Spatiotemporal seamless high-resolution soil moisture data are of great value in disaster prevention and mitigation and in geoscientific research, e.g. in studies related to landslide monitoring, flash flood early warning and digital twins. Current spatiotemporally continuous soil moisture data come mainly from reanalysis data and land surface data assimilation systems, but their spatial resolution is coarse on the scale of tens of kilometers, which cannot meet the requirements. To overcome this problem, we integrated a machine learning algorithm with a dual-cycle land data assimilation system for high-resolution soil moisture estimation. A random forest was deployed to downscale coarse soil moisture data provided by passive microwave sensors such as SMAP, SMOS and AMSR2 to high resolution data. A dual-cycle land data assimilation system, previously developed by the authors and capable of simultaneously correcting errors in the soil moisture products and optimizing data assimilation system parameters, was then used to assimilate the high-resolution soil moisture data generated by the random forest. Finally, high-resolution land surface state data sets, including soil moisture in the surface layer and root zone, were estimated. The results were validated against soil moisture observations from three networks on the Tibetan Plateau. It is shown that the integrated system is capable of producing reliable soil moisture products at high resolutions, such as 5 km, 10 km, etc., with a ubRMSE less than 0.04 m3/m3.

Unmanned aerial vehicles (UAVs) can offer higher spatial resolution images compared to satellites. In this experiment, an L-band microwave radiometer named PoLRa was equipped on an UAV to detect surface soil moisture and a new soil moisture retrieval algorithm was developed for it. Compared with the algorithm provided by suppliers of PoLRa, the new algorithm can significantly improve the accuracy of soil moisture and enhance spatial details. A comparison with ground-based soil moisture measurements showed that the UAV's spatial resolution reached 10 meters with an accuracy of 0.08 m3/m3. This demonstrates significant advantages for monitoring soil moisture at the farmland scale, making it applicable to precision agriculture, flash flood warnings, and drought monitoring in the future.

Drought has a significant impact on water resources and socio-economic development, with evapotranspiration being an important component. Based on total water storage changes observed by GRACE missions and precipitation data obtained by GPM missions, this study estimates monthly anomalies of evapotranspiration through water balance method. The results were verified and analyzed in the Yangtze River Basin. The analysis reveals that during summer drought events, evapotranspiration generally decreases instead of increases, and there is a mutual relationship between evapotranspiration and hydrological and meteorological factors. This study establishes a framework for analyzing ET using GRACE data and water balance methods, providing a reference for future research.

Soil texture is a fundamental soil property and serve as a crucial input to many Land Surface Models (LSMs). However, current soil texture datasets used in LSMs are usually extrapolated from in-situ scale geological surveys, which may contain high uncertainties due to the mismatch in spatial scales. Here, we propose a method to optimize several currently existing soil texture datasets by using a long-term satellite soil moisture dataset. The optimized soil texture datasets may provide an opportunity to improve land surface simulations in LSMs.

Soil moisture drydown plays an important role in many hydrometeorological processes such as regulating surface energy budget, evapotranspiration, and infiltration. In this study, we analyzed the spatial and temporal characteristics of global soil moisture drydown using the daily-scale long-term satellite soil moisture product NNSM. We find that the time-series of τSand τLremained stable over the years. The spatial distribution of global τSand τLshows an anti-spatial correlation pattern, implying that strong land-atmosphere interaction in the short and long term occurs in different regions. τS. of NNSM is closer to the observation measurement than SMAP. The results show that NNSM can provide a long-term global reference for global soil moisture memory characterization, and for improving land surface models.

Remote sensing soil moisture (SM) products are an important source for obtaining surface soil moisture and have been widely used in large-scale hydrological, land surface, and ecological studies. The accuracy of remote sensing products determines the reliability of these applications. In this study, we evaluated three long time-series SM remote sensing products CCI, NNsm, and FY3B in China using SM measured at 732 stations. It is found that all three products tend to underestimate the surface SM. CCI has the highest correlation coefficient with ground observation, implying CCI may have an advantage in predicting long periods of drought. The differences in bias are related to the type of land cover. However, newly developed SM products need to be further validated to provide a solid reference for data selection in China.

Snow is one of the most significant essential climate variables in global climate change study, and snowmelt accounts for a large part of the streamflow in mountainous regions on the Tibetan Plateau. However, previously researchers often calculate snow water equivalent using precipitation data, which is more unreliable in solid precipitation. This study is designed to combine a hydrological model and snow remote sensing data to improve the streamflow simulation in Lhasa river basin. Firstly, this study proposes a method to produce a set of snow cover maps with high temporal and spatial resolution from multiple satellite imagery (MODIS, Landsat, and Sentinel-1. Secondly, by comparing with this snow cover product, we evaluated the capability of a Geomorphology-Based Hydrological Model (GBHM) on snow cover simulation. Third, by margining the remotely sensed snow cover maps into the GBHM, the improvement of spring streamflow simulation was validated against in situ gauge observation. Finally, the GBHM simulated snow water equivalent (SWE), which was constrained by the water balance, was employed to assess the performance of SWE products in this basin. This study provide a method to estimate streamflow reliably in snow-covered mountainous regions, as well as to evaluate SWE at basin scale.

