Advanced Microwave Scanning Radiometer-Earth Observing System Research Articles

Downscaling of AMSR-E Soil Moisture over North China Using Random Forest Regression

Satellite retrieval can offer global soil moisture information, such as Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data. AMSR-E has been used to provide soil moisture all over the world, with a coarse resolution of 25 km × 25 km. The coarse resolution of the soil moisture dataset often hinders its use in local or regional research. This work proposes a new framework based on the random forest (RF) model while using five auxiliary data to downscale the AMSR-E soil moisture data over North China. The downscaled results with a 1 km spatial resolution are verified against in situ measurements. Compared with AMSR-E data, the correlation coefficient of the downscaled data is increased by 0.17, and the root mean squared error, mean absolute error, and unbiased root mean square error are reduced by 0.02, 0.01, and 0.03 m3/m3, respectively. In addition, the comparison results with Multiple Linear Regression and Support Vector Regression downscaled data show that the proposed method significantly outperforms the other two methods. The feasibility of our model is well supported by the importance analysis and leave-one-out analysis. Our study, which combines RF with spatiotemporal search algorithms and efficient auxiliary data, may provide insights into soil moisture downscaling in large areas with various surface characteristics and climatic conditions.

Spatiotemporal Change Analysis of Soil Moisture Based on Downscaling Technology in Africa

Evaluating the long-term spatiotemporal variability in soil moisture (SM) over Africa is crucial for understanding how crop production is affected by drought or flooding. However, the lack of continuous and stable long-term series and high-resolution soil moisture records impedes such research. To overcome the inconsistency of different microwave sensors (Advanced Microwave Scanning Radiometer-EOS, AMSR-E; Soil Moisture and Ocean Salinity, SMOS; and Advanced Microwave Scanning Radiometer 2, AMSR2) in measuring soil moisture over time and depth, we built a time series reconstruction model to correct SM, and then used a Spatially Weighted Downscaling Model to downscale the SM data from three different sensors to a 1 km spatial resolution. The verification of the reconstructed data shows that the product has high accuracy, and can be used for application and analysis. The spatiotemporal trends of SM in Africa were examined for 2003–2017. The analysis indicated that soil moisture is declining in Africa as a whole, and it is notably higher in central Africa than in other subregions. The most significant decrease in SM was observed in the savanna zone (slope < −0.08 m3 m−3 and P < 0.001), followed by South Africa and Namibia (slope < −0.07 m3 m−3 and P < 0.01). Seasonally, the most significant downward trends in SM were observed during the spring, mainly over eastern and central Africa (slope < −0.07 m3 m−3, R < −0.58 and P < 0.001). The analysis of spatiotemporal changes in soil moisture can help improve the understanding of hydrological cycles, and provide benchmark information for drought management in Africa.

A Simplified Approach to Retrieve the K-Band Microwave Surface Emissivity Under Clear Skies

Microwave land surface emissivity (MLSE) at the K band plays a key role in driving geophysical parameters, such as land surface temperature (LST). However, satellite-based MLSE currently is hard to be quickly retrieved in clear skies since the time cost is high in removing atmospheric contributions. In this letter, one clear-sky atmospheric profile dataset, including a wide range of precipitable water vapor (PWV) values, was constructed using the Thermodynamic Initial Guess Retrieval database for analyzing numerical relationships between PWV and atmospheric parameters. Then, a simplified algorithm was developed for accurately retrieving instantaneous K-bandMLSEs (18.7 and 23.8 GHz) under clear skies, which can significantly save the time of atmospheric correction. The sensitivity analysis shows that PWV is a key factor affecting MLSE estimation at 23.8 GHz, and the brightness temperature (BT) uncertainty has a greater impact on MLSE estimation than LST. Additionally, with LST derived from the Moderate-resolution Imaging Spectroradiometer, BT from the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E), and the ERA5 reanalysis PWV in 2008, the proposed algorithm was respectively applied in Europe and the United States for presenting its applicability at a station scale and regional scale. The actual sounding profile and global AMSR-E MLSE product were used as validation datasets. Results indicate the simplified approach has a good performance with RMSEs less than 0.02 in the site and regional validations. Whereas, there are some apparent overestimations in estimating clear-skies MLSEs, especially for 23.8 GHz. We believe the proposed approach is promising for retrieving other parameters.

