the foreseeable potentials for the operational run to generate the optimal global merged precipitation products. [ABSTRACT FROM AUTHOR]

High spatial resolution precipitation data are important for hydrological modeling and meteorological applications, especially at regional scales. Statistical downscaling methods for satellite precipitation products using the normalized difference vegetation index (NDVI) have been carried out in many regions to provide high spatial resolution precipitation. These methods generally use NDVI and precipitation at the same time, assuming that there is a real-time response of vegetation to precipitation. However, this assumption does not hold in many scenarios. It is known that different vegetation types exhibit different response times to precipitation, i.e., there is a possible lag in the response of vegetation to precipitation depending on the vegetation/landcover type. Therefore, it is not appropriate to estimate precipitation using NDVI collected at the same time. To better represent the relationship between precipitation and vegetation, this article develops a new vegetation index based on adaptive lag phase (VIAL) estimated from a new growth rate that is adaptive to landcover type. Based on VIAL, a new local precipitation downscaling method called LPVIAL is proposed, which essentially considers the nonstationary relationship between precipitation and VIAL. The performance of LPVIAL is assessed by downscaling Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) from 0.1° to 1-km spatial resolution over the Pearl River Basin in Southern China from 2010 to 2017 at 16-day temporal resolution, and the downscaled products are validated against ground observations. Results indicate that the high-resolution precipitation data obtained from the new downscaling approach perform well, and the accuracy is higher than traditional approaches. With the enhancement of spatial resolution, LPVIAL downscaled products show more detailed spatial information of precipitation with smooth distribution, and the downscaled products have slightly higher accuracy compared with IMERG. It is, therefore, suggested that the adaptive lag phase should be considered in the satellite precipitation product downscaling process. [ABSTRACT FROM AUTHOR]

Monsoon coastal cities often suffer from extreme rain-induced flooding and severe hazard. However, the associated physical mechanisms and detailed storm structures are poorly understood due to the lack of high-resolution data. This study presents an analysis of a thunderstorm that produces extreme hourly rainfall (EXHR) of 219 mm over the Guangzhou megacity on the southern coast of China using integrated multiplatform observations and a four-dimensional variational Doppler radar analysis system. Results indicate that weak environmental flows and convectively generated weak cold pool facilitate the formation of a quasi-stationary storm, while onshore warm and moist flows in the boundary layer (BL) provide the needed moisture supply. The 219-mm EXHR is attendant by a shallow meso- $\gamma $ -scale vortex due to stretching of intense latent heating-induced convergence, which, in turn, helps organize convective updrafts into its core region. Lightning and dual-polarization radar observations reveal active warm-rain (but weak mixed-phase) microphysical processes, with raindrop size distribution (RSD) closer to marine convection. In contrast, another storm develops about 4 h earlier and only 35 km to the northwest, but with more lightning, higher cloud tops, more graupel and supercooled liquid water content, more continental RSD, little evidence of rotation, and much less rainfall; they are attributable to the presence of larger convective available potential energy resulting from the urban heat island effects and less moisture supply in the BL. These results highlight the importance of using multisource remote sensing data sets in understanding the microphysical and kinematic structures of EXHR-producing storms. [ABSTRACT FROM AUTHOR]

On the basis of the spectral shape of thermal infrared (TIR) emissivity for sandy land, a remote sensing sand index called the sand differential emissivity index (SDEI) is proposed in this article to simply and conveniently detect sandy land over large areas. The SDEI is evaluated on ground, airborne, and spaceborne thermal emissivity data, and it shows good characterization of sandy land and performs better in sandy land identification than two previous indices. The SDEI was also evaluated in the transition zones of China’s four mega-sandy lands and was applied to long-term land surface emissivity to obtain the spatial distribution and variation in China’s sandy land from 2000 to 2016. The findings showed that a mean accuracy of 96% and a mean kappa coefficient of 0.83 were obtained in the transition zones, and the sandy land in the transition zone exhibited a decreasing trend over the past 17 years and a significant decline in the Mu Us sandy land. Meanwhile, the sandy land area in China decreased by 3.6 × 104 km2 (1.53%) by the end of 2016 compared with that in early 2000. [ABSTRACT FROM AUTHOR]