Rapid monitoring of urban waterlogging is of great significance for disaster recovery. In order to quickly extract the inundation area and evaluate loss, based on Google Earth engine (GEE), we compared four water indexes (NDWI, MNDWI, AWEI, WI2015) and data sources (Sentinel-2 and Landsat-8) in 2021/07/20 Zhengzhou rainstorm. It is found that compared with Landsat-8, Sentinel-2 can provide richer data with higher resolution. The accuracy of monitoring disaster inundation area with MNDWI was the highest (89.7%), and especially when its threshold was around 0.15. The quantitative framework of urban rainwater and flood area in this study can provide technical support for flood disaster analysis of relevant departments.

Agricultural drought in China since the 21st century is an important but little discussed issue. In this study, we adopted NNsm, a newly developed long time series soil moisture product based on remote sensing and artificial neural networks, to identify the drought events in China from 2002 to 2019 by using the severity-area-duration (SAD) analysis. The results show there were 52 long-term drought events ($\geq 4$months) in the study period, while 83.81% of the areas in China have experienced drought. In 2015, the drought area was the largest. There was a drying trend in southern China, central Xinjiang and central Tibet, while a wetting trend in eastern Xinjiang and eastern Tibet. This study suggests the drought develop trend and reveals the areas vulnerable to drought in China, which can help with drought monitoring and mitigation, as well as scientific and technological support for early warning of agricultural drought.

Land Surface Temperature (LST) is important for diagnosing surface energy and water exchanges. Accurately predicting LST has over time been challenging for land-atmosphere parameterizations in the Land Surface Model (LSM) community mainly because of the inappropriate representations of crucial surface physical processes and properties (e.g., vegetation dynamics). Feedbacks of vegetation on land-atmosphere interactions have been recognized as influential in determining local weather and climate evolvements, however the calibrations and corrections of the vegetation parameterization in LSMs often remains model-specific and region-dependent. Here, we present two practice to improve the LST simulations in a state-of-art LSM (Noah-MP): (1) By updating Noah-MP 's vegetation parameters from the satellite observations (RS); and (2) By incorporating an empirical vegetation parameterization scheme (RL02). The overall results suggest domain-wide improvement of the simulated LST for both cases, with RMSEs upgraded by ~ 10% relative to the LST result from the default model configuration (CTL). Improved surface soil temperature simulations at three vegetation-diverse sites are also observed, showing negative relevance of the soil temperature improvements and the surface vegetation cover. Our study remarked the substantial impact of vegetation on surface energy exchanges, highlighting the potential to specify vegetation parameters on improving LST simulation. Limitations of vegetation - soil moisture coupling parameterizations in current LSMs will also be discussed in this study.

Terrestrial vegetation dynamics are important for climate variabilities but the understanding of how the vegetation dynamics respond to climate remains limited - not only because the tightly coupled climate-vegetation system makes it tricky to separate water-associated processes (e.g. precipitation and soil moisture) - but the sparse and uneven observations have made it difficult to quantify such links in a larger spatial view. Here, we relate the global vegetation - soil moisture feedbacks to their oscillation characteristics and interpret it in terms of plant-water functional traits from the satellite-based estimates of surface soil moisture (SSM) and normalized difference vegetation index (NDVI). We map the global vegetation - soil moisture oscillation time scales and investigate the spatial distribution across biomes. Our study gives a global quantification on vegetation-soil moisture dynamics, providing references for comparison related to water-and-plant functions with Earth system models.

A soil moisture retrieval algorithm using L-band radar-only observations is applied to soil moisture active and passive (SMAP) radar observations. This algorithm is based on a nonlinear relationship between L-band backscatter and soil moisture, and any ancillary vegetation or roughness information is not needed. This algorithm is based on three limiting cases and end-members: smooth bare soil, rough bare soil and maximum vegetation covered soil. Those parameters is estimated through a iterative process. Three months global soil moisture is retrieved using SMAP radar observations and this algorithm. The accuracy of soil moisture result is validated by the ground network insitu observations and the SMAP standard radar and radiometer product. The soil moisture has similar spatial pattern with that of SMAP standard product at 9 km resolution. In the future, we can estimate the soil moisture at 3 km with SMAP radar data only.

Precipitation plays an important role in land surface processes. It is the vital driver of hydrological and land surface models and key bridge between land and atmosphere in the earth system. Although satellite remote sensing can to provide precipitation estimations at regional and global scales with high spatiotemporal resolutions, it still has great uncertainties at daily scale. On the other hand, soil moisture is the bridge between precipitation and runoff. The dynamic cycle of soil moisture is direct driven by precipitation and constrained by soil texture. In this study, we proposed a novel framework of improving Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) data by including soil moisture and temperature data from ground observation. The results demonstrate that the rainfall estimation skills of TMPA are significantly improved when in situ soil moisture is used at Naqu, by reducing 44% root mean square errors (RMSE) and increasing the correlation coefficient (CC) from 0.27 to 0.71. When soil temperature data is taken into consideration, the False Alarm Ratio (FAR) is further reduced by 10%. It indicates that the proposed method has high potential to improve satellite-based rainfall products over the Tibetan Plateau.

