Retrieval of All-Sky Land Surface Temperature Considering Penetration Effect Using Spaceborne Thermal and Microwave Radiometry

Thermal infrared (TIR) remote sensing (RS) has been widely adopted for monitoring land surface temperature (LST). However, its application has been limited to cloud-free conditions, resulting in a need for LST retrieval methods that combine microwave (MW) and TIR channels. This is especially crucial in areas frequently covered by clouds. One limitation of the current LST retrieval methods is the absence of considering the penetration effect (PE) of MW, which leads to great uncertainty in barren and sparsely vegetated areas. To address this issue, this study proposes a new perspective that considers the PE to merge the LST retrieved from MW and TIR channels. The soil temperature integral equation is simplified based on the soil temperature and water content profiles. Consequently, a PE-based model is developed to convert the effective soil temperature into LST and merge the LST estimated from passive MW observations with those from moderate resolution imaging spectroradiometer (MODIS) LST products. The model considering PE performs better than the method that does not consider PE, as demonstrated by higher $R$ and lower root-mean-square error (RMSE) values. The PE-based model is then applied to Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) data, and the estimated LST is found to fit well with the MODIS LST product ( $R$ = 0.91). Using this model, an all-sky LST is retrieved by merging passive MW observations and MODIS LST products. Validation of the model at eight ground-based stations over the Tibetan Plateau (TP) demonstrates its reasonable accuracy in both clear-sky and cloudy conditions.</p

Satellite microwave assessment of Northern Hemisphere lake ice phenology from 2002 to 2015

A new automated method enabling consistent satellite assessment of seasonal lake ice phenology at 5 km resolution was developed for all lake pixels (water coverage  ≥  90 %) in the Northern Hemisphere using 36.5 GHz H-polarized brightness temperature (Tb) observations from the Advanced Microwave Scanning Radiometer for EOS and Advanced Microwave Scanning Radiometer 2 (AMSR-E/2) sensors. The lake phenology metrics include seasonal timing and duration of annual ice cover. A moving t test (MTT) algorithm allows for automated lake ice retrievals with daily temporal fidelity and 5 km resolution gridding. The resulting ice phenology record shows strong agreement with available ground-based observations from the Global Lake and River Ice Phenology Database (95.4 % temporal agreement) and favorable correlations (R) with alternative ice phenology records from the Interactive Multisensor Snow and Ice Mapping System (R = 0.84 for water clear of ice (WCI) dates; R = 0.41 for complete freeze over (CFO) dates) and Canadian Ice Service (R = 0.86 for WCI dates; R = 0.69 for CFO dates). Analysis of the resulting 12-year (2002–2015) AMSR-E/2 ice record indicates increasingly shorter ice cover duration for 43 out of 71 (60.6 %) Northern Hemisphere lakes examined, with significant (p  \u3c  0.05) regional trends toward earlier ice melting for only five lakes. Higher-latitude lakes reveal more widespread and larger trends toward shorter ice cover duration than lower-latitude lakes, consistent with enhanced polar warming. This study documents a new satellite-based approach for rapid assessment and regional monitoring of seasonal ice cover changes over large lakes, with resulting accuracy suitable for global change studies

Simulation of SMAP and AMSR2 observations and estimation of multi-frequency vegetation optical depth using a discrete scattering model in the Tibetan grassland

Passive microwave observation at multiple frequencies has received increasing research interests due to its capability to provide comprehensive information of land surface properties. This paper contributes to the simulation of land surface emission and estimation of vegetation optical depth (VOD) at multiple frequencies using a discrete scattering model with a single set of model parameter values. Validity of the Tor Vergata (TVG) discrete scattering model in simultaneously reproducing the Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) and Advanced Microwave Scanning Radiometer 2 (AMSR2) C- (6.925 GHz) and X-band (10.7 GHz) observations over the Tibetan grassland ecosystem is evaluated. Frequency-specific and multi-frequency calibration strategies are implemented to find the suitable set of model parameter values and to isolate the impact of frequency on parameter values. On this basis, the calibrated TVG model is further used to estimate the VOD, and to investigate the impact of microwave frequency and observation angle on the emission simulations and VOD parameterization. The results show that both frequency-specific and multi-frequency calibration strategies achieve comparable and reasonable simulations of SMAP and AMSR2 observations, confirming the feasibility of using an identical physically-based model (i.e. the calibrated TVG model) to simulate multi-frequency land emission driven by a single set of model parameter values. As such, the dependence of emission components and VOD on frequency can be elaborated after isolating the impact of frequency on parameter values. The VOD values derived from the TVG simulations generally increase with increasing frequency and can be linearly correlated to the LAI variations, while current satellite-based retrievals have almost the same magnitude at the L-, C-, and X-band. The explanation for this can be that the retrieved VOD is different from the theoretical definition. Sensitivity test performed using the calibrated TVG model further shows that polarization-dependence of VOD becomes more apparent with the increasing observation angle and frequency. New parameterization has thus been developed to characterize the dependence of VOD on the frequency, observation angle, and polarization for grassland based on the results of sensitivity test. This study may provide new insights in improving model of land emission and retrievals of SM and VOD with physical interpretability based on multi-frequency satellite observations.</p

