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Continuously monitoring surface ozone (O3) spatial distribution and forecasting its variations are beneficial to improving air quality and ensuring public health in China, although achieving this goal faces challenges from currently available observations and retrieval techniques. Hence, we introduce a coupled surface O3 estimation framework (LESO) to address these challenges by integrating ground-level observing networks and satellite remote sensing. LESO features easy-to-use deep learning algorithms, independence on chemical transportation models (CTMs), and consistent performance using data from different satellites. LESO includes a deep forest 21 (DF21) model to interpolate O3 concentration by learning spatial patterns and a long short-term memory (LSTM) model to forecast O3 concentration by learning data from the past. We used sites of city-level in situ networks as the control sites to manifest short-distance O3 transportation. Satellite-based observations of O3 precursor indicators were incorporated to capture O3 photochemical reactions. DF21 explained a larger fraction of O3 variability (90%) with a mean bias error (MBE) of smaller than $1~\mu \text {g/m}^{3}$. We also investigated the impact of the number of training sites on the DF21 performance, which suggested that five training sites could ensure a good DF21 performance for the most areas ($R^{2} > 0.85$ and bias $< 2~\mu \text {g/m}^{3}$). The forecast O3 concentration via LSTM showed a good and stable agreement ($R^{2}\approx 0.85$ and bias $< 5~\mu \text {g/m}^{3}$) with ground-based measurements for 8-, 24-, 28-, and 72-h time periods, respectively. Overall, LESO aims to bring convenient functionality and reliable surface O3 estimates for broad users. [ABSTRACT FROM AUTHOR]

Snow depth estimation with passive microwave (PM) remote sensing is challenged by spatial variations in the Earth's surface, e.g., snow metamorphism, land cover types, and topography. Thus, traditional static snow depth retrieval algorithms cannot capture snow thickness well. In this study, we present a new operational retrieval algorithm, hereafter referred to as the pixel-based method (0.25° × 0.25° grid-level), to provide more accurate and nearly real-time snow depth estimates. First, the reference snow depth was retrieved using a previously proposed model in which a microwave snow emission model was coupled with a machine learning (ML) approach. In this process, an effective grain size (effGS) value was optimized by utilizing the snow microwave emission model, and then the nonlinear relationship between snow depth and multiple predictive variables, e.g., effGS, longitude, elevation, and brightness temperature (Tb) gradients, was established with the ML technique to retrieve reference snow depth data. To select a robust and well-performing ML approach, we compared the performance of widely used support vector regression (SVR), artificial neural network (ANN) and random forest (RF) algorithms over China. The results show that the three ML models performed similarly in snow depth estimation, which was attributed to the inclusion of effGS in the training samples. In this study, the RF model was used to retrieve the snow depth reference dataset due to its slightly stronger robustness according to our comparison of results. Second, the pixel-based algorithm was built based on the retrieved reference snow depth dataset and satellite Tb observations (18.7 GHz and 36.5 GHz) from Advanced Microwave Scanning Radiometer 2 (AMSR2) during the 2012–2020 period. For the pixel-based algorithm, the fitting coefficients were achieved dynamically pixel by pixel, making it superior to the traditional static methods. Third, the built pixel-based algorithm was verified using ground-based observations and was compared to the AMSR2, GlobSnow-v3.0, and ERA5-land products during the 2012–2020 period. The pixel-based algorithm exhibited an overall unbiased root mean square error (unRMSE) and R2 of 5.8 cm and 0.65, respectively, outperforming GlobSnow-v3.0, with unRMSE and R2 values of 9.2 cm and 0.22, AMSR2, with unRMSE and R2 values of 18.5 cm and 0.13, and ERA5-land, with unRMSE and R2 values of 10.5 cm and 0.33, respectively. However, the pixel-based algorithm estimates were still challenged by the complex terrain, e.g., the unRMSE was up to 17.4 cm near the Tien Shan Mountains. The proposed pixel-based algorithm in this study is a simple and operational method that can retrieve accurate snow depths based solely on spaceborne PM data in comparatively flat areas. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) is crucial for understanding the hydrological process of our earth surface. The passive microwave (PM) technique has long been the primary tool for estimating global SSM from the view of satellites, while the coarse resolution (usually >∼10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to the public that meet the qualification of 1 km resolution and daily revisit cycles under all-weather conditions. In this study, we developed one such SSM product in China with all these characteristics. The product was generated through downscaling the AMSR-E/AMSR-2-based (Advance Microwave Scanning Radiometer of the Earth Observing System and its successor) SSM at 36 km, covering all on-orbit times of the two radiometers during 2003–2019. MODIS optical reflectance data and daily thermal-infrared land surface temperature (LST) that had been gap-filled for cloudy conditions were the primary data inputs of the downscaling model so that the "all-weather" quality was achieved for the 1 km SSM. Daily images from this developed SSM product have quasi-complete coverage over the country during April–September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations through a specifically developed sub-model for filling the gap between seams of neighboring PM swaths during the downscaling procedure. The product compares well against in situ soil moisture measurements from 2000+ meteorological stations, indicated by station averages of the unbiased root mean square difference (RMSD) ranging from 0.052 to 0.059 vol vol -1. Moreover, the evaluation results also show that the developed product outperforms the SMAP (Soil Moisture Active Passive) and Sentinel (active–passive microwave) combined SSM product at 1 km, with a correlation coefficient of 0.55 achieved against that of 0.40 for the latter product. This indicates the new product has great potential to be used by the hydrological community, by the agricultural industry, and for water resource and environment management. The new product is available for download at 10.11888/Hydro.tpdc.271762 (Song and Zhang, 2021b). [ABSTRACT FROM AUTHOR]