In this study, we present a research to transfer the merits of SMAP (Soil Moisture Active Passive) to AMSR2 (Advanced Microwave Scanning Radiometer 2) with using machine learning method-artificial neural network. The surface soil moisture (SSM) products of SMAP were set as the reference data, while brightness temperature (TB) of various channels and the microwave vegetation index (MVI) obtained or derived from AMSR2 were input into an Artificial Neural Network (ANN). During training period (2015–2017), the ANN product (NNsm) can reproduce the SMAP SSM accurately, with a correlation coefficient (CC) of 0.74, Root Mean Square Error (RMSE) of 0.033 m3/m3, and Bias of −0.00008 m3/m3. It was found that machine learning method failed to provide reliable SSM over moderate vegetated areas where SMAP works well. With these trained networks, we developed a global soil moisture data set (named as NNsm) using AMSR2 TB from 2012 to 2018. Comparing to the in situ SM observations from all SCAN (Soil Climate Analysis Network) sites (named as SCANsm), NNsm has a good agreement with CC = 0.44, RMSE = 0.113 m3/m3and Bias = 0.030 m3/m3, which is much better than those of the AMSR2 SSM products from JAXA and LPRM.

The multi-model results from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) showed obvious wet biases over the Tibetan Plateau (TP) during winter. However, sparse meteorological stations and the limited capacity for getting accurate snowfall may introduce dry biases into the observation and then exaggerate the overestimation of winter precipitation. In order to explore the spatiotemporal variations and reliability of winter precipitation over the TP, we compared five precipitation products, including: ERA-Interim, GLDAS, HAR, TRMM, and the observation provided by China Meteorological Administration (CMA), against a sublimation dataset which is severed as the minimum value of precipitation. The sublimation was estimated by the Kuzmin formula constrained with IMS snow cover product and land surface temperature. The intercomparison reveals that CMA has an obvious underestimation (precipitation is less than the one third of sublimation) over the Qiangtang Plateau where there is no observation site while no underestimation in East TP where dense stations available. For reanalysis and remote sensing data, HAR shows the smallest underestimation, while TRMM and GLDAS shows comparable underestimation and both are more apparent than ERA-interim. It implies that the observation data has considerable dry biases (~200%) in winter precipitation over the Western TP where more ground stations are needed to get a reliable precipitation observation.

Surface roughness plays an important role in retrieving soil moisture from microwave observations. Currently, microwave measurements at low-frequency bands (L, S, C, and X bands) provide global soil moisture data as well as surface roughness information. In this study, four global surface roughness products, including the one used in SMOS standard algorithm (named as SMOSHR), one derived from SMOS soil moisture product (named as SMOSD), the one used in SMAP algorithm (named as SMAPHR) and the one retrieved from AMSR-E soil moisture products (named as AMSRQH), were compared against the in situ measured surface roughness values at Pali network in the Tibetan Plateau. The roughness values of SMOSHR and SMAPHR show the same spatial pattern as the measured data at Pali network. Moreover, SMAPHR and SMOSHR have similar spatial pattern over the Tibetan Plateau. However, SMOSD shows different spatial pattern with the other two both at Pali network and over the Tibetan Plateau. Since similar surface roughness parameterization and values were adopted in SMOS and SMAP algorithm, their soil moisture products show consistent spatiotemporal pattern, which could benefit the merging of various soil moisture products across different sensors and platforms.

In this study, the latest released satellite based precipitation products - Global Precipitation Measurement (GPM) mission Level 3 product Integrated Multi-satellitE Retrievals for GPM (IMERG) and version 7 of Tropic Rainfall Measurement Mission (TRMM 3B42V7) data is evaluated and applied with a distributed hydrological model to examine the precipitation detection and performance in hydrological simulating over the Mekong River Basin (MRB) during 2014/4/1 to 2016/1/31. About 137 rain gauges stations were collected to carry out a pixel-point comparison between observation and satellite precipitation. Moreover, daily discharge observation data from five discharge gauges were used to evaluate the performance of hydrological simulation. The result demonstrate that: 1) IMERG data show more precision in both heavy and light rain detection than 3B42V7; 2) IMERG performs better than 3B42V7 when driving hydrologic model, giving the fact that the simulated discharge result from IMERG is more accurate and stable.

Precipitation plays an essential role in land surface processes, as a vital forcing variable of hydrology and ecosystem models. Satellite remote sensing is able to provide precipitation estimations at regional and global scales with high spatiotemporal resolutions. However, the accuracy of these products still need improvement especially at daily scale. In this study, we proposed a new method which can improve the rainfall product of the Global Precipitation Mission by using the soil moisture product of the Soil Moisture Active Passive mission over the Yarlung Zangbo River basin in the Tibetan Plateau. Compared to rain gauge observation, our method improve rainfall estimation at 97 of 108 GPM grids. Overall, the average correlation between improved GPM and in situ observation increased from 0.12 to 0.31, while the RMSE and RRMSE decreased by 1.16 and 0.42, respectively. It indicates that this approach can be used in a large scale to improve satellite-based rainfall products over the Tibetan Plateau.

Decomposition is performed for the 4×4 SMAP radar channel covariance matrix and the correlation between resulting components, surface soil moisture and vegetation is examined. Globally, the first principal component is the most dominant and the correlation coefficients with respect to soil mortise is highest (R2≥ 0.8) in regions with fractional ground cover and sufficient temporal dynamics of soil moisture.