Triple collocation (TC) is a novel method for quantifying the uncertainties of three data sets with mutually independent errors and has been widely used over different geographical fields. Researches in recent years report that TC shows potential in merging multiple data sets from different sources, while the TC-based merging method has not been used over precipitation. Using the TC formulation, this study merges precipitation from the Climate Prediction Center’s morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5). The interim ECMWF Re-Analysis (ERA-Interim) is also involved to act as the substitute of ERA5 in some specific experiments for quality comparison between them. Merged data sets are produced at $0.25^{\circ } \times 0.25^{\circ }$ and daily resolutions from March 2000 to December 2013 over Mainland China, using ground observations from more than 2000 rain gauges as the validation benchmark. First, the effectiveness of the TC-based method for precipitation merging is assessed. Then, two weighting methods using root-mean-square error (RMSE) in logarithmic scale (log-RMSE) and modified scale (mod-RMSE) are compared because previous studies show that mod-RMSE is more suitable for characterizing errors within estimated data. Meanwhile, two merging strategies are designed, that is, merging rainfall and snowfall separately (RS) and merging precipitation directly (P). The results show that 1) all the merged products are superior to any input product which proves that the TC method is effective in precipitation merging; 2) TC-based merging generally has a better performance than dynamic Bayesian model averaging (DBMA)-based merging; 3) mod-RMSE shows worse performance in weight estimation than log-RMSE because mod-RMSE will deteriorate the impact of the underestimated inputs; and 4) RS-based merging is superior to P-based merging, and the superiority is particularly notable in winter. The RS strategy will be very helpful in improving the accuracy of precipitation estimates in cold climate such as over mountainous and high-altitude regions. Finally, the limitations of the TC method and potential solutions are discussed. This study demonstrates the great potential of the TC-based merging method in precipitation and provides insights into its application and development. [ABSTRACT FROM AUTHOR]

Aerosol particles affect the Earth’s radiative balance and represent one of the largest uncertainties in climate research. The removal of clouds is the first step and critical in the aerosol retrievals. However, the cloud detection is still challenging. Here, a novel simplified cloud detection algorithm (SCDA) is proposed to identify the cloud and clear-sky over land and based on a simplified radiative transfer model (RTM). The fewer input bands, dynamic thresholds, and only one parameter to be modified are the main advantages of the algorithm, which can be applied to different satellite sensors. In this article, we apply SCDA to the Himawari-8 data in 2016 for preliminary analysis. The detection results are validated using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical feature mask (VFM) data and the National Centers for Environmental Information (NCEI) ground-based observation data. We also compare the results with the Himawari-8 cloud products from the Japan Aerospace Exploration Agency (JAXA). Compared with CALIPSO VFM data and NCEI ground-based observation data, the correct rate of SCDA cloud detection result is 86.08% and 79.86%, which are higher than that of Himawari-8 cloud products (85.71% and 78.89%). The correct rate of SCDA clear-sky detection result is 88.33% and 87.85%, which are close to the correct rate of Himawari-8 clear-sky products (90.54% and 88.63%). The overall performance of the SCDA is comparable to that of the threshold method for JAXA Himawari-8 cloud products. Therefore, the SCDA can provide accurate cloud mask with only one threshold to be modified and few input parameters. [ABSTRACT FROM AUTHOR]