Global Snow Depth Retrieval From Passive Microwave Brightness Temperature With Machine Learning Approach

Current global snow retrieval algorithms based on spaceborne microwave measurements inherit noticeable biases and uncertainties regarding spatial distribution and temporal variations. In this article, we present an improved spatiotemporally dynamic global snow depth retrieval algorithm to account for the heterogeneity of snowpacks in different seasons worldwide. The proposed model adopts nonlinear machine learning to retrieve snow depths from passive microwave measurements and other auxiliary information. We indirectly characterized the variation in snow grain size using the daily profiles of the temperature gradient within the snowpack. In addition, a zoning and multitemporal modeling strategy was employed to reduce the bias and uncertainty caused by snow heterogeneity across different ecoregions and seasons. The proposed model was implemented to retrieve the global daily snow depth from 2001 to 2010. The results were validated by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> observations and compared with the NASA Advanced Microwave Scanning Radiometer for EOS (AMSR-E) snow water equivalent product (AE_DySno). Satisfactory accuracy was achieved for different ecoregions with regard to daily, monthly, and yearly validations (the root-mean-square error (RMSE) varied from ~7.5 to ~12 cm; the Pearson correlation coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> ranged from 0.75 to 0.85). The results of ten trials indicated the promising stability of the proposed model in different ecoregions with small variations in RMSE and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> values. Compared with the AE_DySno products, the estimation results did not exhibit the overestimation problem and provided snow depth patterns with greater spatial heterogeneity, showing RMSEs ~5 cm lower and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> values ~0.3 higher than those of the AE_DySno products.

A Novel RFI Detection Method for Microwave Radiometers Using Multilag Correlators

Radio frequency interference (RFI) is an increasing problem in microwave radiometry, particularly for Earth observation, because the antennas are pointing toward the Earth. RFI has been observed at the L-band in European Space Agency’s (ESA’s) Soil Moisture and Ocean Salinity (SMOS) Earth Explorer mission, National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active and Passive (SMAP) and Aquarius missions, and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and WindSat missions at 10.7 and 18.7 GHz (Misra and de Matthaeis, 2014). Therefore, dedicated onboard systems are, nowadays, a must to detect and remove contaminated measurements, improving radiometric accuracy and increasing the spatial coverage. In this work, a novel detection technique, specially tailored for synthetic aperture interferometric radiometers (SAIRs), is proposed and its performance analyzed. It is based on the change of the shape of the cross correlation function at lags different from zero under the presence of RFI. The performance of the proposed technique is compared to other common RFI detection algorithms, and its limitations and advantages are discussed. Postcorrelation detection performance is found to outperform other commonly used algorithms, such as kurtosis. In addition, it presents some convenient properties for its practical application in correlation and real aperture radiometers.

Assessing forest fire properties in Northeastern Asia and Southern China with satellite microwave Emissivity Difference Vegetation Index (EDVI)