In this study we examined the linear decomposition and relationship between the SMAP observed Polarization Ratio into surface soil moisture and vegetation. Temporal linear regression, per each SMAP pixel, is performed to estimate the decomposition coefficients. Variances (explained variance) in PR is predominantly dominated by dynamics of surface soil moisture and degrades with increasing vegetation amount. Although PR, by itself, is high in arid and semi-arid regions, due to lack of moisture and vegetation dynamics, the explained variance is very small.

Landscape freeze/thaw (FT) state is a key variable in Earth's carbon cycle. NASA's Soil Moisture Active Passive (SMAP) satellite mission, launched in January 2015, provides global retrievals of FT state every two to three days. Validating SMAP FT observations with in-situ observations is difficult due to the substantial scale mismatch between a point estimate and a satellite footprint, inducing “representativeness errors” in the in-situ observations. Triple collocation (TC) is a validation technique that addresses this problem by combining estimates from in-situ, model and spaceborne estimates to obtain error estimates for all three products, without assuming that any product is error-free. Unfortunately, it fails when applied to binary or categorical variables, such as landscape FT state. In this study, we use a new variant of TC - categorical triple collocation (CTC) - that can be applied to binary variables, to validate the SMAP FT product across northern land regions (>45N).

Rainfall is one of the key drivers of hydrological and land surface models. Satellite remote sensing is able to provide regional and global rainfall estimates. However, there is still room to improve the accuracy of satellite rainfall products in areas where no/less rain gauges available, such as the Tibetan Plateau. In this study, the satellite rainfall product obtained from the Tropical Rain Measurement Mission (TRMM) and the follow-on Global Precipitation Mission (GPM) is further improved by using the in situ soil moisture observation obtained from a densely soil moisture observation network at Naqu and soil moisture retrieval provided by the Soil Moisture Active Passive (SMAP) mission over the Tibetan Plateau, respectively. In situ rainfall observations obtained from Ministry of Water Resource, which are not used in the calibration of satellite rainfall products, are used as validation data. The results demonstrate that the rainfall estimation skills of TRMM and GPM are significantly improved when in situ soil moisture is used at Naqu, by reducing 20∼30% root mean square errors (RMSE) and increasing the correlation coefficient (CC) from 0.1 to 0.7. When SMAP soil moisture is used, the RMSE is further reduced by 40∼50%, while CC is around 0.6. It indicates that the proposed method has high potential to improve satellite-based rainfall products over the Tibetan Plateau.

Mapping activities of urban land change is important for human activity to Earth's dynamic change. To get the detailed information on urban development maps in large area, dense training samples are needed in different area and specific season, which is cost-consuming. To overcome this issue, we provide a transfer learning method based on deep information to extract urban areas in all season and different areas by only parts of training samples from Beijing in 1999. The proposed method, which is based on an improved recurrent neural network model, aims at: 1) learning a novel model to extract urban features with transfer ability in different areas; 2) overcoming the seasonal, annual and spatial variance to extract urban areas in all seasons; 3) learning the annual urban dynamics in two cities over 30 years simultaneously. Experiments are performed on Beijing and New York over the period from 1984 to 2016, and training samples are only used with a part of Beijing images in 1999. The results show good performances on annual urban detection results.

The synergy of active and passive microwave measurements have attracted increasing attention in recently years. In this study, we investigate the relationship and covariation of the SMAP radar backscatter and radiometer reflectivity as a function of surface roughness and vegetation. Two radar-derived indices, namely the radar vegetation index (RVI) and radar roughness index (RRI) are adopted to account for the contributions from vegetation and surface roughness respectively. The results show RVI distinguishes vegetation density well in sparse to densely vegetated regions, while significantly overestimates the biomass over some dry desert regions due to possible soil volume scattering effects. RRI well captures the negative covariation of active and passive measurements in bare and sparsely vegetated surfaces, while becomes ineffective in densely vegetated areas due to the reduced contribution from soil surfaces.

Urban cellular automata (CA) models propagate and accumulate errors during the modeling process due to the model structure or stochastic processes involved. It is feasible to assimilate real-time observations into an urban CA model to reduce model uncertainties. However, the assimilation performance is sensitive to the spatio-temporal units in the assimilation algorithm, that is, spatial block size and window length (temporal interval). In this study, we coupled an assimilation model, an ensemble Kalman filter (EnKF) and a Logistic-CA model to simulate the urban dynamic in Beijing over a period of two decades. Our results indicate that the coupled EnKF-CA model outperforms the CA-alone counterpart by about 10% in terms of the figure of merit, which reflects the agreement of modeled pixels. We also find that the assimilation performance using a finer block (1 km) is better than that using a coarser block (5 km and 10 km) because of the better depiction of spatial heterogeneity using a finer block. Moreover, the improvement of intermediate outputs using the coupled EnKF-CA model is effective for a certain period (e.g. 5 years). This implies that a high-frequency assimilation may not significantly improve the model performance. The sensitivity analyses of spatio-temporal assimilation in the EnKF-CA model provide a better understanding of the assimilation mechanism that couples with land-use change models.