Near-surface air temperature (Ta) is an important physical parameter that reflects climate change. Many methods are used to obtain the daily maximum (Tmax⁡), minimum (Tmin⁡), and average (Tavg) temperature, but are affected by multiple factors. To obtain daily Ta data (Tmax⁡ , Tmin⁡ , and Tavg) with high spatio-temporal resolution in China, we fully analyzed the advantages and disadvantages of various existing data. Different Ta reconstruction models were constructed for different weather conditions, and the data accuracy was improved by building correction equations for different regions. Finally, a dataset of daily temperature (Tmax⁡ , Tmin⁡ , and Tavg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1 ∘. For Tmax⁡ , validation using in situ data shows that the root mean square error (RMSE) ranges from 0.86 to 1.78 ∘ , the mean absolute error (MAE) varies from 0.63 to 1.40 ∘ , and the Pearson coefficient (R2) ranges from 0.96 to 0.99. For Tmin⁡ , the RMSE ranges from 0.78 to 2.09 ∘ , the MAE varies from 0.58 to 1.61 ∘ , and the R2 ranges from 0.95 to 0.99. For Tavg , the RMSE ranges from 0.35 to 1.00 ∘ , the MAE varies from 0.27 to 0.68 ∘ , and the R2 ranges from 0.99 to 1.00. Furthermore, various evaluation indicators were used to analyze the temporal and spatial variation trends of Ta , and the Tavg increase was more than 0.03 ∘ C yr -1 , which is consistent with the general global warming trend. In summary, this dataset has high spatial resolution and high accuracy, which compensates for the temperature values (Tmax⁡ , Tmin⁡ , and Tavg) previously missing at high spatial resolution and provides key parameters for the study of climate change, especially high-temperature drought and low-temperature chilling damage. The dataset is publicly available at 10.5281/zenodo.5502275 (Fang et al., 2021a). [ABSTRACT FROM AUTHOR]

Ta (Near-surface air temperature) is an important physical parameter that reflects climate change. Although there are currently many methods to obtain the daily maximum (Tmax), minimum (Tmin), and average (Tavg) temperature (meteorological stations, remote sensing, and reanalysis data), these methods are affected by many factors. In order to obtain daily Ta data (Tmax, Tmin, and Tavg) with high spatial and temporal resolution in China, we fully analyzed the advantages and disadvantages of various existing data (reanalysis, remote sensing, and in situ data). Different Ta reconstruction models are constructed for different weather conditions, and we further improve data accuracy through building correction equations for different regions. Finally, a dataset of daily temperature (Tmax, Tmin, and Tavg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1°. For Tmax, validation using in situ data shows that the root mean square error (RMSE) ranges from 0.86 °C to 1.78 °C, the mean absolute error (MAE) varies from 0.63 °C to 1.40 °C, and the Pearson coefficient (R2) ranges from 0.96 to 0.99. For Tmin, RMSE ranges from 0.78 °C to 2.09 °C, the MAE varies from 0.58 °C to 1.61 °C, and the R2 ranges from 0.95 to 0.99. For Tavg, RMSE ranges from 0.35 °C to 1.00 °C, the MAE varies from 0.27 °C to 0.68 °C, and the R2 ranges from 0.99 to 1.00. Furthermore, a variety of evaluation indicators were used to analyze the temporal and spatial variation trends of Ta, and the Tavg increase was more than 0.0 °C/a, which is consistent with the general global warming trend. In conclusion, this dataset had a high spatial resolution and reliable accuracy, which makes up for the previous missing temperature value (Tmax, Tmin, and Tavg) at high spatial resolution. This dataset also provides key parameters for the study of climate change, especially high-temperature drought and low-temperature chilling damage, which is publicly available with the following DOI: https://doi.org/10.5281/zenodo.5502275 (Fang et al., 2021a). [ABSTRACT FROM AUTHOR]

