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Near‐surface soil moisture observations from the active microwave ASCAT and the passive microwave AMSR‐E satellite instruments are assimilated, both separately and together, into the NASA Catchment land surface model over 3.5 years using an ensemble Kalman filter. The impact of each assimilation is evaluated using in situ soil moisture observations from 85 sites in the US and Australia, in terms of the anomaly time series correlation‐coefficient, R. The skill gained by assimilating either ASCAT or AMSR‐E was very similar, even when separated by land cover type. Over all sites, the mean root‐zone R was significantly increased from 0.45 for an open‐loop, to 0.55, 0.54, and 0.56 by the assimilation of ASCAT, AMSR‐E, and both, respectively. Each assimilation also had a positive impact over each land cover type sampled. For maximum accuracy and coverage it is recommended that active and passive microwave observations be assimilated together. Key Points Assimilating ASCAT or AMSR‐E SM significantly improves root‐zone SM skill Assimilating ASCAT or AMSR‐E SM yields similar skill, regardless of land cover The minimum SM observation skill for positive assimilation impact is quantified

Surface soil moisture is the key state variable in various hydrological processes. A physically based statistical methodology for surface soil moisture measurement in the Tibet Plateau was developed in this study. The approach was established based on theoretical relationships from the derivation of physical models. The methodology was calibrated using statistical analysis of a large data set obtained during a long‐term experiment in Tibet. The procedure was conducted using multichannel brightness temperature observations from the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR‐E). The most interesting results of this study were that the newly developed microwave vegetation indices (MVIs) are a function of vegetation water content or vegetation transmissivity. The B parameter of MVIs decreased with increased vegetation water content but increased with increased vegetation transmissivity. This enabled the use of MVIs for the correction of vegetation effects in soil moisture inversion. The methodology was tested against several experimental data sets collected from Tibet and was shown to be an effective method of soil moisture retrieval for areas with sparse vegetation coverage. The results also provided a complementary data set of soil moisture for hydrology and climatology studies in the Tibet Plateau. Key Points A methodology for surface soil moisture measurement was developed MVIs are a function of vegetation water content or transmissivity The methodology was shown to be effective for areas with sparse vegetation

We compared 1 year of Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) Wentz and Moderate Resolution Imaging Spectroradiometer (MODIS) cloud liquid water path estimates in warm marine clouds. In broken scenes AMSR‐E increasingly overestimated MODIS, and retrievals became uncorrelated as cloud fraction decreased, while in overcast scenes the techniques showed generally better agreement, but with a MODIS overestimation. We found microwave and visible near‐infrared retrievals being most consistent in extensive marine Sc clouds with correlations up to 0.95 and typical RMS differences of 15 g m−2. The overall MODIS high bias in overcast domains could be removed, in a global mean sense, by adiabatic correction; however, large regional differences remained. Most notably, MODIS showed strong overestimations at high latitudes, which we traced to 3‐D effects in plane‐parallel visible‐near‐infrared retrievals over heterogeneous clouds at low Sun. In the tropics or subtropics, AMSR‐E‐MODIS differences also depended on cloud type, with MODIS overestimating in stratiform clouds and underestimating in cumuliform clouds, resulting in large‐scale coherent bias patterns where marine Sc transitioned into trade wind Cu. We noted similar geographic variations in Wentz cloud temperature errors and MODIS 1.6–3.7 μm droplet effective radius differences, suggesting that microwave retrieval errors due to cloud absorption uncertainties, and visible near‐infrared retrieval errors due to cloud vertical stratification might have contributed to the observed liquid water path bias patterns. Finally, cloud‐rain partitioning was found to introduce a systematic low bias in Wentz retrievals above 180 g m−2 as the microwave algorithm erroneously assigned an increasing portion of the liquid water content of thicker nonprecipitating clouds to rain.