The lack of in situ data has always posed challenges to remote-sensing-data product validation. Herein, the products of sea-ice concentration (SIC) data derived using the arctic radiation and turbulence interaction study (ARTIST) sea ice (ASI) algorithm were evaluated by comparing them with SICs from a high-resolution sea-ice aerial image obtained during the 27th China Antarctic expedition in January 2011. Results suggest that data obtained from the advanced microwave scanning radiometer for the earth-observing system (AMSR-E) underestimate SICs by 19%. We performed step-by-step comparisons among the aerial image, moderate-resolution-imaging spectroradiometer (MODIS), and AMSR-E SIC. These types of comparisons have not been made in previous validation studies. First, SICs acquired from MODIS-Terra imagery were acquired using a tie-point method and corrected by SICs derived from the aerial photography. Second, SICs of MODIS-Aqua images were trained based on the consistency of SIC results between MODIS-Terra and MODIS-Aqua over the selected region on the same day. Finally, the MODIS-Aqua SICs were employed to validate synchronous AMSR-E swath SIC products. The results show that the AMSR-E products underestimate SICs by 8.5% in the marginal ice zone in comparison with MODIS SICs. According to our further analysis between sea-ice types and AMSR-E biases, the higher the proportion of first-year ice, the smaller the AMSR-E SIC bias. In other words, results suggest that the higher the thin ice proportion, the more the AMSR-E underestimates the SIC. [ABSTRACT FROM AUTHOR]

An inverse mapping table (IMT) method is proposed in this article to retrieve the raindrop size distribution (RSD) parameters from X-band polarimetric weather radar data. In the IMT method, a forward mapping database from three parameters of a gamma-type RSD to polarimetric radar variables is first built based on the scattering simulations under ideal atmospheric conditions, and then an inverse mapping database is derived. In particular, given a fixed shape parameter ($\mu $) of RSD, the intersection of horizontal reflectivity ($Z_{\text {H}}$) and differential reflectivity ($Z_{\text {DR}}$) contour lines is first obtained in the domain of total number concentration ($N_{\text {T}}$) and median volume diameter (MVD) $D_{0}$ ; and the inverse mapping relationship between $Z_{H}$ and $Z_{\text {DR}}$ to $N_{T}$ and $D_{0}$ at a fixed $\mu $ value is derived to form a single layer of IMT. Then, the monotonic relationship between $\mu $ and the specific differential propagation phase shift ($K_{\text {DP}}$) or backscatter differential phase ($\delta _{\text {CO}}$) can aid in determining $\mu $ and a single layer of IMT. Thus, the inverse mapping database from polarimetric observations to the three gamma-type RSD parameters $\mu $ , $N_{\text {T}}$ , and $D_{0}$ can be established. Demonstration studies during a convective rainfall event and a large-scale rainfall event which occurred in northeastern China are carried out to examine the performance of this IMT method compared to a constrained-gamma (C-G) method that uses empirical relations between RSD parameters. The results show that the IMT method has a better performance in the convective case and similar performance in the large-scale continuous rainfall case. [ABSTRACT FROM AUTHOR]

As the direct geo-locating accuracy of spaceborne optical images is limited by the uncertainty of the exterior orientation parameters, precise ground control points (GCPs), which are difficult or expensive to obtain, are commonly required to improve the geometric accuracy in practical applications. In this article, we propose a novel block adjustment (BA) method to make use of the GCPs automatically collected from reference images of coarse resolution (Landsat-8 or Sentinel-2), which are publicly available. Different from the conventional BA methods, the proposed one treats the GCPs of low accuracy as relaxed constraints instead of directly minimizing the error between the geometric models and GCPs, and only guarantees that the GCPs are satisfied by the geometric models with a prior accuracy. In addition, an automated method is introduced to estimate the proper GCP accuracy for the proposed BA. The experimental results of three testing sites in China using three different types of spaceborne images, i.e., Gaofen-1 (GF-1) panchromatic (PAN), ZY-3 nadir (NAD), and SPOT-5 high resolution geometric (HRG) whose spatial resolutions are around 2 m, show that the accuracy of 1–2 pixel can be achieved for these high-resolution images when only coarse reference images (spatial resolution of 15 and 10 m) were used as ground control. The results also show that the inaccurate GCPs are not likely to undermine the geometric consistency of images in the proposed BA, and BA with relaxed constraints can even achieve better tie points (TPs) accuracy than BA without ground control. This article provides a practical way to utilize inaccurate ground control and balance the tradeoff between GCPs and TPs. [ABSTRACT FROM AUTHOR]

