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This article takes the measured Total Electron Content (TEC) from the GPS points of the China Regional Crust Observation Network as the starting point to establish a regional ionospheric empirical model. The model's performance is enhanced by considering solar flux and geomagnetic activity data. The refinement function model of the ionospheric TEC diurnal variation component, seasonal variation component, and geomagnetic component is studied. Using the nonlinear least squares method to fit undetermined coefficients, MEFM-ITCR (Multi-parameter Empirical Fusion Model–Ionospheric TEC China Regional Model) is proposed to forecast the regional ionosphere TEC in China. The results show that the standard deviation of MEFM-ITCR residuals is 3.74TECU, and MEFM-ITCR fits the modeling dataset well. Analyses of geographic location variation, seasonal variation, and geomagnetic disturbance were carried out for MEFM-ITCR performance. The results indicate that in the Chinese region, MEFM-ITCR outperforms IRI2020 and NeQuick2 models in terms of forecast accuracy, linear correlation, and model precision for TEC measured using GPS points under different latitudes and longitudes, different seasons, and different geomagnetic disturbances. The empirical TEC model built for the Chinese region in this paper provides a new ionospheric delay correction method for GNSS single frequency users and is of great significance for establishing other new and improving existing ionospheric empirical models. [ABSTRACT FROM AUTHOR]

Continuous evapotranspiration (ET) data with high spatial resolution are crucial for water resources management in irrigated agricultural areas in arid regions. Many global ET products are available now but with a coarse spatial resolution. Spatial-temporal fusion methods, such as the spatial and temporal adaptive reflectance fusion model (STARFM), can help to downscale coarse spatial resolution ET products. In this paper, the STARFM model is improved by incorporating the temperature vegetation dryness index (TVDI) into the data fusion process, and we propose a spatial and temporal adaptive evapotranspiration downscaling method (STAEDM). The modified method STAEDM was applied to the 1 km SSEBOP ET product to derive a downscaled 30 m ET for irrigated agricultural fields of Northwest China. The STAEDM exhibits a significant improvement compared to the original STARFM method for downscaling SSEBOP ET on Landsat-unavailable dates, with an increase in the squared correlation coefficients (r2) from 0.68 to 0.77 and a decrease in the root mean square error (RMSE) from 10.28 mm/10 d to 8.48 mm/10 d. The ET based on the STAEDM additionally preserves more spatial details than STARFM for heterogeneous agricultural fields and can better capture the ET seasonal dynamics. The STAEDM ET can better capture the temporal variation of 10-day ET during the whole crop growing season than SSEBOP. [ABSTRACT FROM AUTHOR]

The high-precision prediction of sea clutter reflectivity is helpful in improving the performance of marine radar and sea surface remote sensing capabilities. Under the same sea state, when the significant wave height, wave period, wind speed, and other marine environmental parameters are different, the backward reflectivity of the sea clutter corresponding to the wave structure is not the same. Due to the complex and variable nature of sea clutter characteristics across various wave structures, a meticulous classification of wave structures by integrating multiple marine environmental parameters enables the achievement of the high-precision prediction of sea clutter reflectivity. In this study, utilizing measured data of diverse marine environmental parameters in the Yellow Sea, China, we applied the Affinity Propagation algorithm to data clustering. Based on the clustering outcomes, we accomplished a refined classification of wave structures and developed a discriminant model to precisely classify the refined wave structure, facilitating the categorization of new data. In order to achieve more accurate predictions of sea clutter reflectivity, this paper proposes a deep neural network model named GIT-HYB-DNN, which combines the empirical models GIT and HYB. The GIT-HYB-DNN model is applied to predict the reflectivity for each wave structure category separately. The results demonstrate that the root mean square errors of sea clutter reflectivity predictions for different wave structure categories in this study range from 0.62 dB to 0.84 dB. The prediction errors are significantly reduced compared to the root mean square error of 1.08 dB, which was obtained without refined wave structure classification. This study holds theoretical significance and practical value for the investigation of sea clutter characteristics and the selection of radar parameters. [ABSTRACT FROM AUTHOR]

Precipitation datasets derived from reanalysis products play a crucial role in weather forecasting and hydrological applications. This paper aims to evaluate the performance of two distinct reanalysis precipitation products, i.e., the first-generation Chinese global land-surface reanalysis precipitation product (CRA40) and the fifth-generation European reanalysis precipitation product (ERA5), over mainland China. The evaluation is based on continuous and categorical statistical indicators with daily-scale gridded-point rain gauge data obtained from Chinese surface meteorological stations. The findings of this study can be summarized as follows: (1) CRA40 demonstrates a clear superiority over ERA5 in terms of the 13-year daily mean precipitation and seasonal daily precipitation. CRA40 exhibits better correlation coefficients (0.97), relative biases (5.25%), root mean square errors (0.34 mm), and fractional standard errors (0.05). (2) Both reanalyzed precipitation products generally exhibit an overestimation of precipitation in mainland China. The degree of overestimation is particularly pronounced in dry climatic regions (e.g., QZ, XJ), while wet regions (e.g., CJ, HN) demonstrate relatively less overestimation. (3) ERA5 shows better performance in the detection of daily precipitation. Neither CRA40 nor ERA5 can capture heavy precipitation events well. These findings are expected to advance our understanding of the strengths and limitations of the reanalysis precipitation products, CRA40 and ERA5, over China. [ABSTRACT FROM AUTHOR]

The simulation and prediction of coastline changes are of great significance for the development and scientific management of coastal zones. Coastline changes are difficult to capture completely but appear significantly periodic over a long time series. In this paper, the transformer model is used to learn the changing trend of the coastline so as to deduce the position of the coastline in the coming year. First, we use the distance regularization level set evolution (DRLSE) model for instantaneous waterline extraction (IWE) from preprocessed Landsat time-series images from 2010–2020 in Weitou Bay, China. Then, tidal correction (TC) is performed on the extracted instantaneous waterline dataset to obtain coastlines projected to a single reference tidal datum. Finally, the coastline datasets from 2010–2019 are used for model training, and the coastline in 2020 is used for accuracy assessment. Three precision evaluation methods, including receiver operating characteristic curve matching, the mean offset, and the root mean square error, were used to verify the predicted coastline data. The receiver operating characteristic curve was specifically designed and improved to evaluate the accuracy of the obtained coastline. Compared with the support vector regression (SVR) and long–short-term memory (LSTM) methods, the results showed that the coastline predicted by the transformer model was the closest to the accurate extracted coastline. The accuracies of the correct values corresponding to SVR, LSTM, and transformer models were 88.27%, 94.08%, and 98.80%, respectively, which indicated the accuracy of the coastline extraction results. Additionally, the mean offset and root mean square error were 0.32 pixels and 0.57 pixels, respectively. In addition, the experimental results showed that tidal correction is important for coastline prediction. Moreover, through field investigations of coastlines, the predicted results obtained for natural coastlines were more accurate, while the predicted results were relatively poor for some artificial coastlines that were intensely influenced by human activities. This study shows that the transformer model can provide natural coastline changes for coastal management. [ABSTRACT FROM AUTHOR]