Analysis of the precipitation characteristics on the Tibetan Plateau using Remote Sensing, Ground-Based Instruments and Cloud models

In this Thesis work, carried out in the frame of CEOP-AEGIS, an EU FP7 funded project, the problem of the precipitation monitoring over the Tibetan Plateau has been addressed. Despite the Plateau key role in water cycle of South East Asia (and in the life of 1.5 billions of people), there is a critical lack of knowledge, because the current estimates of relevant geophysical parameters are based on sparse and scarce observations than can not provide the required accuracy for quantitative studies and reliable monitoring, especially on a climate change perspective. This is particularly true for precipitation, the geophysical parameter with highest spatial and temporal variability. The constantly increasing availability of Earth system observation from spaceborne sensors makes the remote sensing an effective option for precipitation monitoring and the main focus of the present work is the implementation and applications for three years of data (2008, 2009 and 2010) of an array of satellite precipitation techniques, based on different methodological approaches and data sources. First, a sensitivity study on the capability of the most used satellite sensors to detect precipitation at the ground, assessed with respect to raingauges data for selected case studies, has been carried out. Then, two physically based techniques have been implemented based on satelliteborne active (for snow-rate) and passive (for rain-rate) microwave sensor data and the output used for calibrate geostationary IR-based techniques. Finally, two well established global multisensor precipitation products have been considered for reference and intercomparison. All the techniques have been implemented for the 3 years and the results compared at different spatial and temporal scales. The analysis of daily rain amount has shown that in general global algorithms are able to estimate rain amount larger than the ones estimated by other techniques during the monsoon season. In cold months global techniques underestimate precipitation amount and areas, resulting in a dry bias with respect to IR calibrated techniques. Case studies compared with ground radar precipitation data on convective episodes shown that global products tend to underestimate precipitation areas, while IR calibrated techniques provides reliable rainrate patterns, as compared with radar data. Unfortunately, the number of radar case studies was not large enough to allow significant validation studies, and also non data were available for cold months. Annual precipitation cumulated maps show marked differences among the techniques: IR calibrated techniques generally overestimate precipitation amount by a factor of 2 with respect of global products. Reasons for discrepancies are investigated and discussed, pointing out the uncertainties that will probably be solved only with the exploitation of new satellite missions

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

A Review of Global Satellite-Derived Snow Products

Snow cover over the Northern Hemisphere plays a crucial role in the Earth's hydrology and surface energy balance, and modulates feedbacks that control variations of global climate. While many of these variations are associated with exchanges of energy and mass between the land surface and the atmosphere, other expected changes are likely to propagate downstream and affect oceanic processes in coastal zones. For example, a large component of the freshwater flux into the Arctic Ocean comes from snow melt. The timing and magnitude of this flux affects biological and thermodynamic processes in the Arctic Ocean, and potentially across the globe through their impact on North Atlantic Deep Water formation. Several recent global remotely sensed products provide information at unprecedented temporal, spatial, and spectral resolutions. In this article we review the theoretical underpinnings and characteristics of three key products. We also demonstrate the seasonal and spatial patterns of agreement and disagreement amongst them, and discuss current and future directions in their application and development. Though there is general agreement amongst these products, there can be disagreement over certain geographic regions and under conditions of ephemeral, patchy and melting snow

Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas

© 2019 by the authors. High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms

A Blended Global Snow Product using Visible, Passive Microwave and Scatterometer Satellite Data

A joint U.S. Air Force/NASA blended, global snow product that utilizes Earth Observation System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and QuikSCAT (Quick Scatterometer) (QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by employing a newly-developed Air Force Weather Agency (AFWA)/National Aeronautics and Space Administration (NASA) Snow Algorithm (ANSA). This initial blended-snow product uses minimal modeling to expeditiously yield improved snow products, which include snow cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt, and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the U.S., from Colorado during the Cold Lands Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or Diurnal Amplitude Variations (DAV) to detect the onset of melt, and a QSCAT product will be used to map areas of snow that are actively melting