L-band passive microwave remote sensing is currently the optimum tool for the large-scale soil moisture mapping. However, due to the coarse spatial resolution of passive microwave remote sensing, the existence and dynamic changes of water bodies in the field of view would affect the performance of soil moisture retrieval. The Qinghai-Tibet Plateau (QTP) is a densely populated area of lakes in China, and the correction of water contamination effect is much more challenging. In this letter, we proposed a simulation-based approach which utilizes a radiative transfer model to simulate the brightness temperature (TB) of water bodies to correct the Soil Moisture Active Passive (SMAP) data over the QTP, taking into account of wind speed, surface temperature and salinity of lakes. The simulated data and water area fraction obtained from a dynamic map of water bodies were applied to remove the effect of water contamination. Results showed that the relatively low SMAP TBs surrounding both small and large lakes could be eliminated and do not have correlation with the water area fraction after using the proposed approach. Validation results in the Naqu network demonstrated that the simulation-based approach can effectively remove the water contamination from SMAP data with improved soil moisture retrievals. [ABSTRACT FROM AUTHOR]

Variable atmospheric conditions are typically ignored in the retrieval of geophysical parameters of the Earth’s surface when using spaceborne passive microwave observations. However, high frequencies, for example, 91.7 GHz, are sensitive to variable atmospheric absorption, even in winter’s dry conditions. In this article, the influence of variable atmospheric absorption on snow cover extent (SCE) mapping was quantitatively investigated. A physical method was derived to enable atmospheric correction for variable atmospheric conditions. The total column precipitable water vapor (TPWV) from Moderate Resolution Imaging Spectroradiometer (MODIS) was parametrized into transmittances in this correction method. The corrected brightness temperature at 19 and 91.7 GHz from the Special Sensor Microwave Imager Sounder (SSMIS) was applied to the threshold algorithm for snow mapping over China. Compared with the Interactive Multisensor Snow and Ice Mapping System (IMS) data in winter from 2012 to 2013, for Qinghai–Tibet plateau (QTP), a significant improvement after correction was obtained from February to March over ephemeral and shallow snow, where the largest daily improvement of accuracy is up to 20%. The accuracy (incl. precision, recall, and F1 index) improved on average is from 0.77 (0.60, 0.68, and 0.63) to 0.79 (0.69, 0.7, and 0.68) over the full winter time from December to March. Over forest-rich Northeast China, where snow in winter is thicker, small improvement was observed at the onset of the snow season and over snow margin area. It was evidenced that high frequency is a promising way of snow cover mapping with the proposed atmospheric correction method. [ABSTRACT FROM AUTHOR]

Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture across large areas, but the coarse spatial resolution of microwave data imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05 ∘ , monthly) for China from 2002 to 2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products – including AMSR-E and AMSR2 (Advanced Microwave Scanning Radiometer for Earth Observing System) JAXA (Japan Aerospace Exploration Agency) Level 3 products and SMOS-IC (Soil Moisture and Ocean Salinity designed by the Institut National de la Recherche Agronomique, INRA, and Centre d'Etudes Spatiales de la BIOsphère, CESBIO) products – calibrated with a consistent model in combination with ground observation data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates that the accuracy of the new dataset is satisfactory (bias: - 0.057, - 0.063 and - 0.027 m3m-3 ; unbiased root mean square error (ubRMSE): 0.056, 0.036 and 0.048; correlation coefficient (R): 0.84, 0.85 and 0.89 on monthly, seasonal and annual scales, respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past 17 years, China's soil moisture has shown cyclical fluctuations and a slight downward trend and can be summarized as wet in the south and dry in the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in Zenodo at 10.5281/zenodo.4738556 (Meng et al., 2021a). [ABSTRACT FROM AUTHOR]

Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture over large areas, but the coarse spatial resolution of microwave data imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05°, monthly) for China from 2002-2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products (including the AMSR-E/2 Level 3 products and the SMOS-INRA-CESBIO (SMOS-IC) products) calibrated with a consistent model in combination with ground observation data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates that the accuracy of the new dataset is satisfactory (bias: -0.024, -0.030 and -0.016 m3/m3, unbiased root mean square error (ubRMSE): 0.051, 0.048 and 0.042, correlation coefficient (R): 0.82, 0.88, and 0.90 on monthly, seasonal and annual scales, respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past 17 years, China's soil moisture has shown cyclical fluctuations and a downward trend (slope = -0.167, R = 0.750) and can be summarized as wet in the south and dry in the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in the Zenodo at http://doi.org/10.5281/zenodo.4049958 (Meng et al., 2020). [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60 % of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 ∘ C, the mean absolute error (MAE) varies from 1.23 to 1.37 ∘ C and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1 K (R>0.71 , P<0.05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. The data are available through Zenodo at 10.5281/zenodo.3528024 (Zhao et al., 2019). [ABSTRACT FROM AUTHOR]

This study examines the progress made by reanalyses and satellite products in the estimation of cloud cover over China: the ECMWF reanalyses ERA5 and ERA-Interim, geostationary satellite observation Himawari-8 (H8) and the International Satellite Cloud Climatology Project H-series (ISCCP) product. There is great similarity in spatial patterns of cloud cover in reanalyses and satellite observations, especially between ERA5 and H8. Distinct characteristics of the seasonal evolution of cloud cover are shown over the Tibetan Plateau (TP), the southeast (SE) and northeast (NE) of China. Differences in magnitudes of cloud cover exist. Overestimations are about 10% for reanalyses and about 20% for ISCCP in compared with certain cloud cover in H8. When probable cloud (about 10%) in H8 is included in the estimation, biases reduce the most in ERA5. The cloud hit rate (CHR) and false alarm rate (FAR) in against H8 and ISCCP reveal that simulated clouds in ERA5 have been improved especially over eastern China, but with limited improvement over TP in compared with ERA-Interim. Diurnal variations of cloud cover are characterized by increases during daytime over those three regions. Amplifications of diurnal variation vary over different regions and months. Satellite observations and ERA5 indicate distinguished diurnal cycle of cloud cover over TP, while further investigation based on ERA5 reveals coherent diurnal cycle in meteorological environment. Long-term changes of cloud cover highlight decreasing trends over TP and particular during March in past decades based on ISCCP and ERA5, which require further investigation in future. [ABSTRACT FROM AUTHOR]

An ensemble method was used to combine three surface soil moisture products, retrieved from passive microwave remote sensing data, to reconstruct a monthly soil moisture data set for China between 2003 and 2010. Using the ensemble data set, the temporal and spatial variations of surface soil moisture were analyzed. The major findings were: (1) The ensemble data set was able to provide more realistic soil moisture information than individual remote sensing products; (2) during the study period, the soil moisture increased in semiarid regions and decreased in arid regions with anoverall drying trend for the whole country; (3) the soil moisture variation trends derived from the three retrieval products and the ensemble data differ from each other but all data sets show the dominant drying trend for the summer, and that most of the drying regions were in major agricultural areas; (4) compared with the precipitation trends derived from Global Precipitation Climatology Project data, it is speculated that climate change is a possible cause for the drying trend in semiarid regions and the wetting trend in arid regions; and (5) combining soil moisture trends with land surface temperature trends derived from Moderate Resolution Imaging Spectroradiomete, the study domain was divided into four categories. Regions with drying and warming trends cover 33.2%, the regions with drying and cooling trends cover 27.4%, the regions with wetting and warming trends cover 21.1% and the regions with wetting and cooling trends cover 18.1%. The first two categories primarily cover the major grain producing areas, while the third category primarily covers nonarable areas such as Northwest China and Tibet. This implies that the moisture and heat variation trends in China are unfavorable to sustainable development and ecology conservation. [ABSTRACT FROM AUTHOR]