In this paper, we forecasted the ionospheric total electron content (TEC) over the region of China using the bidirectional convolutional gated recurrent unit (BiConvGRU) model. We first generated the China Regional Ionospheric Maps (CRIMs) using GNSS observations provide by the Crustal Movement Observation Network of China (CMONOC). We then used gridded TEC maps from 2015 to 2018 with a 1 h interval from the CRIMs as the dataset, including quiet periods and storm periods of ionospheric TEC. The BiConvGRU model was then utilized to forecast the ionospheric TEC across China for the year 2018. The forecasted TEC was compared with the TEC from the International Reference Ionosphere (IRI-2016), Convolutional Long Short-Term Memory (ConvLSTM), Convolutional Gated Recurrent Unit (ConvGRU), Bidirectional Convolutional Long Short-Term Memory (BiConvLSTM), and the 1-day Predicted Global Ionospheric Map (C1PG) provided by the Center for Orbit Determination in Europe (CODE). In addition, indices including Kp, ap, Dst and F10.7 were added to the training dataset to improve the forecasting accuracy of the model (-A indicates no indices, while -B indicates with indices). The results verified that the prediction accuracies of the models integrated with these indices were significantly improved, especially during geomagnetic storms. The BiConvGRU-B model presented a decrease of 41.5%, 22.3%, and 13.2% in the root mean square error (RMSE) compared to the IRI-2016, ConvGRU, and BiConvLSTM-B models during geomagnetic storm days. Furthermore, at a specific grid point, the BiConvGRU-B model showed a decrease of 42.6%, 49.1%, and 31.9% in RMSE during geomagnetic quiet days and 30.6%, 34.1%, and 15.1% during geomagnetic storm days compared to the IRI-2016, C1PG, and BiConvLSTM-B models, respectively. In the cumulative percentage analysis, the BiConvGRU-B model had a significantly higher percentage of mean absolute error (MAE) within the range of 0–1 TECU in all seasons compared to the BiConvLSTM-B model. Meanwhile, the BiConvGRU-B model outperformed the BiConvLSTM-B model with lower RMSE for each month of 2018. [ABSTRACT FROM AUTHOR]

Landslides are a major concern in the mountainous regions of southwest China, leading to significant loss of life and property damage. Therefore, it is crucial to identify potential landslides for early warning and mitigation. stacking-InSAR, a technique used for landslide identification in a wide area, has been found to be faster than conventional time-series InSAR. However, the dense vegetation in southwest China mountains has an adverse impact on the coherence of stacking-InSAR, resulting in more noise and inaccuracies in landslide identification. To address this problem, this paper proposes an improved seasonal interferometry stacking-InSAR method. It uses Sentinel-1 satellite data from 2017 to 2022. The study area is the river valley section of the G213 road from Wenchuan County to Mao County. The study reveals the characteristics of seasonal decoherence in the steep mountainous region, and identifies a total of 21 potential landslides using the improved method. Additionally, optical satellite imagery and LiDAR data were used to assist in the identification of potential landslides. The results of the conventional stacking-InSAR method and the improved seasonal interferometry stacking-InSAR method are compared, showing that the latter is more effective in noise suppression caused by low coherence. Their standard deviations were reduced by 46%, 22%, 10%, and 14%, respectively, using the quantitative statistics for the four tested areas. The proposed method provides an efficient and effective approach for detecting potential landslides in the mountainous regions of southwest China. It can serve as a valuable technical reference for future landslide identification studies in this area. [ABSTRACT FROM AUTHOR]

To improve the accuracy and reliability of precipitation estimation, numerous models based on machine learning technology have been developed for integrating data from multiple sources. However, little attention has been paid to extracting the spatiotemporal correlation patterns between satellite products and rain gauge observations during the merging process. This paper focuses on this issue by proposing an integrated framework to generate an accurate and reliable spatiotemporal estimation of precipitation. The proposed framework integrates Funk-Singular Value Decomposition (F-SVD) in the recommender system to achieve the accurate spatial distribution of precipitation based on the spatiotemporal interpolation of rain gauge observations and Convolutional Long Short-Term Memory (ConvLSTM) to merge precipitation data from interpolation results and satellite observation through exploiting the spatiotemporal correlation pattern between them. The framework (FS-ConvLSTM) is utilized to obtain hourly precipitation merging data with a resolution of 0.1° in Jianxi Basin, southeast of China, from both rain gauge data and Global Precipitation Measurement (GPM) from 2006 to 2018. The LSTM and Inverse Distance Weighting (IDW) are constructed for comparison purposes. The results demonstrate that the framework could not only provide more accurate precipitation distribution but also achieve better stability and reliability. Compared with other models, it performs better in variation process description and rainfall capture capability, and the root mean square error (RSME) and probability of detection (POD) are improved by 63.6% and 22.9% from the original GPM, respectively. In addition, the merged precipitation combines the strength of different data while mitigating their weaknesses and has good agreement with observed precipitation in terms of magnitude and spatial distribution. Consequently, the proposed framework provides a valuable tool to improve the accuracy of precipitation estimation, which can have important implications for water resource management and natural disaster preparedness. [ABSTRACT FROM AUTHOR]