Effective utilization of the changing precipitation microphysics in real-time radar quantitative precipitation estimation (QPE) is challenging, which requires dynamic adjustment of the radar reflectivity ($Z$) and rain rate ($R$) relations. This article develops and demonstrates two dynamic radar rainfall approaches using 16 Doppler weather radars and 4579 surface rain gauges deployed over the Eastern Jiang Huai River Basin (EJRB) in China. Both approaches are derived based on the radar-gauge feedback mechanism. Although the $Z$ – $R$ relations in both approaches are dynamically adjusted within a precipitation system, one is using a single global optimum (SGO) $Z$ – $R$ relation, while the other is using different $Z$ – $R$ relations for different storm cells identified by a storm cell identification and tracking (SCIT) algorithm. Four precipitation events featured by different rainfall characteristics are investigated to evaluate the performances of various QPE methodologies. In addition, the short-term vertical profile of reflectivity (VPR) clusters is extensively analyzed to resolve the storm-scale characteristics of different storm cells. The evaluation results based on independent gauge observations show that both rainfall approaches with dynamic $Z$ – $R$ relations perform much better than the fixed $Z$ – $R$ relations. The adaptive approach incorporating the SCIT algorithm and real-time gauge measurements has the best performance since it can better capture the spatial variability and evolution of precipitation. [ABSTRACT FROM AUTHOR]

Traditional remote sensing observation can only derive chlorophyll-a concentration in surface water (Chlasurf), which is insufficient for water quality monitoring or biogeochemical applications. In this paper, a column-integrated biomass (CIB) estimation algorithm was established in Lake Dianchi, China, and the CIB was estimated by Ocean and Land Color Instrument (OLCI) data from 2016 to 2018. First, the results show that the method is applicable to CIB estimation in nonbloom conditions [mean absolute percentage error (MAPE) = 10.55%, mean ratio (MR) = 0.996]. On the one hand, Chlasurf could be estimated using a universal model of Chla (UMOC) estimation for case-II waters; on the other hand, CIB could be obtained from Chlasurf according to the linear relationship between the Chlasurf and CIB. Second, both the spatial and temporal variabilities of Chlasurf are larger than those of CIB, which indicates that the biomass of the whole lake is relatively stable from day to day. During algal bloom periods, the CIB at nonbloom areas tends to decrease, suggesting that algae from nonbloom regions have migrated to bloom regions. Third, wind speed (WS) is the most critical meteorological factor affecting the water surface biomass, compared with other parameters. The average percentage of surface-to-total biomass in nonbloom conditions is about 7% when the WS is less than 2 m/s. [ABSTRACT FROM AUTHOR]

Land surface temperature and emissivity (LST&E) are key variables for a wide variety of surface–atmosphere studies, and it is very important to validate the accuracy of different LST&E retrieval algorithms. In this paper, the accuracy of the Advanced Spaceborne Thermal Emission and Reflection (ASTER) temperature emissivity separation (TES) algorithm with the water vapor scaling (WVS) method and the ASTER standard LSE&E products (without WVS) was validated using 12 ASTER scenes from May 2012 to September 2012 with concurrent ground LST and emissivity measurements collected in an arid area in northwest China during the Heihe Watershed Allied Telemetry Experimental Research experiment. Both the National Center for Environmental Prediction (NCEP) and MOD07 atmospheric profile products were employed to perform the atmospheric correction. The results showed that the WVSTES LSTs retrieved from the two profiles both demonstrate good accuracies, with average biases of 0.34 and 0.24 K and average root-mean-square errors (RMSEs) of 1.52 and 1.46 K for the NCEP and MOD07 profiles, respectively. When the WVS was not applied, we obtained an average bias of 1.26 K and an average RMSE of 2.05 K for the AST08 product. The emissivities from the WVSTES algorithm with the two profiles both showed good agreements with the ground CE312 measurements, with mean differences of the five ASTER bands that were less than 0.01. AST05 showed anomalous emissivity spectra and underestimated the emissivity values of graybody surfaces when compared with the ground data, with mean differences of greater than 0.015, which were more obvious for cases with high water vapor. This paper demonstrated that the WVS method is critical for retrieving ASTER TES LST with accuracies within 1.5 K and emissivity within 0.015 for a wide range of atmospheric conditions and land surface types. [ABSTRACT FROM AUTHOR]