The Rümker region is located in the northern Oceanus Procellarum, which has been selected as the landing and sampling region for China's Chang'e-5 (CE-5) mission. The thermophysical features of the mare units are studied in detail using the brightness temperature (TB) maps (TB, normalized TB, TB difference) derived from the CE-2 microwave radiometer data. The previously interpreted geological boundaries of the Rümker region are revisited in this study according to their TB behaviors: IR1, IR2, and IR3 Rümker plateau units are combined into one single unit (IR); and a hidden unit is found on the Mons Rümker; Mare basaltic units Im1 and Em1 are combined into Em1; and Em2 is more likely the extending of Im2. Each of the previous proposed landing sites and their scientific value are summarized and reevaluated. Based on this, four landing sites are recommended in order to maximize the scientific outcome of the CE-5 mission. We suggest that the Eratosthenian-aged Em4 and Em1 units as the top priority landing site for the CE-5 mission; the age-dating results will provide important clues concerning the thermal evolution of the Moon. [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]

The Soil Moisture Experiment in the Luan River (SMELR) was conducted from 2017 to 2018 in the semi-arid Luan River watershed located in the North of China. One of the objectives of SMELR is to serve as an assessment tool and demonstration for a new Terrestrial Water Resources Satellite (TWRS) concept with one-dimensional synthetic aperture microwave techniques, for which soil moisture retrieval under variable satellite observing configurations (mainly in terms of incidence angels) is the greatest challenge. This proposed mission is targeted to provide continuity for the current satellite L-band microwave observations, and further improve the accuracy and spatial resolution of soil moisture mapping through the synergistic use of active, passive and optical remote sensing data. Multi-resolution, multi-angle and multi-spectral airborne data were obtained four times over a 70 km by 12 km area in the Shandian River basin, and one time over a 165 km by 5 km area that includes the Xiaoluan River basin. The near surface soil moisture (0 cm–5 cm) was measured extensively on the ground in fifty 1 km by 2 km quadrats (targeted to compare with the airborne radiometer), and two hundred and fifty 200 m by 200 m quadrats corresponding to radar observations. Two networks were established for continuous measurement of the soil moisture and temperature profile (3 cm, 5 cm, 10 cm, 20 cm, 50 cm) and precipitation in the Shandian and Xiaoluan River basin, respectively. Supporting ground measurements also included ground temperature, vegetation water content, surface roughness, continuous measurement of microwave emission and backscatter at a pasture site, reflectance of various land cover types, evapotranspiration and aerosol observations. Preliminary results within the experimental area indicate that (1) the near surface soil moisture spatial variability at a 200 m scale was up to ~0.1 cm3/cm3 at an intermediate value of ~0.35 cm3/cm3. (2) The difference of soil and vegetation temperature in grass and croplands reach its maximum of 11 K around solar noon time, and the soil temperature gradient is largest at around 15 P.M. (3) Both the airborne and ground measurements cover a wide range of conditions. The L-band active and passive observations exhibit a large variation of ~30 dB and ~80 K, respectively, corresponding to soil moisture range from 0.1 cm3/cm3 to 0.5 cm3/cm3. The sensitivity of both active and passive data to soil moisture is compared at corresponding spatial resolutions and show high information complementarity for better accuracy and resolution soil moisture retrieval. • First airborne optical and active-passive microwave experiment conducted in China • Demonstration of the active-passive microwave synergy at varying incidence angles • Addressing prelaunch science questions of the terrestrial water resources mission [ABSTRACT FROM AUTHOR]