In the context of global warming, forest fires are expected to occur more frequently and intensively, and impose more significant impacts on human society, terrestrial ecosystems, and atmosphere. Most of the existing methods in monitoring large-scale forest fire are based either on satellite visible and infrared observations or weather-based indices. This work explored the advantages of a new satellite microwave-based vegetation index in monitoring forest fire occurrence and fire intensity in Northeastern Asia and Southern China. Specifically, we used satellite observations during 2002–2011 to investigate the correlation at different temporal scales between forest fire properties (fire count, FC; fire radiative power, FRP) and the vegetation water content proxy of the Microwave Emissivity Difference Vegetation Index (EDVI) derived from the Moderate Resolution Imaging Spectroradiometer and the Advanced Microwave Scanning Radiometer-EOS on Aqua satellite. The correlations were compared to that with three weather-based indices including the Fine Fuel Moisture Code, Initial Spread Index and Fire Weather Index (FWI) to determine whether EDVI provides new independent information of forest fires. Finally, EDVI and the weather-based indices FWI were combined to establish multivariate linear regression models to estimate FC and FRP. Results show that: 1) the temporal variations of FC and total FRP are negatively correlated with EDVI using the daily and monthly observations at 1° grid and regional scales; and overall opposite annual cycles and interannual variations between FC (and total FRP) and EDVI are observed in Northeastern Asia and Southern China; 2) compared to the weather-based indices, EDVI shows higher correlation with the temperate forest fire properties in Southern China while shows weaker correlation with the forest fire properties in Northeastern Asia; and a combination of the two kind of indices is found to improve the explained variance for fire properties in both regions; 3) multivariate linear regression models based on EDVI and FWI provide better estimation of FC and FRP compared to the linear regression models based on FWI alone. To our knowledge, this is the first work that comprehensively investigates the potential application of the microwave-based vegetation water content index in forest fire count and fire intensity.

A Genetic Algorithm–Assisted Deep Neural Network Model for Merging Microwave and Infrared Daily Sea Surface Temperature Products

Sea surface temperature (SST) is an important factor in the global ocean–atmosphere system, being vital in a variety of climate analyses and air–sea interaction research studies. However, estimating daily SST with both high precision and high spatial completeness remains a challenge. This article attempts to solve this problem by merging two complementary daily SST products, that is, the 25 km-resolution Advanced Microwave Scanning Radiometer for EOS (AMSR-E) SST and 4 km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) SST, using a genetic algorithm–assisted deep neural network model (GA-DNNM). The merged SST with a spatial resolution of 4 km and a temporal resolution of 1 day is achieved. Experiments in the Asia and Indo-Pacific Ocean (AIPO) region in 2005 were conducted to demonstrate the feasibility and advantages of the proposed method. Results showed that the spatial coverages of the original MODIS SST and AMSR-E SST are ranging from 25.0 to 48.1%, and 31.5 to 47.6%, respectively, while the merged SST achieves a spatial coverage ranging from 56.1 to 73.1%, with improvements ranging from 50.2 to 131.7% relative to the original MODIS SST. Comparisons with drifting buoy observations indicate that the merged SST is accurate, with an average bias of 0.006°C and an average RMSE of 0.502°C, in places where the MODIS SST data are missing before being merged in the AIPO area, and with an average bias of −0.082 °C, and an average RMSE of 0.603°C for the merged SST in the whole study area.

A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002\u20132019)

Long term surface soil moisture (SSM) data with stable and consistent quality are critical for global environment and climate change monitoring. L band radiometers onboard the recently launched Soil Moisture Active Passive (SMAP) Mission can provide the state-of-the-art accuracy SSM, while Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 series provide long term observational records of multi-frequency radiometers (C, X, and K bands). This study transfers the merits of SMAP to AMSR-E/2, and develops a global daily SSM dataset (named as NNsm) with stable and consistent quality at a 36 km resolution (2002–2019). The NNsm can reproduce the SMAP SSM accurately, with a global Root Mean Square Error (RMSE) of 0.029 m3/m3. NNsm also compares well with in situ SSM observations, and outperforms AMSR-E/2 standard SSM products from JAXA and LPRM. This global observation-driven dataset spans nearly two decades at present, and is extendable through the ongoing AMSR2 and upcoming AMSR3 missions for long-term studies of climate extremes, trends, and decadal variability.