Soil temperature is one of the essential variables governing the land atmosphere interaction. In this study, we proposed a statistical algorithm to retrieve the surface soil temperature from AMSR-E brightness temperature (TB) observations. The algorithm was developed based on the regression relationship between AMSR-E TB and corresponding in situ soil temperature observed at the Naqu network in the central Tibetan Plateau (CTP-Naqu). The algorithm was validated by application in six soil observation networks distributed globally. The results demonstrate that the algorithm has high accuracy and good transferability and can be applied globally. Moreover, the new algorithm uses passive microwave observations and is able to retrieve soil temperature all-day and all-weather, which compensates the deficiency of thermal infrared-based algorithms that are vulnerable to cloud contamination.

In this study, we present a detailed inter-annual analysis of four remotely sensed leaf area index (LAI) products: GLASS, GLOBALBNU, GLOBMAP, and MODIS LAI, and conduct correlation analysis between LAI and three meteorological variables over China. The results manifest that the four products agree well in most of northern regions of China, with higher correlation coefficient and the same response to meteorological factors. The phenology matches well for different biome types, reaching the peak in July or August. The changing trend of LAI from 2001-2011 is almost same for four products, with approximately 56% greening and 44% browning. However, abrupt changes occur at different time among four products. LAI is positively correlated with meteorological station annual average precipitation and temperature, while negatively correlated with annual average sunshine duration in spatial scale.

Land data assimilation system (LDAS) has been a powerful tool to optimally combine the advantages of microwave remote sensing and land surface model together and then improve the accuracy of land surface fluxes and status estimation. In this study, we developed a land data assimilation system, in which the water imbalance is constrained by using a multiply times assimilation approach. The first step is a standard variational assimilation operating at normal assimilation windows. And then the LDAS will run at an optimal assimilation window for several times to minimize the water imbalance accumulated in the first step. The LDAS is tested over CEOP Mongolia network. The microwave brightness temperature (TB) observation of AMSR-E are assimilated into the LDAS. Sensitivity test which considering the impacts of assimilation frequency and length of assimilation window are conducted. The results show that for soil moisture assimilation 48 hours window with 2 times assimilation could control the accumulated residual into the acceptable region.

In this paper, a novel change detection method learned from Recurrent Neural Network with transferable ability is proposed. The proposed method, which is based on an improved Long Short Term Memory (LSTM) model, aims at: 1) learning a novel change detection rule to distinguish changed regions with high accuracy; 2) analyzing a new target data with transferable ability from learned change rule; 3) learning the differencing information and detecting the changes independently without any classifiers. In the process of learning the change rule, a core memory cell is utilized to detect and record the differencing information in multi-temporal images; meanwhile, the memory cell can update the storage by iteration for optimization. Finally, experiments are performed on two multi-temporal datasets, and the results show superior performance on detecting changes with transferable ability.

This paper presents a very early evaluation and comparison of GPM Level 3 IMERGHH and TRMM3B42V7 final products from 2014/4/1 to 2015/3/31 at daily scale over whole Mekong River Basin (MRB). Daily observation data from 53 in situ rainfall gauges is obtained to do the pixel-point comparison. Two aspects (rainfall amount and occurrence) were taken to evaluate the performance of these two satellite precipitation products. The result demonstrates that: (1) both TRMM3B42 V7 and GPM 3IMERGHH have an underestimation of rainfall over MRB; (2) the performance of the two products in wet season is better than dry season; (3) generally TRMM3B42V7 can achieve a better estimation of rainfall amount than GPM 3IMERGHH during both wet season and dry season; (4) for event judge, GPM can get a better result than TRMM3B42 V7 with a higher Probability of Detection (POD) and a higher Critical Success Index (CSI); (5) TRMM3B42 V7 is more likely to miss a rainfall event while GPM is more likely to make a false alarms. This study shows that although GPM has more capacity to catch a rainfall event, it still has much room for further improvement, especially in the estimation of rainfall amount in dry season.

The spatial distribution characteristics and temporal variation trends of soil moisture significantly affect terrestrial water, energy, and carbon cycles at various scales. Satellite remote sensing is highly expected to provide such valuable information. Before applying the remotely sensed soil moisture products, a thorough validation must be conducted to insure product quality. In this paper, we evaluate the soil moisture products retrieved from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission and the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission - Water (GCOM-W) over Heihe river basin in China, respectively. The land cover in Heihe river basin changes from desert to grass, agriculture field, and then mountain forest, which makes the basin an obvious spatial variation in soil moisture field and valuable to check the reliability and stability of two soil moisture products. We calculate the diurnal relative difference (DRD) of monthly averaged soil moisture between day and night observation of each products, and comparing them with that calculated from corresponding Global Land Data Assimilation System (GLDAS) simulations. The comparison results indicate that the SMOS soil moisture products are much unstable than AMSR2 retrievals. The DRD of SMOS is 20 times larger than that of AMSR2 and 100 times larger than that of GLDAS. We speculate that the radio frequency interference effects on SMOS observation may contribute to this unstable performance. Moreover, the retrievals from multi-angle observations in SMOS algorithm is also a potential source causing this systemic bias.