GaoFen1 is the first high-resolution earth observation satellite built in China, and carries four wide field-of-view (WFV) cameras to achieve large-scale monitoring and mapping. However, the unstable attitude measurement accuracy of the satellite generally imparts low geopositioning accuracy and inconsistent geometric error in overlapping areas of WFV images. A feasible and effective large-scale planar block adjustment (PBA) method is presented that corrects the geometric errors of the vast WFV images integrally, further improving the geometric accuracy of these images. In addition, whether ground control points (GCPs) are needed, and the effect of different numbers of GCPs on PBA accuracy is also investigated. Two key technologies are used in this paper. First, a universal PBA error equation based on the virtual control points is presented to allow PBA with or without GCPs. Second, an adjustment method aided by a digital elevation model is adopted to overcome the weak convergence geometry among WFV images, further ensuring stable estimation of PBA. The effectiveness of the proposed method was verified by 664 WFV images covering most of mainland China. The satisfactory experimental results indicate that the method presented herein is reasonable and effective, but that a certain number of GCPs is needed to ensure the accuracy of large-scale PBA results for WFV images. [ABSTRACT FROM AUTHOR]

Due to the rapid urbanization of China, many villages in the urban fringe are enveloped by ever-expanding cities and become so-called urban villages (UVs) with substandard living conditions. Despite physical similarities to informal settlements in other countries (e.g., slums in India), UVs have access to basic public services, and more importantly, villagers own the land legitimately. The resulting socio-economic impact on urban development attracts increasing interest. However, the identification of UVs in previous studies relies on fieldwork, leading to late and incomplete analyses. In this paper, we present three scene-based methods for detecting UVs using high-resolution remotely sensed imagery based on a novel multi-index scene model and two popular scene models, i.e., bag-of-visual-words and supervised latent Dirichlet allocation. In the experiments, our index-based approach produced Kappa values around 0.82 and outperformed conventional models both quantitatively and visually. Moreover, we performed multitemporal classification to evaluate the transferability of training samples across multitemporal images with respect to three methods, and the index-based approach yielded best results again. Finally, using the detection results, we conducted a systematic spatiotemporal analysis of UVs in Shenzhen and Wuhan, two mega cities of China. At the city level, we observe the decline of UVs in urban areas over the recent years. At the block level, we characterize UVs quantitatively from physical and geometrical perspectives and investigate the relationships between UVs and other geographic features. In both levels, the comparison between UVs in Shenzhen and Wuhan is made, and the variations within and across cities are revealed. [ABSTRACT FROM PUBLISHER]

China's industrialized regions have seen increasing occurrence of heavy haze caused by severe particle pollution. However, aerosol retrieval under these circumstances is often excluded from NASA's Moderate Resolution Imaging Spectrometer (MODIS) aerosol products due to cloud mask and suspected high surface reflectance. An algorithm to retrieve the haze aerosol optical thickness (HAOT) is developed using MODIS data to supplement the current MODIS retrieval algorithm. This method includes 1) haze identification, 2) the generation of a surface reflectance database using MODIS data in hazy conditions, and 3) the development of haze aerosol models with four aerosol components simulated by a global 3-D atmospheric chemical transport model (GEOS-Chem). This algorithm was used in combination with the MODIS dense dark vegetation algorithm to retrieve 1 km HAOT over North China Plain from March to September of 2008. The values of the retrieved HAOT values are mostly between 0.7–3, with a correlation coefficient of 0.82 with the Aerosol Robotic NETwork observations and a 19% mean relative difference. Retrieval uncertainties associated with the errors in haze detection, surface reflectance, and haze models were analyzed using ground measurements. [ABSTRACT FROM PUBLISHER]