Retrieving Aerosol Optical Depth and High Spatial Resolution Ocean Surface Wind Speed From CALIPSO: A Neural Network Approach

A neural network nonlinear regression algorithm is developed for retrieving ocean surface wind speed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The neural network is trained with CALIPSO ocean surface and atmospheric backscatter measurements together with collocated Advanced Microwave Scanning Radiometer for EOS (AMSR-E) ocean surface wind speed. Ocean surface wind speeds are derived by applying the neural network algorithm to CALIPSO measurements between 2008 and 2020. CALIPSO wind speed measurements of 2015 are also compared with Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements on the Global Change Observation Mission–Water “Shizuku” (GCOM-W) satellite. Aerosol optical depths are then derived from CALIPSO’s ocean surface backscatter signal and theoretical ocean surface reflectance calculated from CALIPSO wind speed and Cox-Munk wind–surface slope variance relation. This CALIPSO wind speed retrieval technique is an improvement from our previous studies, as it can be applied to most clear skies with optical depths up to 1.5 without making assumptions about aerosol lidar ratio.

A Remote Sensing-Based Assessment of Water Resources in the Arabian Peninsula

A better understanding of the spatiotemporal distribution of water resources is crucial for the sustainable development of hyper-arid regions. Here, we focus on the Arabian Peninsula (AP) and use remotely sensed data to (i) analyze the local climatology of total water storage (TWS), precipitation, and soil moisture; (ii) characterize their temporal variability and spatial distribution; and (iii) infer recent trends and change points within their time series. Remote sensing data for TWS, precipitation, and soil moisture are obtained from the Gravity Recovery and Climate Experiment (GRACE), the Tropical Rainfall Measuring Mission (TRMM), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), respectively. The study relies on trend analysis, the modified Mann–Kendall test, and change point detection statistics. We first derive 10-year (2002–2011) seasonal averages from each of the datasets and intercompare their spatial organization. In the absence of large-scale in situ data, we then compare trends from GRACE TWS retrievals to in situ groundwater observations locally over the subdomain of the United Arab Emirates (UAE). TWS anomalies vary between −6.2 to 3.2 cm/month and −6.8 to −0.3 cm/month during the winter and summer periods, respectively. Trend analysis shows decreasing precipitation trends (−2.3 × 10−4 mm/day) spatially aligned with decreasing soil moisture trends (−1.5 × 10−4 g/cm3/month) over the southern part of the AP, whereas the highest decreasing TWS trends (−8.6 × 10−2 cm/month) are recorded over areas of excessive groundwater extraction in the northern AP. Interestingly, change point detection reveals increasing precipitation trends pre- and post-change point breaks over the entire AP region. Significant spatial dependencies are observed between TRMM and GRACE change points, particularly over Yemen during 2010, revealing the dominant impact of climatic changes on TWS depletion.

Impact of channel selection on SST retrievals from passive microwave observations

Two retrieval algorithms developed as a part of the European Space Agency Climate Change Initiative (ESA-CCI) project are used to assess the effects of withholding observations from selected frequency channels on the retrieved subskin Sea Surface Temperature (SST) from AQUA's Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) and to evaluate a Copernicus Imaging Microwave Radiometer (CIMR) like channel configuration.The first algorithm is a statistical regression-based retrieval algorithm, while the second is a physically based optimal estimation (OE) algorithm. A database with matching satellite and drifting buoy observations is used to test the performance of each channel configuration using both retrieval algorithms to identify the most optimal channel selection for accurate SST retrievals. The evaluation against in situ observations allows identification of the strengths and weaknesses of the two retrieval algorithms, and demonstrates the importance of using in situ observations to evaluate existing theoretical retrieval uncertainty studies. Overall, the performance increases as expected when more channels are included in the retrieval. In particular, more channels allow a better performance for the range of different observing conditions (e.g. cold waters). The two retrieval algorithms agree that for a three-channel configuration, the 6, 10, 18 GHz (V and H polarization) is better than the 6, 10, 23 GHz configuration (V and H polarization). This is demonstrated for different geographical regions and throughout all seasons. Of the different combinations tested here, it is evident that withholding observations from the 23 and 36 GHz channels from the retrieval has the least impact on the SST performance. Overall, this analysis shows that the CIMR like channel configuration performs very well when compared to an AMSR-E like constellation using both retrieval algorithms.