Highly accurate near-real-time satellite precipitation estimates (SPEs) are important for hydrological forecasting and disaster warning. The near-real quantitative precipitation estimates (REGC) of the recently developed Chinese geostationary meteorological satellite Fengyun 4A (FY4A) have the advantage of high spatial and temporal resolution, but there are errors and uncertainties to some extent. In this paper, a self-adaptive ill-posed least squares scheme based on sequential processing (SISP) is proposed and practiced in mainland China to correct the real-time biases of REGC hour by hour. Specifically, the scheme adaptively acquires sample data by setting temporal and spatial windows and constructs an error-correction model based on the ill-posed least squares method from the perspectives of climate regions, topography, and rainfall intensity. The model adopts the sequential idea to update satellite precipitation data within time windows on an hour-by-hour basis and can correct the biases of real-time satellite precipitation data using dynamically changing parameters, fully taking into account the influence of precipitation spatial and temporal variability. Only short-term historical data are needed to accurately rate the parameters. The results show that the SISP algorithm can significantly reduce the biases of the original REGC, in which the values of relative bias (RB) in mainland China are reduced from 11.2% to 3.3%, and the values of root mean square error (RMSE) are also reduced by about 17%. The SISP algorithm has a better correction in humid and semi-humid regions than in arid and semi-arid regions and is effective in reducing the negative biases of precipitation in each climate region. In terms of rain intensity, the SISP algorithm can improve the overestimation of satellite precipitation estimates for low rain intensity (0.2–1 mm/h), but the correction for high rain intensity (>1 mm/h) needs further improvement. The error component analysis shows that the SISP algorithm can effectively correct the hit bias. This study serves as a valuable reference for real-time bias correction using short-term accumulated precipitation data. [ABSTRACT FROM AUTHOR]

Precise Point Positioning (PPP) is an official service of the BeiDou Global Navigation Satellite System (BDS-3) through the PPP-B2b signal. In this paper, we mainly focus on the long-term performance evaluation of BDS-3 PPP-B2b products and their application in time service. Since the PPP-B2b product is only available in and around China area, the arcs of PPP-B2b products are about several hours. We propose to evaluate the time datum stability by using all available satellites. Then, 557 day PPP-B2b products are collected for this experiment. The results show that there are large jumps in the GPS satellite clock time datum series. However, the BDS-3 satellite clock datum stability is almost at the same level with current Space State Representation (SSR) corrections from the International Global navigation satellite system Service (IGS). The difference between PPP-B2b GPS and BDS-3 satellite clock time datum will be absorbed into the Inter System Bias (ISB) parameter. Thus, it should be specially noted that the ISB parameter cannot be estimated as constant values if users use PPP-B2b products. In addition, the accuracy of the BDS-3 satellite clock is significantly better than that of the GPS for both the Root Mean Square Error (RMSE) and standard deviation (STD). The average Signal in Space Range Errors (SISREs) is 0.22 ns and 0.13 ns for GPS and BDS-3, respectively. The one-way timing experiment shows BDS-3 timing stability is 2.9 × 10−14@104 s. In addition, 10 baselines from 13 km to 4494 km are formed for time synchronization evaluation by using PPP-B2b products. The average RMSEs of time synchronization is from 0.46 ns to 1.58 ns and from 0.66 ns to 1.19 ns for GPS and BDS-3, respectively. As for STD, the average values are from 0.27 ns to 0.74 ns and from 0.27 ns to 0.47 ns for GPS and BDS-3, respectively. Overall, the results show that the time datum stability, accuracy, and service performance of BDS-3 PPP-B2b products has been stable over the past two years. [ABSTRACT FROM AUTHOR]

Total phosphorus (TP) concentration is high in countless small inland waterbodies in Hubei province, middle China, which is threating the water environment. However, there are almost no ground-based water quality monitoring points in small inland waterbodies, because the cost of time, labor, and money is high and it does not meet the needs of spatiotemporal dynamic monitoring. Remote sensing provides an effective tool for TP concentration monitoring spatiotemporally. However, monitoring the TP concentration of small inland waterbodies is challenging for satellite remote sensing due to the inadequate spatial resolution. Recently, unmanned aerial vehicles (UAV) have been applied to quantitatively retrieve the spatiotemporal distribution of TP concentration without the challenges of cloud cover and atmospheric effects. Although state-of-the-art algorithms to retrieve TP concentration have been improved, specific models are only used for specific water quality parameters or regions, and there are no robust and reliable TP retrieval models for small inland waterbodies at this time. To address this issue, six machine learning methods optimized by intelligent optimization algorithms (IOA-ML models) have been developed to quantitatively retrieve TP concentration combined with the reflectance of original bands and selected band combinations of UAV multispectral images. We evaluated the performances of models in terms of coefficient of determination (R2), root mean squared error (RMSE), and residual prediction deviation (RPD). The results showed that the R2 of the six IOA-ML models for training, validation, and test sets were 0.8856–0.984, 0.8054–0.8929, and 0.7462–0.9045, respectively, indicating the methods had high precision and transferability. The extreme gradient boosting optimized by genetic algorithm (GA-XGB) performed best, with the highest precision for the validation and test sets. The spatial distribution of TP concentration of each flight derived from different models had similar distribution characteristics. This paper provides a reference for promoting the intelligent and automatic level of water environment monitoring in small inland waterbodies. [ABSTRACT FROM AUTHOR]

Soil freeze depth variations greatly affect energy exchange, carbon exchange, ecosystem diversity, and the water cycle. Given the importance of these processes, obtaining freeze depth data over large scales is an important focus of research. This paper presents a simple empirical algorithm to estimate the maximum seasonally frozen depth (MSFD) of seasonally frozen ground (SFG) in snowy regions. First, the potential influences of driving factors on the MSFD variations were quantified in the baseline period (1981–2010) based on the 26 meteorological stations within and around the SFG region of Heilongjiang province. The three variables that contributed more than 10% to MSFD variations (i.e., air freezing index, annual mean snow depth, and snow cover days) were considered in the analysis. A simple multiple linear regression to estimate soil freeze depth was fitted (1981–2010) and verified (1975–1980 and 2011–2014) using ground station observations. Compared with the commonly used simplified Stefan solution, this multiple linear regression produced superior freeze depth estimations, with the mean absolute error and root mean square error of the station average reduced by over 20%. By utilizing this empirical algorithm and the ERA5-Land reanalysis dataset, the multi-year average MSFD (1981–2010) was 132 cm, ranging from 52 cm to 186 cm, and MSFD anomaly exhibited a significant decreasing trend, at a rate of −0.38 cm/decade or a net change of −28.14 cm from 1950–2021. This study provided a practical approach to model the soil freeze depth of SFG over a large scale in snowy regions and emphasized the importance of considering snow cover variables in analyzing and estimating soil freeze depth. [ABSTRACT FROM AUTHOR]