This paper presents an evaluation of two different global satellite precipitation products (TRMM 3B42v7 and TRMM 3B42 RT) during 2001-2004 over the Mekong River basin for hydrologic applications with using a distributed hydrological model. The result demonstrates that generally 3B42 V7 is closer to rainfall field interpolated by gauge data and has a better performance in runoff simulation than 3B42 RT, which overestimates 15.6% annual rainfall over the basin. In addition, a pixel-point comparison with the gauge data shows that, calibration against ground rain gauge observation greatly reduce the bias in 3B42 V7. On the contrary, 3B42 RT shows region-dependent bias: overestimation in the upper Mekong; underestimation in the middle basin. Such region-dependent bias of 3B42 RT is propagated into the simulated discharge, which makes the simulated discharge overestimate at all the stations especially at the upstream stations. Regard to the seasonal variations, both 3B42 V7 and RT have a good estimation during wet seasons with Pearson correlation coefficient (CC) higher than 0.60. The discharge simulation reveals that 3B42 V7 is able to monitor the streamflow at daily scale over whole basin and that 3B42 RT performs well during wet season in lower basin. It implies there is potential to make near real time hydrological simulation through using 3B42 RT.

In this study, we inter-compared the spatial-temporal characteristics of SMOS and AMSR-E soil moisture products over Mongolia plateau. The results show that in temporal scale, the standard deviation of SMOS soil moisture data is higher than JAXA and ECMWF products, comparable to the in situ observation. To demonstrate the spatial variation of SMOS and JAXA soil moisture products, we first defined the smoothness index (SSI). The mean and maximum value of SSI of SMOS are much higher than those of JAXA and ECMWF for both daily and monthly soil moisture products. The mean value of SSI of daily SMOS products is 1.284, while that of JAXA and ECMWF is 0.010 and 5.010@10-4, respectively. For monthly products, the mean value of SSI of SMOS is 0.264, while the value of JAXA is 0.004, and 3.753*10-4for ECMWF. Further, we counted the SSI of SMOS and JAXA TB, and the SSI mean value of SMOS TB is higher than that of JAXA TB for both daily and monthly time scale. It indicates that the big uncertainty of SMOS soil moisture products may raises from the unstable TB observation, which is highly contaminated by RFI and even cannot be removed at monthly scale.

In this study, we tested the influence of aerosols on surface and satellite observations of total cloud cover (TCC). The surface-observed TCC (TCCsur) and satellite-retrieved TCC (TCCsat) shows a good agreement over China and difference between them (TCCDIFF) are mostly within the -10% to 10% range. The monthly mean TCCDIFF, AOD and visibility have a consistent spatial pattern and the negative TCCDIFFappears mostly in high AOD and low visibility regions. As expected, TCC difference would increase as aerosol loading increases. We found that for negative TCCDIFF(MODIS reports more clouds than Synop), both the frequency and range increase as the AOD increases from 0 to 0.8. Compared with the positive TCCDIFF, negative ones show a more clear relation with aerosol loading. When the aerosol loading increases, there are more significant difference cases with TCCDIFFless than -20%.

Soil moisture is an important variable in the Earth system. Reliable estimation of soil moisture, especially in regional and global scale, is essential for climatic, meteorological and hydrological researches. This study presents a revised soil moisture retrieval algorithm of ASMR2 that aims to improve soil moisture estimate in dry regions through including the vertical profile effects of soil temperature into the radiative transfer model. In situ observations of soil moisture and temperature profile, measured during HIWater Experiment intensive observation period, were used to formulate profile functions. By comparing the results of new algorithm against ground observations, the feasibility and capability of the revised algorithm were verified. The application of this algorithm in wide regions also demonstrated that our method could alleviate the overestimation-in-desert problem of original JAXA AMSR2 soil moisture products.

Low cloud is the main source of precipitation as well as an important modulator of radiative fluxes. Satellite and in situ observations provide valuable cloud information. In this study, we estimated the low cloud amount over North China Plain based on MODIS products, and validated the results with ground-based and CloudSat data. First, we classified low cloud into two types: pure low cloud and overlapped low cloud. Then we conducted detection algorithm for pure low cloud, and estimated the total low cloud fraction (LCF) under a random overlapping assumption. Monthly mean MODIS-derived and ground-based LCF showed a good agreement in seasonal variation. And the linear correlation coefficient for these two LCFs was 0.642. Finally, MODIS-derived low cloud scenes were collated with CloudSat cloud scenarios, both the pure and overlapped ones were validated.

It is widely recognized that remote sensing data a very valuable source of information for the modeling of land-atmosphere interactions. During the last of couple decades, remote sensing data were used mainly to define the initial status of land surface models, to classify the land cover and land use type, and to correct the estimation of state variables in a data assimilation system. One relatively unexplored issue consists of the optimization of land surface parameters, such as, for example, soil texture, soil porosity, through remote sensing. This is due to the lack of a relationship which connecting remote sensing data with land surface parameters. The objective of this paper is to develop a systemic method to retrieve a number of land surface parameters through a combination of microwave remote sensing, radiative transfer modeling, land surface modeling and multi-objective optimizing. The microwave brightness temperature observations are used as the calibration references. The land surface model and radiative transfer model are used to determine the relationship between land surface parameters and the brightness temperature. The method is validated through a field experiment, in which a ground-based microwave radiometer is used to provide brightness temperature observations in a controllable footprint. Grand truth of soil parameters is also measured through intensive in situ samplings. The comparison of optimized parameters with the in situ observed ones indicates that our method has high potential to calibrate land surface parameters.