A New Approach for Soil Moisture Downscaling in the Presence of Seasonal Difference

The variation of soil moisture (SM) is a complex and synthetic process, which is impacted by numerous factors. The effects of these factors on soil moisture are dynamic. As a result, the relationship between soil moisture and explanatory variables varies with time and season. This kind of change should be considered in obtaining fine spatial resolution soil moisture products. We chose a study area with four distinct seasons in the temperate monsoon region. In this research, we established seasonal downscaling models to avoid the influence of seasonal differences. Precipitation, land surface temperature, evapotranspiration, vegetation index, land cover, elevation, slope, aspect and soil texture were taken as explanatory variables to produce fine spatial resolution SM. SM products derived from Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) were downscaled with the help of machine learning algorithms. We compared three machine learning algorithms of random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) to determine the most suitable algorithm for this study. The results show that season-based downscaling is even better than continuous time series. In the analysis of seasonal differences, precipitation plays a dominant role, but its contribution rate is different in each season. Moreover, the influence of vegetation is more prominent in winter, while the influence of terrain is more important in the other three seasons. It could be noted that the accuracy of the RF model is the best among three machine learning algorithms, and the RF-downscaled products have superior matching performance to both AMSR (AMSR-E and AMSR2) SM products and in-situ measurements. The analysis indicates considering seasonal difference and the application of machine learning has high potential for spatial downscaling in remote sensing applications.

A Fundamental Climate Data Record Derived from AMSR-E, MWRI, and AMSR2

Fundamental climate data records (FCDRs) play a vital role in monitoring climate change. In this article, we develop a spaceborne passive microwave-based FCDR by recalibrating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) on the Aqua satellite, the microwave radiometer imager (MWRI) onboard the Feng-Yun-3B (FY3B) satellite, and the Advanced Microwave Scanning Radiometer-2 (AMSR2) onboard the JAXA’s Global Change Observation Mission first-Water (GCOM-W1) satellite. Before recalibration, it is found that AMSR-E and AMSR2 observations are stable over time, but MWRI drifted colder before May 2015 and had nonnegligible errors in geolocation for most channels. In addition, intersensor differences of brightness temperatures (TBs) are as large as 5–10 K. To improve data consistency and continuity, several intersensor calibration methods are applied by using AMSR2 as a reference while using MWRI to bridge the data gap between AMSR2 and AMSR-E. The double difference method is used to provide intersensor difference time series for correcting calibration biases, such as scene temperature-dependent bias, solar-heating-induced bias, and systematic constant bias. Hardware differences between sensors are corrected using principal component analysis. After recalibration, the mean biases of both MWRI and AMSR-E are less than 0.3 K compared to the AMSR2 reference and their standard deviations are less than 1 K for all channels. Under oceanic rain-free conditions, the TB biases are less than 0.2 K for all channels and no significant relative bias drifts were found between sensors for overlapping observations. These statistics suggest that the consistency between these instruments was significantly improved and the derived FCDR could be useful to obtain long-term water cycle-related variables for climate research.

Spatiotemporal variation of ice sheet melting in the Antarctic coastal marginal zone and the influence of ice lenses and rain using satellite microwave observation

This study uses satellite microwave observations to examine the spatiotemporal variations of melting ice sheets on the slopes of Antarctica. These observations were focused on the coastal marginal zone and inland traverse route near Syowa Station. Melting was detected using a 36 GHz vertical polarization diurnal amplitude variation (DAV). Data from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 during 2002–2011 and 2012–2017, respectively, showed DAV variations over time. It was estimated that the greatest melting extent occurred during the 2003/2004 summer; the DAV rose even in the inland areas. In contrast, DAV decreased during rain periods, which was reported by the Japanese Antarctic Research Expedition in 2004 and 2012. This is due to the rise in nighttime brightness temperature (TB). whereas the signal from rain was limited to the lower area. Coastal zones showed a low TB. The low coastal emissions may be attributed to the refrozen ice lenses under the coastal snow layers. The DAV can be significant as the contrast between daytime and nighttime increases from this low TB zone.