The Advanced Technology Microwave Sounder (ATMS) mounted on the Suomi National Polar-Orbiting Partnership (NPP) satellite can provide both temperature and humidity information for a weather prediction model. Based on the rapid-refresh multi-scale analysis and prediction system—short-term (RMAPS-ST), we investigated the impact of ATMS radiance data assimilation on strong rainfall forecasts. Two groups of experiments were conducted to forecast heavy precipitation over North China between 18 July and 20 July 2016. The initial conditions and forecast results from the two groups of experiments have been compared and evaluated against observations. In comparison with the first group of experiments that only assimilated conventional observations, some added value can be obtained for the initial conditions of temperature, humidity, and wind fields after assimilating ATMS radiance observations in the system. For the forecast results with the assimilation of ATMS radiances, the score skills of quantitative forecast rainfall have been improved when verified against the observed rainfall. The Heidke skill score (HSS) skills of 6-h accumulated precipitation in the 24-h forecasts were overall increased, more prominently so for the heavy rainfall above 25 mm in the 0–6 h of forecasts. Assimilating ATMS radiance data reduced the false alarm ratio of quantitative precipitation forecasting in the 0–12 h of the forecast range and thus improved the threat scores for the heavy rainfall storm. Furthermore, the assimilation of ATMS radiances improved the spatial distribution of hourly rainfall forecast with observations compared with that of the first group of experiments, and the mean absolute error was reduced in the 10-h lead time of forecasts. The inclusion of ATMS radiances provided more information for the vertical structure of features in the temperature and moisture profiles, which had an indirect positive impact on the forecasts of the heavy rainfall in the RMAPS-ST system. However, the deviation in the location of the heavy rainfall center requires future work. [ABSTRACT FROM AUTHOR]

A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface temperature. The twelve V/H channel combinations (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) resulted in the most stable and accurate CNN retrieval model. Vertical polarizations performed better than horizontal polarizations; however, because CNNs rely heavily on large amounts of data, the combination of vertical and horizontal polarizations performed better than a single polarization. The retrievals in different regions indicated that the CNN accuracy was highest over large bare land areas. A comparison of the retrieval results with ground measurement data from meteorological stations yielded R2 = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K, which indicated that the accuracy of the CNN LST retrieval algorithm was high and the retrieval results can be applied to long-term LST sequence analysis in China. [ABSTRACT FROM AUTHOR]

Abstract: Snow cover plays an important role in hydrological processes and global climate change research. Geostationary satellites with high temporal resolution provide multiple observations in one day, which highlights their potential for monitoring real-time snow-cover information. In this paper, data from the Chinese meteorological satellites Fengyun-2D (FY-2D), Fengyun-2E (FY-2E) and Fengyun-3B (FY-3B) was used for snow-cover mapping over China. A new method of detecting snow-cover information is proposed, that combines the Visible and Infrared Spin Scan-Radiometer (VISSR) on board the geostationary satellites FY-2D and FY-2E and the Microwave Radiation Imager (MWRI) on board the polar orbiting satellite FY-3B. The snow cover estimated from Fengyun satellites was compared by the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-cover products (MOD10A1 and MYD10A1), and Interactive Multisensor Snow and Ice Mapping System (IMS) snow-cover products. The Fengyun satellite snow-cover images and IMS snow-cover products were validated with meteorological station observations for the 2010–2011 and 2011–2012 winter seasons. The influence of elevation and land-cover types on the accuracy of snow retrievals was also analyzed. The results showed that the combined use of FY-2D and FY-2E VISSR data reduced cloud obscuration by 30.47% compared to the MODIS products. The validation demonstrated that the accuracy of the final multi-sensor snow-cover images was 91.28%, which is similar to that for IMS snow-cover products. This work indicates that combined data from geostationary satellites and passive microwave remote sensing monitored snow cover over China to a high level of accuracy. [Copyright &y& Elsevier]

Total precipitable water (TPW) is a key parameter in the study of water cycle and climate change. High-resolution TPW is critical for understanding regional water cycle processes. Besides, high-resolution TPW is also an important input parameter for atmospheric correction which helps to eliminate atmospheric impact and improve surface parameter retrieval accuracy. However, obtaining high-resolution TPW in all-weather condition from a single satellite sensor still has many limitations and challenges. This paper proposed a new fusion method to obtain all-weather TPW with a high spatial resolution of 0.05° × 0.05° by taking advantage of various water vapor related variables derived from microwave and near-infrared (NIR) remote sensing. At the same time, the contribution of variables in the fusion algorithm was analyzed to find the best fusion scheme. There are two major improvements in the newly developed fusion method. First, a new water vapor sensitive parameter – ratio of brightness temperature polarization difference between frequencies 89 and 36.5 GHz (∆ Tb 89 / ∆ Tb 36.5) was first introduced in the fusion algorithm to provide more high spatial resolution TPW information in both clear and cloudy sky conditions, and the accuracy of the fused TPW was significantly improved when the parameter ∆ Tb 89 / ∆ Tb 36.5 was introduced. Second, a two-steps fusion method was proposed to produce a fused high spatial resolution TPW by fully taking advantage of high quality TPW information from AMSR2 TPW and ∆ Tb 89 / ∆ Tb 36.5 in cloudy condition in the first step, and high quality TPW from MODIS in clear sky condition in the second step. The TPW derived from global navigation satellite system (GNSS) stations of Crustal Movement Observation Network of China (CMONOC) was used to validate the fused TPW and original TPW from MYD05 and AMSR2 at spatial resolution of 0.05° × 0.05°. According to the validation, the correlation coefficient, root mean square error, and relative root mean square error between the fused TPW and the GNSS TPW are 0.96, 4.23 mm and 23.67% respectively. The root mean square errors were improved by 19.96% and 27.32%, respectively, compared to the original MYD05 TPW and AMSR2 TPW. • A new two-step TPW fusion method was proposed based on MW and NIR remote sensing. • The influence of ∆Tb 89 /∆Tb 36.5 on fused TPW was analyzed in all weather conditions. • The fused TPW had a significant improvement compared to the original TPW datasets. [ABSTRACT FROM AUTHOR]