GaoFen 7 (GF-7) is China's first submeter high-resolution stereo mapping satellite with dual-linear-array cameras and a laser altimeter system onboard for high-precision mapping. To further take advantage of the very high elevation accuracy of laser altimetry data and the high relative accuracy with stereo images, an innovative combined adjustment method for GF-7 stereo images with laser altimetry data is presented in this paper. In this method, two flexible and effective schemes were proposed to extract the elevation control point according to the registration of footprint images and stereo images and then utilized as vertical control in the block adjustment to improve the elevation accuracy without ground control points (GCPs). The validation experiments were conducted in Shandong, China, with different terrains. The results demonstrated that, after using the laser altimetry data, the root mean square error (RMSE) of elevation was dramatically improved from the original 2.15 m to 0.75 m, while the maximum elevation error was less than 1.6 m. Moreover, by integrating a few horizontal control points, the planar and elevation accuracy can be simultaneously improved. The results show that the method will be useful for reducing the need for field survey work and improving mapping efficiency. [ABSTRACT FROM AUTHOR]

Different radiometer sensors have different frequencies, spatial resolutions, and time resolutions, which lead to inconsistencies in ice drift products retrieved by radiometer sensors. Based on the continuous maximum cross-correlation method, in this paper, we used China's FY-3 and HY-2 satellite radiometer data to generate sea ice drift products; we further evaluated the consistency between them and sea ice drift products retrieved from AMSR2 and SSMIS satellite radiometer data, which could help in future retrieval accuracies of more radiometer sea ice drift products. The results show that ice drift products with good reliability can be obtained by retrievals using 37 and 89 GHz channels of FY-3 and HY-2 radiometer bright temperature data. Compared with the buoy data, the root mean square errors (RMSEs) of the 37 GHz HY-2 sea ice drift product (at an interval of 6 days) were 1.40 cm/s and 7.31° for speed and direction, respectively, and the relative errors (REs) were 5.78% and 6.44%, respectively. The RMSEs of the 37 GHz FY-3 sea ice drift product were 0.77 cm/s and 6.49° for speed and direction, respectively, and the REs were 4.38% and 9.23%, respectively. Moreover, comparisons between sea ice drift vectors derived from AMSR2 and SSMIS satellites showed good quantitative agreement. [ABSTRACT FROM AUTHOR]

Because the penetration depth of electromagnetic waves in forests is large in the longer wavelength band, most traditional forest height estimation methods are carried out using polarimetric interferometry synthetic aperture radar (PolInSAR) data of the L or P band, and the estimation method is a three-stage method based on the random volume over ground (RVoG) model. For X-band electromagnetic waves, the penetration depth of radar waves in forests is limited, so the traditional forest height estimation method is no longer applicable. In view of the above problems, in this paper we propose a new forest height estimation strategy for airborne X-band PolInSAR data. Firstly, the sub-view interferometric SAR pairs obtained via frequency segmentation (FS) in the Doppler domain are used to extend the polarimetric interferometry coherence coefficient (PolInCC) range of the original SAR image under different polarization states, so as to obtain the accurate ground phase. For the determination of the effective volume coherence coefficient (VCC), part of the fitting line of the extended-range PolInCC distribution that is intercepted by the fixed extinction coherence coefficient curve (FECCC) of the fixed range is averaged to obtain the accurate effective VCC. Finally, the high-precision forest canopy height in the X-band is estimated using the effective VCC with the ground phase removed in the look-up table (LUT). The effectiveness of the proposed method was verified using airborne-measured data obtained in Shaanxi Province, China. The comparison was carried out using different strategies, in which we substituted one step of the process with the conventional method. The results indicated that our new strategy could reduce the root mean square error (RMSE) of the predicted canopy height vastly to 1.02 m, with a lower estimation height error of 12.86%. [ABSTRACT FROM AUTHOR]

The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin'anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models. [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]

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]

Lop Nur is a famous dry lake in the arid region of China. It was an important section of the ancient "Silk Road", famous in history as the prosperous communication channel between Eastern and Western cultures. At present, there is no surface water in Lop Nur Lake basin, and on SAR (Synthetic Aperture Radar) images, it looks like an "Ear". The objective of this paper is to interpret the Lop Nur phenomenon from the perspective of scattering mechanisms. Based on field investigation and analysis of sample properties, a two-layer scattering structure is proposed with detailed explanations of scattering mechanisms. In view of the rough surface, the MIEM (Modified Integral Equation Model) was introduced to represent air-surface scattering in Lop Nur. Then, a two-layer scattering model was developed which can describe surface scattering contribution. Using polarimetric decomposition, validations were carried out, and the RMSE (root mean square error) values for the HH and VV polarizations were found to be 1.67 dB and 1.06 dB, respectively. Furthermore, according to model parametric analysis, surface roughness was identified as an apparent reason for the "Ear" feature. In addition, the polarimetric decomposition result also showed that the volume scattering part had rich texture information and could portray the "Ear" feature exactly compared with the other two parts. It is maintained that subsurface properties, mainly generating volume scattering, can determine the surface roughness under the certain climate conditions, according to geomorphological dynamics, which can help to develop an inversion technology for Lop Nur. [ABSTRACT FROM AUTHOR]

Deformation prediction for a salt solution mining area is essential to mining environmental protection. The combination of Synthetic Aperture Radar Interferometry (InSAR) technique with Probability Integral Method (PIM) has proven to be powerful in predicting mining-induced subsidence. However, traditional mathematical empirical models (such as linear model or linear model combined with periodical function) are mostly used in InSAR approaches, ignoring the underground mining mechanisms, which may limit the accuracy of the retrieved deformations. Inaccurate InSAR deformations will transmit an unavoidable error to the estimated PIM parameters and the forward predicted subsidence, which may induce more significant errors. Besides, theoretical contradictory and non-consistency between InSAR deformation model and future prediction model is another limitation. This paper introduces the Coordinate-Time (CT) function into InSAR deformation modeling. A novel time-series InSAR model (namely, CT-PIM) is proposed as a substitute for traditional InSAR mathematical empirical models and directly applied for future dynamic prediction. The unknown CT-PIM parameters can be estimated directly via InSAR phase observations, which can avoid the error propagation from the InSAR-generated deformations. The new approach has been tested by both simulated and real data experiments over a salt mine in China. The root mean square error (RMSE) is determined as ±10.9 mm, with an improvement of 37.2% compared to traditional static PIM prediction method. The new approach provides a more robust tool for the forecasting of mining-induced hazards in salt solution mining areas, as well as a reference for ensuring the environment protection and safety management. [ABSTRACT FROM AUTHOR]