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) 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; 3) 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 non arable 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.

A land surface data assimilation system was developed to simulate the water, energy and mass exchanges between land and atmosphere. The atmospheric forcing data was subset from GLDAS, which was free and globally available. The dynamic model is a modified version of the Simple Biosphere Model, in which the carbon flux simulation was improved by including a soil respiration mechanism. Brightness temperature observation from AMSR-E was assimilated into the system to improve the surface soil moisture estimation, and then the energy and carbon fluxes simulation. The system was validated by ground observations and then applied over whole Africa continent for 2009. The distribution patterns of carbon sink and source over the continent are identified from the results.

Information about vegetation status has widespread utility in agriculture, forestry, hydrology, and land-atmosphere interaction study. This paper presents an attempt to monitor the vegetation water content (VWC) by merging visible/infrared remote sensing and microwave remote sensing. We first derived a relationship between VWC and microwave vegetation index (MVI) through a field experiment. The relationships between VWC and Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were also studied. We found that MVI has the largest correlation coefficient with VWC, while NDWI has the second largest one. The VWC-MVI relationship derived from field experiment was used to estimate VWC from AMSR-E data at Mongolia sites. The results were compared with AMSR-E standard VWC products. We found the new method overestimated VWC but highly correlated to the AMSR-E products. This study reveals the potential of MVI to monitoring VWC variation.

The land-atmosphere interaction in the Tibetan Plateau plays an important role in the Asian summer monsoon and the global energy and water cycle. This study presents a method to improve the land surface water and energy fluxes simulation by using a land data assimilation system (LDAS), which merging microwave remote sensing data and GCM output into a land surface model. NCEP reanalysis data is used as the background field and also as the meteorological forcing for the land surface model. Two experiments were designed as by driving LDAS-UT with two sets of atmospheric forcing data, (1) with in situ observed forcing data and (2) with NCEP reanalysis data at Gaize and Naqu sites. Results show that LDAS is able to estimate land surface soil moisture and energy fluxes accurately. The RMSE of soil moisture simulation is around 0.03–0.05 and RMSE of net radiation simulation is around 30W/M2. This study reveals the potential for using satellite remote sensing data to improve land surface fluxes estimation.

Soil moisture is the central focus of land surface and atmospheric modeling because it controls surface water and energy fluxes and consequently affects land-atmosphere interactions. Although global or regional satellite-derived surface soil moisture data sets are readily available, knowledge about assimilating them into numerical weather prediction (NWP) models is limited. The methods of assimilating soil moisture products in NWP models have several limitations, and they cannot be applied in near-real-time applications. As a result, this paper focuses on the development of a system [a Land Data Assimilation System coupled with a mesoscale Atmospheric model (LDAS-A)] that couples satellite land data assimilation with a mesoscale model to physically introduce land surface heterogeneities into the mesoscale model. The LDAS-A consists of a sequential LDAS that directly assimilates the lower frequency passive microwave brightness temperatures, and therefore, its use is feasible for near-real-time NWP applications. The LDAS-A was validated for the Tibetan Plateau using surface, radiosonde, and satellite observations. The simulation results show that the LDAS-A effectively improved the land surface variables (i.e., surface soil moisture and skin temperature) compared with the no-assimilation case and that it has the potential to correct uncertainties resulting from model initialization, model-specific parameters, and model forcing on a wider scale. The improved land surface conditions in the LDAS-A improve the land-atmosphere feedback mechanism, and the assimilated results provide better prediction of atmospheric profiles (i.e., potential temperature and specific humidity) than the no-assimilation case when compared with radiosonde soundings. Improvements in solar radiation, in addition to soil moisture, are necessary to introduce realistic land-atmosphere interactions into a mesoscale model.

In this study, we generated and compared two sets of soil moisture data in the North Africa region, by using the land data assimilation system developed at the University of Tokyo (LDAS-UT) which is driven by the NECP reanalysis data and UKMO forcing data, separately. GPCP precipitation data was used as reference data for the indirect validation, after its accuracy was confirmed by comparing it with the in-situ observation in the Medjerdah Basin. The soil moisture generated by LDAS-UT with NECP forcing is in a good relationship to the GPCP rainfall data, while that generated by LDAS-UT with UKMO forcing is mismatching with the GPCP precipitation patterns.

This paper reports an application of an Land Data Assimilation System developed in the University of Tokyo (LDASUT) on the Gaize PBL site at the northwest of Tibet Plateau, for the period from July to August, 2007. The objectives of this study are: (1) to validate LDASUT in bare soil field using in-situ observation, (2) to check the feasibility to estimate areal land surface variables reliably with using LDASUT driven by GCM output data. For the system validation, LDASUT was first driven by in-situ observed micrometeorological data, and simulated energy fluxes were compared to hourly direct measurements; simulated soil moisture content was compared to the in-situ soil moisture observation at the depth of 4 cm. The results show that LDAS can generally simulate those variables well and thus the capability of LDAS is validated. In order to check the possibility of applying LDAS globally and simulating surface energy and water budget worldwide, Japan Meteorology Agency (JMA) Model Output Local Time Series (MOLTS) data were used as the driven data of LDAS. Performance of LDAS was not so good when it was driven by the JMA MOLTS data. This result demonstrated that there were systemic biases lied in JMA MOLTS data in the study region and thus it can not directly apply to LSM or LDAS.