Passive microwave remote sensing of soil moisture is an underdetermined problem, as observed microwave emission from the landscape is affected by a variety of unknown surface parameters. Increasing observation information is an effective means to make retrievals more robust. In this study, a multi-temporal and multi-angular (MTMA) approach is proposed using SMOS (Soil Moisture and Ocean Salinity) satellite L-band data for retrieving vegetation optical depth (VOD p , p indicates the polarization with H for horizontal and V for vertical)), effective scattering albedo (ω p eff ), soil surface roughness (Z p s ), and soil moisture (SM p). The advantage of the MTMA approach is that it does not need auxiliary data as inputs or constraints. SMOS polarization-dependent VOD are produced and compared at a global scale for the first time, and it is found that the polarization difference of vegetation effects should not be ignored in the SM retrieval algorithm. The MTMA VOD retrievals are found to have a reasonable global spatial distribution, which is generally consistent with the VOD retrievals obtained from the SMOS Level 3 (SMOS-L3) and SMOS-IC Version 2 (V2) (referred to as SMOS-IC), except for showing relatively lower values over densely vegetated areas compared with the other two SMOS products. The spatial distribution of retrieved ω p eff generally shows a dependence on both VOD and land cover types. In addition, the values of MTMA - ω V eff are higher than that of MTMA - ω H eff , indicating stronger microwave scattering of V-pol in the vegetation layer than that of H-pol. The retrieved surface roughness parameter (Z p s ) ranges from 0.04 to 0.22 cm, and its spatial distribution is partially different from the existing roughness products/auxiliary data from SMOS and SMAP. The retrieved MTMA SM shows generally high correlations with in-situ measurements (11 dense observation networks) with overall correlation coefficients of > 0.75. The overall ubRMSE of MTMA - SM H and MTMA - SM V are < 0.055 m3/m3 and lower than that of SMOS-IC and SMOS-L3 products. SMOS-IC generally presents higher correlation coefficients compared to MTMA in most sites outside China; in China, RFI filtering is crucial and makes it very difficult when comparing algorithms based on different brightness temperature products. The number of effective retrievals of MTMA - SM H and MTMA- SM V ranges from 1409 to 1640 and 1104 to 1603 respectively, which is more than that from SMOS-IC (from 236 to 1358) over the selected 11 networks. Therefore, it is concluded that by incorporating multi-temporal SMOS data, the proposed method of MTMA can be used to systematically retrieve SM, VOD and additional surface parameters (effective scattering albedo and surface roughness) with comparable or better performance of SM than that of SMOS-IC and SMOS-L3. Moreover, this paper for the first time produced a polarization-dependent SMOS VOD product at a global scale. • A multi-temporal and multi-angular retrieval algorithm is developed for SMOS. • Vegetation optical depth and single scattering albedo are simultaneously retrieved. • The first retrieval of vegetation optical depth at V/H polarization, respectively. • Soil moisture and surface roughness are simultaneously retrieved. • The number of effective soil moisture retrievals is significantly increased. [ABSTRACT FROM AUTHOR]