The total suspended solid (TSS) concentration (mg/L) is an important parameter of water quality in coastal waters. It is of great significance to monitor the spatiotemporal distribution and variation of TSS as well as its influencing factors. In this study, a quantitative retrieval model of TSS in Jiaozhou Bay (JZB) was established based on Landsat images from 1984 to 2020 (coefficient of determination (R2) = 0.77, root mean square error (RMSE) = 1.82 mg/L). In this paper, first, the long-term spatiotemporal variation of TSSs in JZB is revealed and, next, its influencing factors are further analyzed. The results show that the annual average TSSs in JZB reached their highest level in 1993 and their lowest level in 2016, showing a decreasing trend during the past decades. The TSSs were high in spring and winter and low in summer and autumn. The spatial distribution of the TSSs in JZB was similar at different timepoints, i.e., high in the northwest and gradually decreasing to the southeast. Tidal elevation exerted a significant influence on the daily variation of TSSs, and wind speed had a significant influence on the seasonal variation of TSSs. The Dagu River's discharge only affected the TSSs at the river mouth. Tidal elevation, river discharge, and wind speed were major influence factors for TSSs' variation in JZB. The results showed that the empirical model based on Landsat satellite data could be used to effectively monitor the long-term variation of TSSs in JZB. [ABSTRACT FROM AUTHOR]

Digital Surface Model (DSM) derived from high resolution satellite imagery is important for various applications. GFDM is China's first civil optical remote sensing satellite with multiple agile imaging modes and sub-meter resolution. Its panchromatic resolution is 0.5 m and 1.68 m for multi-spectral images. Compared with the onboard stereo viewing instruments (0.8 m for forward image, 0.65 m for back image, and 2.6 m for back multi-spectrum images) of GF-7, a mapping satellite of China in the same period, their accuracy is very similar. However, the accuracy of GFDM DSM has not yet been verified or fully characterized, and the detailed difference between the two has not yet been assessed either. This paper evaluates the DSM accuracy generated by GFDM and GF-7 satellite imagery using high-precision reference DSM and the observations of Ground Control Points (GCPs) as the reference data. A method to evaluate the DSM accuracy based on regional DSM errors and GCPs errors is proposed. Through the analysis of DSM subtraction, profile lines, strips detection and residuals coupling differences, the differences of DSM overall accuracy, vertical accuracy, horizontal accuracy and the strips errors between GFDM DSM and GF-7 DSM are evaluated. The results show that the overall accuracy of both is close while the vertical accuracy is slightly different. When regional DSM is used as the benchmark, the GFDM DSM has a slight advantage in elevation accuracy, but there are some regular fluctuation strips with small amplitude. When GCPs are used as the reference, the elevation Root Mean Square Error (RMSE) of GFDM DSM is about 0.94 m, and that of GF-7 is 0.67 m. GF-7 DSM is more accurate, but both of the errors are within 1 m. The DSM image residuals of the GF-7 are within 0.5 pixel, while the residuals of GFDM are relatively large, reaching 0.8 pixel. [ABSTRACT FROM AUTHOR]

Accurate determination of phenological information of crops is essential for field management and decision-making. Remote sensing time-series data are widely used for extracting phenological phases. Existing methods mainly extract phenological phases directly from individual remote sensing time-series, which are easily affected by clouds, noise, and mixed pixels. This paper proposes a novel method of phenological phase extraction based on the time-weighted dynamic time warping (TWDTW) algorithm using MODIS Normalized Difference Vegetation Index (NDVI) 5-day time-series data with a spatial resolution of 500 m. Firstly, based on the phenological differences between winter wheat and other land cover types, winter wheat distribution is extracted using the TWDTW classification method, and the results show that the overall classification accuracy and Kappa coefficient reach 94.74% and 0.90, respectively. Then, we extract the pure winter-wheat pixels using a method based on the coefficient of variation, and use these pixels to generate the average phenological curve. Next, the difference between each winter-wheat phenological curve and the average winter-wheat phenological curve is quantitatively calculated using the TWDTW algorithm. Finally, the key phenological phases of winter wheat in the study area, namely, the green-up date (GUD), heading date (HD), and maturity date (MD), are determined. The results show that the phenological phase extraction using the TWDTW algorithm has high accuracy. By verification using phenological station data from the Meteorological Data Sharing Service System of China, the root mean square errors (RMSEs) of the GUD, HD, and MD are found to be 9.76, 5.72, and 6.98 days, respectively. Additionally, the method proposed in this article is shown to have a better extraction performance compared with several other methods. Furthermore, it is shown that, in Hebei Province, the GUD, HD, and MD are mainly affected by latitude and accumulated temperature. As the latitude increases from south to north, the GUD, HD, and MD are delayed, and for each 1° increment in latitude, the GUD, HD, and MD are delayed by 4.84, 5.79, and 6.61 days, respectively. The higher the accumulated temperature, the earlier the phenological phases occur. However, latitude and accumulated temperature have little effect on the length of the phenological phases. Additionally, the lengths of time between GUD and HD, HD and MD, and GUD and MD are stable at 46, 41, and 87 days, respectively. Overall, the proposed TWDTW method can accurately determine the key phenological phases of winter wheat at a regional scale using remote sensing time-series data. [ABSTRACT FROM AUTHOR]

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost. [ABSTRACT FROM AUTHOR]

Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem health and is critical for water purification, carbon cycle, and biodiversity conservation. Accurate AGB estimation is essential for the monitoring and supervision of ecosystems, especially in seasonal floodplain wetlands. This paper explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modeling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site). We use five-fold cross-validation to verify the model effectiveness. The results indicated that the RF and XGBoost models had a robust and efficient performance (with root mean square error (RMSE) of 126.571 g·m−2 and R2 of 0.844 for RF, RMSE of 112.425 g·m−2 and R2 of 0.869 for XGBoost), and the XGBoost models, by contrast, performed better. Both traditional and red-edge vegetation indices (VIs) obtained satisfactory results of AGB estimation (RMSE = 127.936 g·m−2, RMSE = 125.879 g·m−2 in XGBoost models, respectively), with the red-edge VIs contributed more to the AGB models. Moreover, we selected eight gray-level co-occurrence matrix (GLCM) textures calculated by four processing window sizes using the mean value of four offsets, and further analyzed the results of three analysis sets. Textures derived from traditional and red-edge bands using a 7 × 7 window size performed better in biomass estimation. This finding suggested that textures derived from the traditional bands were as important as the red-edge bands. The introduction of textures moderately improved the accuracy of modeling AGB, whereas the use of textures alo ne was not satisfactory. This research demonstrated that using the Sentinel-2 MSI and the two ensemble algorithms is an effective method for long-term dynamic monitoring and assessment of grass AGB in seasonal floodplain wetlands, which can support sustainable management and carbon accounting of wetland ecosystems. [ABSTRACT FROM AUTHOR]

The forest canopy height (FCH) plays a critical role in forest quality evaluation and resource management. The accurate and rapid estimation and mapping of the regional forest canopy height is crucial for understanding vegetation growth processes and the internal structure of the ecosystem. A stacking algorithm consisting of multiple linear regression (MLR), support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF) was used in this paper and demonstrated optimal performance in predicting the forest canopy height by synergizing Sentinel-2 images acquired from the cloud-based computation platform Google Earth Engine (GEE) with data from ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). This research was conducted to achieve continuous mapping of the canopy height of plantations in Saihanba Mechanical Forest Plantation, which is located in Chengde City, northern Hebei province, China. The results show that stacking achieved the best prediction accuracy for the forest canopy height, with an R2 of 0.77 and a root mean square error (RMSE) of 1.96 m. Compared with MLR, SVM, kNN, and RF, the RMSE obtained by stacking was reduced by 25.2%, 24.9%, 22.8%, and 18.7%, respectively. Since Sentinel-2 images and ICESat-2 data are publicly available, this opens the door for the accurate mapping of the continuous distribution of the forest canopy height globally in the future. [ABSTRACT FROM AUTHOR]

In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China's Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran's I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of "east-west" spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate. [ABSTRACT FROM AUTHOR]

Accurate solar radiation estimation is very important for solar energy systems and is a precondition of solar energy utilization. Due to the rapid development of new energy sources, the demand for surface solar radiation estimation and observation has grown. Due to the scarcity of surface radiation observations, high-precision remote sensing data are trying to fill this gap. In this paper, a global solar irradiance estimation method (in different months, seasons, and weather conditions), using data from the advanced geosynchronous radiation imager (AGRI) sensor onboard the FengYun-4A satellite with cloud index methodology (CSD-SI), was tested. It was found that the FengYun-4A satellite data could be used to calculate the clear sky index through the Heliosat-2 method. Combined with McClear, the global horizontal irradiance (GHI) and the direct normal irradiance (DNI) in northeast China could be accurately obtained. The estimated GHI accuracy under clear sky was slightly affected by the seasons and the normalized root mean square error (nRMSE) values (in four sites) were higher in summer and autumn (including all weather conditions). Compared to the estimated GHI, the estimated DNI was less accurate. It was found that the estimated DNI in October had the best performance. In the meantime, the nRMSE, the normalized mean absolute error (nMAE), and the normalized mean bias error (nMBE) of Zhangbei were 35.152%, 27.145%, and −8.283%, while for Chengde, they were 43.150%, 28.822%, and −13.017%, respectively. In addition, the estimated DNI at ground level was significantly higher than the actual observed value in autumn and winter. Considering that the error mainly came from the overestimation of McClear, a new DNI radiation algorithm during autumn and winter is proposed for northern China. After applying the new algorithm, the nRMSE decreased from 49.324% to 48.226% for Chengde and from 48.342% to 41.631% for Zhangbei. Similarly, the nMBE decreased from −32.351% to −18.823% for Zhangbei and from −26.211% to −9.107% for Chengde. [ABSTRACT FROM AUTHOR]

The data assimilation technique is an effective method for reducing initial condition errors in numerical weather prediction (NWP) models. This paper evaluated the potential of the weather research and forecasting (WRF) model and its three-dimensional data assimilation (3DVar) module in improving the accuracy of rainfall-runoff prediction through coupled atmospheric-hydrologic systems. The WRF model with the assimilation of radar reflectivity and conventional surface and upper-air observations provided the improved initial and boundary conditions for the hydrological process; subsequently, three atmospheric-hydrological systems with variable complexity were established by coupling WRF with a lumped, a grid-based Hebei model, and the WRF-Hydro modeling system. Four storm events with different spatial and temporal rainfall distribution from mountainous catchments of northern China were chosen as the study objects. The assimilation results showed a general improvement in the accuracy of rainfall accumulation, with low root mean square error and high correlation coefficients compared to the results without assimilation. The coupled atmospheric-hydrologic systems also provide more accurate flood forecasts, which depend upon the complexity of the coupled hydrological models. The grid-based Hebei system provided the most stable forecasts regardless of whether homogeneous or inhomogeneous rainfall was considered. Flood peaks before assimilation were underestimated more in the lumped Hebei model relative to the other coupling systems considered, and the model seems more applicable for homogeneous temporal and spatial events. WRF-Hydro did not exhibit desirable predictions of rapid flood process recession. This may reflect increasing infiltration due to the interaction of atmospheric and land surface hydrology at each integration, resulting in mismatched solutions for local runoff generation and confluence. [ABSTRACT FROM AUTHOR]