The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation

Abstract. The observational uncertainty in sea ice concentration estimates from remotely sensed passive microwave brightness temperatures is a challenge for reliable climate model evaluation and initialization. To address this challenge, we introduce a new tool: the Arctic Ocean Observation Operator (ARC3O). ARC3O allows us to simulate brightness temperatures at 6.9 GHz at vertical polarization from standard output of an Earth System Model. To evaluate sources of uncertainties when applying ARC3O, we compare brightness temperatures simulated by applying ARC3O on three assimilation runs of the MPI Earth System Model (MPI-ESM), assimilated with three different sea ice concentration products, with brightness temperatures measured by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) from space. We find that the simulated and observed brightness temperatures differ up to 10 K in the period between October and June, depending on the region and the assimilation run. We show that these discrepancies between simulated and observed brightness temperature can be attributed mainly to the underlying observational uncertainty in sea ice concentration and, to a lesser extent, to the data assimilation process, rather than to biases in ARC3O itself. In summer, the discrepancies between simulated and observed brightness temperatures are larger than in winter and locally reach up to 20 K. This is caused by the very large observational uncertainty in summer sea ice concentration and the melt pond parametrization in MPI-ESM, which is not necessarily realistic. ARC3O is therefore capable of realistically translating the simulated Arctic Ocean climate state into one observable quantity for a more comprehensive climate model evaluation and initialization.

Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging

In this study, an existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products from 1998 to 2015 to assess its added value in long-term applications. Five of the products used are included in existing merging schemes such as the European Space Agency’s essential climate variable soil moisture (ECV) program. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National Aeronautics and Space Administration’s (NASA) Aqua satellite, the WindSAT radiometer, onboard the Coriolis satellite and the soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission on Water (GCOM-W). The sixth, the microwave radiometer imager (MWRI) onboard China’s Fengyun-3B (FY3B) satellite, is absent in the ECV scheme. Here, the normalized soil moisture products are merged based on their availability within the study period. Evaluation of the merged product demonstrated that the correlations and unbiased root mean square differences were improved over the whole period. Compared to ECV, the merged product from this scheme performed better over dense and sparsely vegetated regions. Additionally, the trends in the parent inputs are preserved in the merged data. Further analysis of FY3B’s contribution to the merging scheme showed that it is as dependable as the widely used AMSR2, as it contributed significantly to the improvements in the merged product.

Discriminating Possible Causes of Microwave Brightness Temperature Positive Anomalies Related With May 2008 Wenchuan Earthquake Sequence

Based on the spatiotemporally weighted two-step method (STW-TSM), the spatiotemporal characteristics of the residual microwave brightness temperature (MBT) with the Mw7.9 Wenchuan earthquake on May 12, 2008 are revealed by satellite data from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) sensor. Two significant MBT positive anomalies are found to be exactly in spatial accordance with two geological Quaternary zones, and the detailed geometric information of the MBT positive anomaly is found to be correlated with the microwave frequencies. After eliminating other possible influential factors, including surface temperature, vegetation index, land-surface roughness, and surface soil moisture under the conditions of space, time, and magnitude, and according to the microwave radiative transfer model, the dielectric variation in the ground surface is suggested to be the primary contributor of the MBT positive anomaly. The positive-hole (P-hole) theory is applied to interpret the geological preference of the MBT positive anomalies through a chain process: crustal stress enhancing—P-hole producing and flowing down stress gradients—surface P-hole accumulating—dielectric constant decreasing—and microwave radiation increasing. The stress-resulting effect of the dielectric decrease on MBT the increase provides a novel mechanism for microwave remote-sensing monitoring of crustal stress field alteration, earthquake preparation, and upcoming shocks. This research has a particular significance for searching potential georelations between the tectonic earthquake preparation and the abnormal satellite MBT.