Radar remote sensing has shown its potential for retrieving soil moisture from soil surfaces. Since the backscattering process is also influenced by soil roughness, the characterization of this roughness is crucial for an accurate estimation. The algorithm proposed in this investigation aiming to obtain the roughness parameters for every SAR pixel which could facilitate the derivation of soil moisture in virtue of multi-angle ASAR images, and combined with a semi-empirical calibration for the correlation length. An application of the means was performed in the middle reaches of the Heihe river basin and achieved satisfactory results (RMSE less than 6 vol %).

This paper reports the progresses of AMSR-E soil moisture algorithm development at the University of Tokyo. The first progress is made through improving the forward model, i.e. radiative transfer model (RTM), based on field experiment and numerical simulation. The second progress is the development of a new parameterization method, through which the parameters necessary for the algorithm are optimized by a land data assimilation system developed at the University of Tokyo (LDAS-UT). The capability of LDAS-UT was validated successfully with winter wheat experiment data. Finally, the new RTM and parameterization method was validated on AMSR-E match up data set. The results demonstrate that the simulated brightness temperature is in good agreements with the one observed by AMSR-E.

From November 2006 to June 2007 a field experiment 'Tanashi Experiment' was conducted in a farm of the University of Tokyo, Japan. Continuous ground measurements of meteorological variables, soil moisture and temperature profiles and vegetation status have been taken. At the same time, the ground based microwave radiometers (GBMR) are employed to provide accurate field measurements of brightness temperature up-welling from the plot, at the frequencies of 6.925, 10.65, 18.7, 23.8, 36.5 and 89 GHz. The scientific objectives are presented in this paper and the corresponding experiment set-up is described. The influences of vegetation layer on the brightness of various frequencies are analyzed. The TB of all frequencies and polarization is found to be saturated and reach same values when vegetation water content larger than 4 kg/m2. Based on the analysis of winter wheat experiment results, the Polarization Index (PI) of 6.9 GHz and the Index of Soil Wetness (ISW) calculated from 18 GHz and 6.9 GHz horizontal polarization were recommended to compose the look up table. Potential applications of this winter wheat microwave radiometer observation are the development and validation of land surface variables retrieval algorithm and the study of land surface process and the land atmosphere interaction, and.

This paper reports the development of a soil moisture retrieval algorithm for spaceborne passive microwave radiometers. The algorithm is based on a modified radiative transfer model, so-called DMRT-AIEM model. The implementation of this algorithm consists of three steps: 1) forward model parameters optimization; 2) lookup table generation and 3) lookup table reversion and soil moisture estimation. The algorithm was tested at a CEOP (coordinate enhanced observing period) reference site on the Mongolia Gobi. The retrieved soil moisture data was compared with the in situ observations. The comparison results show that the performance of the new algorithm is good, giving a standard error of the estimate (SEE) of 3.8% and R-square of 0.4. Moreover, a successful TB validation on SSM/I low frequencies was achieved by the RTM used in this algorithm. It means this algorithm provides a possibility to retrieval around 20 years' soil moisture data from SSM/I observations.

To investigate the potential of passive microwave techniques for observing the atmosphere over land, it is important to understand the nature of emissions from the land surface. The heterogeneity of large-scale land surface emissions has been cited as a major impediment in conducting observations of the atmosphere over land. Many models, both theoretical and empirical, have been developed to explain the surface emission with varying degrees of success. In the past, most field-supported research in soil observations using microwave techniques has concentrated on lower frequencies (L-band). This paper reports on a study, supported by field data, that seeks to improve our understanding of surface emission at various frequencies using passive microwave radiometers. This provides a crucial link between remote sensing of the land surface and the atmosphere. We show that it is important to consider shadowing associated with rough wet surfaces. By incorporating shadowing effects, the advanced integral equation model (AIEM) shows remarkable agreement with observations at all frequencies and polarizations. Although the roughness parameters obtained during our experiment correspond to very rough conditions, by including shadowing effects the AIEM model is able to transition from the not so rough natural condition as observed from space to the very rough as obtained during field experiments

A series of field experiments were conducted to verify the effects of roughness on passive microwave emission. From these field experiments, it was observed that surface roughness increases observed brightness temperatures (higher emissivity) at horizontal polarization while diminishing the vertically polarized brightness temperatures marginally. The advanced integral equation method (AIEM) and QP models were found to model the effects of surface roughness fairly reasonably. The QP model which is a parameterized version of the AIEM was found to show correspondence with the AIEM simulations and is therefore recommended for application in AMSR based data assimilation schemes.

This paper presents the development of an improved soil radiative transfer model (RTM) which considering the volume scattering effect of soil particles, an unexplored part of traditional RTMs, through field experiments and numerical simulations. The field observations were conducted by using the ground based passive microwave radiometer (GBMR) to measure the brightness temperature of dry sand layer over background materials, metal plates or absorbers. The existence of volume scattering effects in the dry sand was demonstrated through field experiments. Then, the observed data were simulated by the dense media radiative transfer (DMRT) model. The simulation results show that the DMRT model which includes the volume scattering effects performers better than the generally used surface emission model which does not include volume scattering effects.