Understanding the spatial distribution of populations at a finer spatial scale has important value for many applications, such as disaster risk rescue operations, business decision-making, and regional planning. In this study, a random forest (RF)-based population density mapping method was proposed in order to generate high-precision population density data with a 100 m × 100 m grid in mainland China in 2015 (hereafter referred to as 'Popi'). Besides the commonly used elevation, slope, Normalized Vegetation Index (NDVI), land use/land cover, roads, and National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS), 16,101,762 records of points of interest (POIs) and 2867 county-level censuses were used in order to develop the model. Furthermore, 28,505 township-level censuses (74% of the total number of townships) were collected in order to evaluate the accuracy of the Popi product. The results showed that the utilization of multi-source data (especially the combination of POIs and NPP/VIIRS data) can effectively improve the accuracy of population mapping at a finer scale. The feature importances of the POIs and NPP/VIIRS are 0.49 and 0.14, respectively, which are higher values than those obtained for other natural factors. Compared with the Worldpop population dataset, the Popi data exhibited a higher accuracy. The number of accurately-estimated townships was 19,300 (67.7%) in the Popi product and 16,237 (56.9%) in the Worldpop product. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were 14,839 and 7218, respectively, for Popi, and 18,014 and 8572, respectively, for Worldpop. The research method in this paper could provide a reference for the spatialization of other socioeconomic data (such as GDP). [ABSTRACT FROM AUTHOR]

The Corona Virus Disease 2019 (COVID-19) appeared in Wuhan, China, at the end of 2019, spreading from there across China and within weeks across the whole world. In order to control the rapid spread of the virus, the Chinese government implemented a national lockdown policy. It restricted human mobility and non-essential economic activities, which, as a side effect, resulted in the reduction of the emission of pollutants and thus the improvement of the air quality in many cities in China. In this paper, we report on a study on the changes in air quality in the Guanzhong Basin during the COVID-19 lockdown period. We compared the concentrations of PM2.5, PM10, SO2, NO2, CO and O3 obtained from ground-based monitoring stations before and after the COVID-19 outbreak. The analysis confirmed that the air quality in the Guanzhong Basin was significantly improved after the COVID-19 outbreak. During the emergency response period with the strictest restrictions (Level-1), the concentrations of PM2.5, PM10, SO2, NO2 and CO were lower by 37%, 30%, 29%, 52% and 33%, respectively, compared with those before the COVID-19 outbreak. In contrast, O3 concentrations increased substantially. The changes in the pollutant concentrations varied between cities during the period of the COVID-19 pandemic. The highest O3 concentration changes were observed in Xi'an, Weinan and Xianyang city; the SO2 concentration decreased substantially in Tongchuan city; the air quality had improved the most in Baoji City. Next, to complement the sparsely distributed air quality ground-based monitoring stations, the geographic and temporally weighted regression (GTWR) model, combined with satellite observations of the aerosol optical depth (AOD) and meteorological factors was used to estimate the spatial and temporal distributions of PM2.5 and PM10 concentrations with a resolution of 6 km × 6 km before and after the COVID-19 outbreak. The model was validated by a comparison with ground-based observations from the air quality monitoring network in five cities in the Guanzhong Basin with excellent statistical metrics. For PM2.5 and PM10 the correlation coefficients R2 were 0.86 and 0.80, the root mean squared errors (RMSE) were 11.03 µg/m3 and 14.87 µg/m3 and the biases were 0.19 µg/m3 and −0.27 µg/m3, which led to the conclusion that the GTWR model could be used to estimate the PM concentrations in locations where monitoring data were not available. Overall, the PM concentrations in the Guanzhong Basin decreased substantially during the lockdown period, with a strong initial decrease and a slower one thereafter, although the spatial distributions remained similar. [ABSTRACT FROM AUTHOR]

Pseudo-invariant calibration sites (PICS) have been used for the radiometric calibration and stability monitoring of satellite optical sensors. Several stable PICS, such as those in the Sahara Desert in North Africa, were selected for the vicarious calibration of earth remote sensing satellites. However, the selection procedure of PICSs in the whole of Northwest China has not been fully explored before. This paper presents a novel technique for selecting PICS in Northwest China by combined using the coefficient of variation (CV) and the iteratively reweighted multivariate alteration detection (IR-MAD) technique. IR-MAD, which calculates the differences between two multispectral N-band images from the same scene acquired at different times, is used to identify no-change pixels (NCPs) of the scene through one image pair. The NCPs from IR-MAD using the long-term data of FY-3 visible infrared radiometer (VIRR) and aqua Moderate Resolution Imaging Spectroradiometer (MODIS) were aggregated into the contiguously stable sites. The traditional spatial uniformity and temporal stability from MODIS surface products were used to select the potential PICS. By combining the results of both methods, over thirty PICSs with a wider brightness range of the scene types were selected. To confirm and characterize these PICSs over Northwest China, Landsat operational land imager (OLI) high-spatial-resolution images were used to check the spatial uniformity of the selected site to determine the specific location and the size of these sites. Additionally, the surface spectral reflectance and bidirectional reflectance distribution function (BRDF) were obtained from the field campaign at Chaidamu Basin, 2018. To demonstrate the practical utilization and usability of these PICSs, they were employed in the multi-site top of atmosphere (TOA) reflectance simulation to validate the operational calibration performance of Aqua/MODIS and FY-3D/MERSI-II (Medium Resolution Spectral Imager II). The simulation results showed good consistency compared with the observations from both MODIS and MERSI-II, with a relative bias and root mean square error (RMSE) of <5% and <0.05%, respectively. These sites provide prospects for multi-site vicarious calibrations of solar reflective bands, which may help to evaluate or characterize instrumental nonlinear responses using a wider signal dynamic from the sites in different seasons. [ABSTRACT FROM AUTHOR]

Total precipitable water (TPW) is an important key factor in the global water cycle and climate change. The knowledge of TPW characteristics at spatial and temporal scales could help us to better understand our changing environment. Currently, many algorithms are available to retrieve TPW from optical and microwave sensors. There are still no available TPW data over land from FY-3D MWRI, which was launched by China in 2017. However, the TPW product over land is a key element for the retrieval of many ecological environment parameters. In this paper, an improved algorithm was developed to retrieve TPW over land from the brightness temperature of FY-3D MWRI. The major improvement is that surface emissivity, which is a key parameter in the retrieval of TPW in all-weather conditions, was developed and based on an improved algorithm according to the characteristics of FY-3D MWRI. The improvement includes two aspects, one is selection of appropriate ancillary data in estimating surface emissivity parameter Δε18.7/Δε23.8 in clear sky conditions, and the other is an improvement of the Δε18.7/Δε23.8 estimation function in cloudy conditions according to the band configuration of FY-3D MWRI. Finally, TPW retrieved was validated using TPW observation from the SuomiNet GPS and global distributed Radiosonde Observations (RAOB) networks. According to the validation, TPW retrieved using observations from FY-3D MWRI and ancillary data from Aqua MODIS had the best quality. The root mean square error (RMSE) and correlation coefficient between the retrieved TPW and observed TPW from RAOB were 5.47 and 0.94 mm, respectively. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R2 = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R2 = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature. [ABSTRACT FROM AUTHOR]