Comparison of HY-2B Passive Brightness Temperatures with SSMI/S, GMI, AMSR2 and MWRI in Land Surface

China launched a new marine satellite HaiYang (HY)-2B satellite on October 24, 2018 at 22:57 (UTC time), which provides full-time and all-weather observations with a design life of five years. It can cover 90% of the world’s oceans and obtain marine dynamic environment data. The Scanning Microwave Radiometer (SMR) is the main payload onboard the HY-2B, which can provide near-real-time observations. The instrument is a 5-frequency 9-channel microwave observing instrument with the central frequencies set to 6.925, 10.7, 18.7, 23.8, and 37 GHz. Except for 23.8 GHz, which is vertical polarization only, other four frequencies have both vertical and horizontal polarization detection channels. For the past forty years, passive microwave brightness temperature data from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), Special Sensor Microwave Imager/Sounder (SSMI/S), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Microwave Scanning Radiometer 2 (AMSR2), Microwave Radiation Imager (MWRI) and the microwave imager of Global Precipitation Measurement mission (GMI) have provided considerable information on geophysical and climatic research. In order to expand the use of SMR observations on the earth’s land surface, in this study, SMR is compared with other on-orbit radiometers (SSMI/S, AMSR2, MWRI and GMI), and is conducted the cross-sensor calibration in land surface, which is critical foundation for the inversion of Earth surface geophysical parameters and climate researches in the near future.

Development of a daily soil moisture product for the period of 2002–2011 in Chinese mainland

Soil moisture is an essential climate variable (ECV) concerned widely. Due to its high spatial variability, it is costly to measure soil moisture at tens of kilometers scale. In this study, a ten-year (2002–2011) daily soil moisture dataset at 0.25° spatial resolution for Chinese mainland was produced through assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature (TB) data into a land surface model (LSM). The obtained soil moisture data was evaluated against soil moisture-measuring networks deployed in two wet areas and one dry area of the Tibetan Plateau. The results show that for the wet areas the accuracy of the soil moisture product obtained from the assimilation is considerably higher than that of both AMSR-E official soil moisture products and land surface simulation results, and for the dry area their accuracy is comparable to each other. The spatial pattern of the soil moisture from the new product is consistent with that of soil porosity from an independent survey-based dataset, further confirming the credibility of the new product. According to this product, the transition regions in China show stronger seasonal variation of soil moisture than dry and wet regions, and drier regions have stronger inter-annual variability of soil moisture than wetter regions, particularly during transitional seasons (spring and autumn). The soil moisture product is accessible at the National Tibetan Plateau Data Center.

Estimating Meltwater Drainage Onset Timing and Duration of Landfast Ice in the Canadian Arctic Archipelago Using AMSR-E Passive Microwave Data

Meltwater drainage onset (DO) timing and drainage duration (DD) related to snowmelt-water redistribution are both important for understanding not only the Arctic energy and heat budgets but also the salt/heat balance of the mixed layer in the ocean and sea-ice ecosystem. We present DO and DD as determined from the time series of Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) melt pond fraction (MPF) estimates in an area with Canadian landfast ice. To address the lack of evaluation on a day-by-day basis for the AMSR-E MPF estimate, we first compared AMSR-E MPF with the daily Medium Resolution Imaging Spectrometer (MERIS) MPF. The AMSR-E MPF estimate correlates significantly with the MERIS MPF (r = 0.73–0.83). The estimate has a product quality similar to the MERIS MPF only when the albedo is around 0.5–0.7 and a positive bias of up to 10% in areas with an albedo of 0.7–0.9, including melting snow. The DO/DD estimates are determined by using a polynomial regression curve fitted on the time series of the AMSR-E MPF. The DOs/DDs from time series of the AMSR-E and MERIS MPFs are compared, revealing consistency in both DD and DO. The DO timing from 2006 to 2011 is correlated with melt onset timing. To the best of our knowledge, our study provides the first large-scale information on both DO timing and DD.