The MEdium Resolution Spectral Imager (MERSI) is the keystone instrument onboard FengYun-3 (FY-3). After FY-3A, FY-3B MERSI is the second launched in November 2010. Nineteen of the 20 MERSI spectral bands are the reflective solar bands, which cannot be absolutely calibrated onboard. The annual vicarious calibration (VC) based on synchronous in situ measurements at the Dunhuang site is the baseline calibration method for MERSI. To assure frequent and stable calibration updates, a multisite calibration tracking method is developed. This paper presents the FY-3B MERSI postlaunch daily calibration updating method based on multisite calibration tracking with the Dunhuang VC correction, the long-term sensor response on-orbit change, and the calibration performance evaluation. A reflectance-based method is used for the Dunhuang VC, and the reflectance calibration uncertainties are within 3% for most MERSI bands. The multisite calibration tracking method relies on simulated radiation over several stable sites without synchronous in situ measurements. A postlaunch daily calibration updating model is established using a linear function of days since launch to describe the long-term trend. The calibration updating model is validated by the Dunhuang VC, showing the relative bias within 3.5% for most bands. It is found that the shortwave channels of MERSI experience large degradation, particularly the 412-nm band with an annual degradation rate of approximately 18%, whereas most red and near-infrared bands are relatively stable. Using the calibration updating model with the Dunhuang VC correction, the recalibrated MERSI data are validated against Moderate Resolution Imaging Spectroradiometer by near synchronous-nadir-observation analysis, and good agreement is achieved. [ABSTRACT FROM PUBLISHER]

The impacts of the August 21, 2003 geomagnetic storm on the ionosphere over China have been first investigated by using the so-called computerized ionospheric tomography (CIT) technique and the observations of the Crustal Movement Observation Network of China. Tomographic results show that the main ionospheric effects of this geomagnetic storm over China are as follows: 1) the negative storm phase effect appears in the F region and 2) the positive storm phase effect occurs above the F region. Meanwhile, some key features in the ionospheric structure have been revealed in the ionospheric images during the storm; this includes the disturbances and an elongated region of the reduced electron density at the latitude around 32° N. Statistical comparisons are carried out to confirm the reliability of the global-navigation-satellite-system-based CIT reconstruction results using the profile obtained from ionosonde observations. [ABSTRACT FROM AUTHOR]

A new approach is presented in this paper to produce Digital Elevation Model (DEM) in mountainous areas with steep slope using ERS-½ tandem data. In order to reduce the impact of phase errors on the Interferometric Synthetic Aperture Radar (InSAR)-generated DEM, an external DEM such as that from Shuttle Radar Topography Mission (SRTM) is utilized in this approach. The proposed algorithm includes two steps: The first step is to model and remove phase trends with a linear regression analysis before converting phase to height; the second step is to filter unreliable height points before interpolating the DEM from the InSAR height map. The critical points are the following: 1) determining the one-to-one correspondence between the interferogram and the SRTM DEM before knowing the InSAR-derived elevation values and 2) estimating the elevation range of every pixel from SRTM DEM. To solve the first problem, an iteratively geocoding algorithm is performed. A DEM interpolation error model solves the second one. For InSAR data processing, the SRTM DEM is not only usable for modeling systematic phase errors but also for filtering gross height errors. The experiments in Zhangbei and the Three Gorges areas in China show that our approach has improved the accuracy of the resulting DEMs significantly without any ground control points. [ABSTRACT FROM AUTHOR]

Range and sill are two important parameters of a variogram. Their extraction usually Involves experimental fitting of variograms using models specified by the analyst and requires much use of trial and error. The objective of this paper is to desâgn an algorithm for extracting the range and sill of a variogram automatically without fitting a model Combined with the semivariance at the lag of one pixel (γ1), the extracted range and sift were applied to the textural classification of a panchromatic IKONOS image over Xichang, Sichuan Province, China. Results show that any of these three parameters can lead to the increase of the classification accuracy. When all three parameters were used with the raw image data, the average kappa statistic for five window sizes increased from 0.24 to 0.76, indicating promise of the range and sill in texture classification. [ABSTRACT FROM AUTHOR]