The Chang'E-2 digital elevation model (DEM, named CE2TMap2015), which is the highest resolution DEM product in China, was released with global coverage of the lunar surface. A detailed geometric quality assessment of the product is desirable for wider applications by users. A novel procedure for evaluating DEM geometric quality, which investigates both the global geometric uncertainty and local gross errors, is proposed in this paper. First, the DEM was divided into regular blocks and matched with the reference DEM. Then, the characteristics of errors were investigated using the statistical information of the matched tie points. Next, the local outlier factor (LOF) algorithm was performed to locate the gross errors. In our experiment, CE2TMap2015 was evaluated using the proposed method, with SLDEM2015 as the reference DEM. The results show that there were widespread geometric inconsistencies with an area-weighted average of 183.1 m horizontally (with the standard deviation being 101.2 m) and 2.3 m vertically (with the standard deviation being 15.4 m). Gross errors were detected automatically and were excluded in the statistical analysis. The periodic errors were extracted in the frequency domain using a Fourier transform. Our research results provide instructional information for the utilization of CE2TMap2015 by world-wide users. The proposed method can be used in the assessment of other planetary DEMs. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems-Reflectometry (GNSS-R) has shown unprecedented advantages to sense Soil Moisture Content (SMC) with high spatial and temporal coverage, low cost, and under all-weather conditions. However, implementing an appropriated physical basis to estimate SMC from GNSS-R is still a challenge, while previous solutions were only based on direct comparisons, statistical regressions, or time-series analyses between GNSS-R observables and external SMC products. In this paper, we attempt to retrieve SMC from GNSS-R by estimating the dielectric permittivity from Fresnel reflection coefficients. We employ Cyclone GNSS (CYGNSS) data and effectively account for the effects of bare soil roughness (BSR) and vegetation optical depth by employing ICESat-2 (Ice, Cloud, and land Elevation Satellites 2) and/or SMAP (Soil Moisture Active Passive) products. The tests carried out with ICESat-2 BSR data have shown the high sensitivity in SMC retrieval to high BSR values, due to the high sensitivity of ICESat-2 to land surface microrelief. Our GNSS-R SMC estimates are validated by SMAP SMC products and the results provide an R-square of 0.6, Root Mean Squared Error (RMSE) of 0.05, and a zero p-value, for the 4568 test points evaluated at the eastern region of China during April 2019. The achieved results demonstrate the optimal capability and potential of this new method for converting reflectivity measurements from GNSS-R into Land Surface SMC estimates. [ABSTRACT FROM AUTHOR]

Salt-affected soil is a prominent ecological and environmental problem in dry farming areas throughout the world. China has nearly 9.9 million km 2 of salt-affected land. The identification, monitoring, and utilization of soil salinization have become important research topics for promoting sustainable progress. In this paper, using field-measured spectral data and soil salinity parameter data, through analysis and transformation of spectral data, five machine learning models, namely, random forest regression (RFR), support vector regression (SVR), gradient-boosted regression tree (GBRT), multilayer perceptron regression (MLPR), and least angle regression (Lars) are compared. The following performance measures of each model were evaluated: the collinear problems, handling data noise, stability, and the accuracy. In terms of these four aspects, the performance of each model on estimating soil salinity is evaluated. The results demonstrate that among the five models, RFR has the best performance in dealing with collinearity, RFR and MLPR have the best performance in dealing with data noise, and the SVR model is the most stable. The Lars model has the highest accuracy, with a determination coefficient ( R 2 ) of 0.87, ratio of performance to deviation (RPD) of 2.67, root mean square error (RMSE) of 0.18, and mean absolute percentage error (MAPE) of 0.11. Then, the comprehensive comparison and analysis of the five models are carried out, and it is found that the comprehensive performance of RFR model is the best; hence, this method is most suitable for estimating soil salinity using hyperspectral data. This study can provide a reference for the selection of regression methods in subsequent studies on estimating soil salinity using hyperspectral data. [ABSTRACT FROM AUTHOR]

The Luojia1-01 satellite provides high-resolution, high-sensitivity nighttime light data at a resolution of 130 m. To effectively use the Luojia1-01 nighttime light data for global applications, the problems of relative and absolute positioning accuracy should be solved. This paper proposes a high accuracy regional geometric processing method of nighttime light imagery. We utilized a nighttime light image matching algorithm to obtain tie points, which are used in the planar block adjustment with ground control points. Then, orthorectification of all images is implemented. Finally, we obtain the nighttime light map of China by mosaicking all the nighttime light orthoimages. According to the experimental results for 275 Luojia1-01 images, the root mean square error of the tie points is 0.983 pixels and the root mean square error of independent checkpoints is 195.491 m (less than 1.5 pixels) after the planar block adjustment. The experimental results prove the validity and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

The accurate prediction of surface solar irradiance is of great significance for the generation of photovoltaic power. Surface solar irradiance is affected by many random mutation factors, which means that there are great challenges faced in short-term prediction. In Northwest China, there are abundant solar energy resources and large desert areas, which have broad prospects for the development of photovoltaic (PV) systems. For the desert areas in Northwest China, where meteorological stations are scarce, satellite remote sensing data are extremely precious exploration data. In this paper, we present a model using FY-4A satellite images to forecast (up to 15–180 min ahead) global horizontal solar irradiance (GHI), at a 15 min temporal resolution in desert areas under different sky conditions, and compare it with the persistence model (SP). The spatial resolution of the FY-4A satellite images we used was 1 km × 1 km. Particle image velocimetry (PIV) was used to derive the cloud motion vector (CMV) field from the satellite cloud images. The accuracy of the forecast model was evaluated by the ground observed GHI data. The results showed that the normalized root mean square error (nRMSE) ranged from 18.9% to 21.6% and the normalized mean bias error (nMBE) ranged from 3.2% to 4.9% for time horizons from 15 to 180 min under all sky conditions. Compared with the SP model, the nRMSE value was reduced by about 6%, 8%, and 14% with the time horizons of 60, 120, and 180 min, respectively. [ABSTRACT FROM AUTHOR]