A new soil moisture and soil temperature wireless sensor network (the SMN-SDR) consisting of 34 sites was established within the Shandian River Basin in 2018, located in a semi-arid area of northern China. In this study, in situ measurements of the SMN-SDR were used to evaluate 24 different soil moisture datasets grouped according to three categories: (1) single-sensor satellite-based products, (2) multi-sensor merged products, and (3) model-based products. Triple collocation analysis (TCA) was applied to all possible triplets to verify the reliability and robustness of the results. Impacts of different factors on the accuracy of soil moisture products were also investigated, including local acquisition time, physical surface temperature, and vegetation optical depth (VOD). The results reveal that the latest Climate Change Initiative (CCI) -combined product (v06.1, merging extra low-frequency passive microwave data) had the best agreement with in situ measurements from the SMN-SDR, with the lowest ubRMSE (< 0.04 m3/m3) and highest R (> 0.6). Among all single-sensor retrieved soil moisture products, the Soil Moisture Active Passive (SMAP) products performed best in terms of R (> 0.6) and ubRMSE (close to 0.04 m3/m3), with the SMAP-MDCA (Modified Dual Channel Algorithm) being slightly better than the baseline SCA-V (Single Channel Algorithm-Vertical polarization). Importantly, the newly developed SMAP-IB product, which does not use auxiliary data, delivered the best bias statistics and higher VOD values compared with the drier SMAP retrievals, suggesting that the low VOD values (underestimated vegetation effects) may be the major factor causing the dry bias of SMAP products in this study area. It was also found that TCA may systematically overestimate the correlation and underestimate the ubRMSE of soil moisture products as compared with ground-based metrics. TCA-based metrics may vary considerably when using different triplets, due to the TCA assumptions being violated even with the most conservative triplets (in this case an active product, a passive product, and a model-based product). Redundant TCA-based metrics from multiple independent triplets could be averaged to increase the accuracy of final TCA estimates. This study is the first to use in situ measurements from the SMN-SDR to conduct a comprehensive evaluation of commonly used, multi-source soil moisture products. These results are expected to further promote the improvement of satellite- and model-based soil moisture products. • First use of a new ground network to assess different soil moisture products. • There is an obvious deviation between TCA- and ground-based metrics. • SMAP-MDCA retrieval is more accurate than SCA-V in SMN-SDR. • Underestimation of the vegetation effect is the main factor of SMAP underestimation in SMN-SDR. • First evaluation of the newly developed SMAP-IB product. [ABSTRACT FROM AUTHOR]

• The ANN algorithm model can quickly and accurately calculate the hourly SSR by directly connect the satellite data and in-situ SSR measurements in a simple way rather than the complex scheme based on the lookup table estimation approach. • The ANN model was applied to Himawari-8 geostationary satellite data and the estimated SSR show a good agreement with in-situ observation. • We analyze the spatio-temporal characteristics of SSR in China on the hourly, daily and monthly scale in 2016. The previous studies have less to analyze the characteristics of SSR spatio-temporal at the daily scale, especially at the hourly scale. In this study, we use an artificial neural network (ANN) method to estimate the downward surface shortwave radiation (DSSR) over China from Himawari-8 geostationary satellite data. As the training data of the DSSR estimation algorithm, ground-observed DSSR (GOS) data is compiled to complete the ANN method. GOS data from 89 stations over mainland China in 2016 are divided into training, testing and validation samples with a proportion of 3:1:1, in order to perform the DSSR estimation and accuracy validation. As a result, estimated DSSR from Himawari-8 data in 2016 shows good consistency with validation samples of ground observed DSSR, holding the determination coefficient and root mean square error of 0.90 and 88.86 W m−2 for the hourly mean DSSR, and 0.96 and 24.46 W m−2 for the daily mean DSSR, respectively. To investigate the spatio-temporal variation of daily DSSR in China, we performed the first empirical orthogonal function (EOF) analysis based on the ANN-derived DSSR estimates. The spatial pattern of the first EOF mode reveals a larger DSSR variation in northeast and northwest China, in contrast to the smallest variation appeared in the southwest. The hourly ANN-derived DSSR analysis shows a peak point at noon (local time) in each region, particularly in the Tibetan Plateau. In terms of the monthly and annual DSSR spatial distribution, the regions with higher elevations, such as Northwest, Tibetan Plateau and Inner Mongolia Plateau in China, have larger DSSR than other regions. In contrast, relatively low DSSR appears in the southwest and the northeast of China through the year. [ABSTRACT FROM AUTHOR]