From comparisons with thickness of sea ice from Advanced Very High Resolution Radiometer (AVHRR) and ice‐profiling sonar data we have developed an Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) thin ice thickness algorithm for the Sea of Okhotsk. This algorithm can estimate ice thickness of ≤0.2 m without snow using the polarization ratio of AMSR‐E brightness temperature at a 36.5 GHz channel from a linear relationship with AVHRR ice thickness. When a snow cover exists on the thin ice surface, as occurred a few times in each winter, it is shown that the algorithm cannot detect the thin ice. Sea ice and dense shelf water (DSW) production in coastal polynya are estimated on the basis of heat flux calculation with the daily AMSR‐E ice thickness for three winters (December–March) of 2002–2003 to 2004–2005. The ice production is largest in the northwest shelf (NWS) polynya which accounts for ∼45% of the sum of ice production in major coastal polynyas. The ice production in major coastal polynyas would cover the maximum ice area of the Okhotsk Sea if the average ice thickness is assumed to be 1 m. Variability of the ice production is mainly modulated by air temperature. In the NWS polynya, which is the main DSW production area, the annual DSW formation rate is estimated to be ∼0.36 Sv.

Microwave (MW) remote sensing has the potential to obtain all‐weather land surface temperature (LST) and serves as a complement to the thermal‐infrared (TIR) LST under cloudy sky conditions. However, the accuracy of MW LST is generally lower than that of TIR LST, making the retrieval of highly accurate all‐weather LST a challenging task. We propose an empirical algorithm for retrieving LST from the Advanced Microwave Scanning Radiometer (AMSR‐E) brightness temperature (BT) data. First, we constructed a comprehensive classification system of environmental variables (CCSEV), allowing for the influence of topography, land cover, solar radiation, and atmospheric condition on the spatiotemporal distribution of LST, then the LST was expressed as a function of the combination of different AMSR‐E channels for each CCSEV class. When performing the testing with the data from 2005, 2009 and 2011, the accuracy is 3.27 K, 2.65 K and 3.48 K in the daytime and 2.94 K, 2.63 K, 2.15 K at nighttime, respectively. The proposed algorithm was compared to an existing algorithm developed for China without considering the topography. The result shows that the accuracy of LST has improved by 2.81 K in the daytime and 2.14 K at nighttime in China, compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST. The verification at the Naqu sites in the Qinghai‐Tibet Plateau shows that the accuracy has improved by 1–2 K in the daytime and 0.7–1 K at nighttime. These results indicate that the developed algorithm is universal and accurate and benefits the retrieval of accurate all‐weather LST. Key Points Topographic effect is considered for the first time in the microwave land surface temperature retrieval A classification system involving various factors on land surface temperature distribution is constructed Accuracy of the AMSR‐E LST in China retrieved from the proposed algorithm has been obviously improved compared to the previous algorithms

To overcome the difficulties in determining the optimal parameters needed for a radiative transfer model (RTM), which acts as the observational operator in a land data assimilation system, we have designed a dual‐pass assimilation (DP‐En4DVar) framework to optimize the model state (volumetric soil moisture content) and model parameters simultaneously using the gridded Advanced Microwave Scanning Radiometer–EOS (AMSR‐E) satellite brightness temperature data. This algorithm embeds a dual‐pass (the state assimilation pass and the parameter optimization pass) optimization technique based on an ensemble‐based four‐dimensional variational assimilation method and a shuffled complex evolution approach (SCE‐UA). The SCE‐UA method optimizes the parameters using observational information, thereby leading to improved simulations. The RTM is used to estimate brightness temperature from surface temperature and soil moisture. This algorithm is implemented differently in two phases: the parameter calibration phase and the pure assimilation phase. Both passes are applied in each assimilation time window during the parameter calibration phase. However, only the state assimilation pass is used in the pure assimilation phase after the parameters are determined during the parameter calibration phase. Several experiments conducted using this framework coupled partially with a land surface model (the NCAR CLM3) show that volumetric soil moisture content can be significantly improved to be comparable with in situ observations by assimilating only daily satellite brightness temperature. Furthermore, the improvement in surface soil moisture also propagates to lower layers where no observations are available.

Passive microwave remotely sensed soil moisture products, such as Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) data, have been routinely used to monitor global soil moisture patterns. However, they are often limited in their ability to provide reliable spatial distribution data for soil moisture due to their coarse spatial resolutions. In this study, three machine learning approaches—random forest, boosted regression trees, and Cubist—were examined for the downscaling of AMSR-E soil moisture (25 × 25 km) data over two regions (South Korea and Australia) with different climatic characteristics using moderate resolution imaging spectroradiometer products (1 km), including surface albedo, land surface temperature (LST), Normalized Difference Vegetation Index, Enhanced Vegetation Index, Leaf Area Index, and evapotranspiration (ET). Results showed that the random forest approach was superior to the other machine learning models for downscaling AMSR-E soil moisture data in terms of the correlation coefficient [ r  = 0.71/0.84 (South Korea/Australia) for random forest, 0.75/0.77 for boosted regression trees, and 0.70/0.61 for Cubist] and root-mean-square error (RMSE = 0.049/0.057, 0.052/0.078, and 0.051/0.063, respectively) through cross-validation. The ET and LST were identified as the most influential among the six input parameters when estimating AMSR-E soil moisture for South Korea, while ET, albedo, and LST were very useful for Australia. In overall, the downscaled soil moisture with 1 km resolution yielded a higher correlation with in situ observations than the original AMSR-E soil moisture data. The latter appeared higher than the downscaled data in forested areas, possibly due to the overestimation of soil moisture by passive microwave sensors over forests, which implies that downscaling can mitigate such overestimation of soil moisture.

Land surface temperature (LST) plays a major role in the study of surface energy balances. Remote sensing techniques provide ways to monitor LST at large scales. However, due to atmospheric influences, significant missing data exist in LST products retrieved from satellite thermal infrared (TIR) remotely sensed data. Although passive microwaves (PMWs) are able to overcome these atmospheric influences while estimating LST, the data are constrained by low spatial resolution. In this study, to obtain complete and high-quality LST data, the Bayesian Maximum Entropy (BME) method was introduced to merge 0.01° and 0.25° LSTs inversed from MODIS and AMSR-E data, respectively. The result showed that the missing LSTs in cloudy pixels were filled completely, and the availability of merged LSTs reaches 100%. Because the depths of LST and soil temperature measurements are different, before validating the merged LST, the station measurements were calibrated with an empirical equation between MODIS LST and 0~5 cm soil temperatures. The results showed that the accuracy of merged LSTs increased with the increasing quantity of utilized data, and as the availability of utilized data increased from 25.2% to 91.4%, the RMSEs of the merged data decreased from 4.53 °C to 2.31 °C. In addition, compared with the filling gap method in which MODIS LST gaps were filled with AMSR-E LST directly, the merged LSTs from the BME method showed better spatial continuity. The different penetration depths of TIR and PMWs may influence fusion performance and still require further studies.

Summer sea-ice cover in the Arctic varies largely from year to year owing to several factors. This study examines one such factor, the relationship between interannual difference in winter ice motion and ice area in the following summer. A daily-ice velocity product on a 37.5-km resolution grid is prepared using the satellite passive microwave sensor Advanced Microwave Scanning Radiometer-Earth Observing System data for the nine years of 2003-2011. Derived daily-ice motion reveals the dynamic modification of the winter ice cover. The winter ice divergence/convergence is strongly related to the summer ice cover in some regions; the correlation coefficient between the winter ice convergence and summer ice area ranges between 0.5 and 0.9 in areas with high interannual variability. This relation implies that the winter ice redistribution controls the spring ice thickness and the summer ice cover.

Amazon forests represent nearly half of all tropical vegetation biomass and, through photosynthesis and respiration, annually process more than twice the amount of estimated carbon (CO2) from fossil fuel emissions. Yet the seasonality of Amazon canopy cover, and the extent to which seasonal fluctuations in water availability and photosynthetically available radiation influence these processes, is still poorly understood. Implementing six remotely sensed data sets spanning nine years (2003-2011), with reported field and flux tower data, we show that southern equatorial Amazon forests exhibit a distinctive seasonal signal. Seasonal timing of water availability, canopy biomass growth and net leaf flush are asynchronous in regions with short dry seasons and become more synchronous across a west-to-east longitudinal moisture gradient of increasing dry season. Forest cover is responsive to seasonal disparities in both water and solar radiation availability, temporally adjusting net leaf flush to maximize use of these generally abundant resources, while reducing drought susceptibility. An accurate characterization of this asynchronous behavior allows for improved understanding of canopy phenology across contiguous tropical forests and their sensitivity to climate variability and drought.