Advanced Microwave Scanning Radiometer 2 Soil Moisture Research Articles

Refinement of NOAA AMSR-2 Soil Moisture Data Product—Part 2: Development With the Optimal Machine Learning Model

Advanced Microwave Scanning Radiometer-2 (AMSR2) is a successor of AMSR for Earth-Observation System (AMSR-E), while the third generation of AMSR (AMSR3) will be launched in the near future. The AMSR2 soil moisture product is also an important component of the Soil Moisture Operational Products System (SMOPS) datasets that are operationally produced by National Oceanic and Atmospheric Administration (NOAA). Refinement of the NOAA AMSR2 soil moisture data product can not only benefit the past AMSR-E and the upcoming AMSR3, but also improve the SMOPS data quality. In this second paper of the two-part series, the Extreme Gradient Boosting (XGB) model was trained using the AMSR2 6.925 GHz, 10.65 GHz, 18.7 GHz and 36.5 GHz brightness temperature (Tb) measurements in dual polarizations, ancillary maps and the vegetation index datasets, and in turn used to predict the daily global AMSR2 soil moisture retrievals from 2012 to 2021. Validation results show that the refined AMSR2 soil moisture retrievals (AMSRr) show an overwhelming advantage in data accuracy over the currently operational AMSR2 product (AMSRc). Comapred to the AMSRc, the developed AMSRr presents a significant improvement on data availability. Results also indicate that the refined AMSR2 datasets are comparable with the latest version Soil Moisture Active Passive soil moisture product. Based on this study, higher quality AMSRr soil moisture data product will be operationally produced in the NOAA, and will eventually benefit the NOAA SMOPS blended soil moisture product and its users.

Refinement of NOAA AMSR-2 Soil Moisture Data Product: 1. Intercomparisons of the Commonly Used Machine-Learning Models

Soil Moisture is an important variable for hydrological, meteorological and agricultural studies and applications. The Soil Moisture Operational Products System (SMOPS) was developed by the National Oceanic and Atmospheric Administration (NOAA)-National Environmental Satellite, Data, and Information Service (NESDIS) to operationally provide an integrated satellite soil moisture data product. The Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture retrieval is an important component of the currently operational SMOPS. This study is proposed to refine the AMSR2 data product using an optimal machine learning model, and this first paper of the two-part series is to intercompare the six commonly-used machine learning models including multiple linear regression (MLR), Regression Tree (RRT), Random Forest (RFT), Gradient Boosting (GBR), Extreme Gradient Boosting (XGB) and Artificial Neural Network (ANN). Results indicate that all of the six approaches can preserve the reference data information beyond the training time period, which ensures them to predict past and future satellite retrievals without a new training procedure. Relative to other models, the XGB method is more successful to respect to the reference data Soil Moisture Active Passive (SMAP) and the in-situ observations from the U. S. Department of Agriculture Soil Climate Analysis Network (SCAN). It has a good implication on the implementation of the XGB model to refine the AMSR2 soil moisture retrievals in the second paper.

Evaluation of SMOS, SMAP, AMSR2 and FY-3C soil moisture products over China

Microwave remote sensing can provide long-term near-surface soil moisture data on regional and global scales. Conducting standardized authenticity tests is critical to the effective use of observed data products in models, data assimilation, and various terminal scenarios. Global Land Data Assimilation System (GLDAS) soil moisture data were used as a reference for comparative analysis, and triple collocation analysis was used to validate data from four mainstream passive microwave remote sensing soil moisture products: Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active and Passive (SMAP), Global Change Observation Mission–Water using the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument, and Fengyun-3C (FY-3C). The effects of topography, land cover, and meteorological factors on the accuracy of soil moisture observation data were determined. The results show that SMAP had the best overall performance and AMSR2 the worst. Passive microwave detection technology can accurately capture soil moisture data in areas at high altitude with uniform terrain, particularly if the underlying surface is soil, and in areas with low average temperatures and little precipitation, such as the Qinghai–Tibet Plateau. FY-3C performed in the middle of the group and was relatively optimal in northeast China but showed poor data integrity. Variation in accuracy between products, together with other factors identified in the study, provides a baseline reference for the improvement of the retrieval algorithm, and the research results provide a quantitative basis for developing better use of passive microwave soil moisture products.

A Comprehensive Evaluation of Gridded L-, C-, and X-Band Microwave Soil Moisture Product over the CZO in the Central Ganga Plains, India

Recent developments in passive microwave remote sensing have provided an effective tool for monitoring global soil moisture (SM) observations on a spatiotemporal basis, filling the gap of uneven in-situ measurement distribution. In this paper, four passive microwave SM products from three bands (L, C, and X) are evaluated using in-situ observations, over a dry–wet cycle agricultural (mostly paddy/wheat cycle crops) critical zone observatory (CZO) in the Central Ganga basin, India. The L-band and C/X-band information from Soil Moisture Active Passive (SMAP) Passive Enhanced Level 3 (SMAP-L3) and Advanced Microwave Scanning Radiometer 2 (AMSR2), respectively, was selected for the evaluation. The AMSR2 SM products used here were derived using the Land Parameter Retrieval Model (LPRM) algorithm. Spatially averaged observations from 20 in-situ distributed locations were initially calibrated with a single and continuous monitoring station to obtain long-term ground-based data. Furthermore, several statistical metrices along with the triple collocation (TC) error model were used to evaluate the overall accuracy and random error variance of the remote sensing products. The results indicated an overall superior performance of SMAP-L3 with a slight dry bias (−0.040 m3·m−3) and a correlation of 0.712 with in-situ observations. This also met the accuracy requirement (0.04 m3·m−3) during most seasons with a modest accuracy (0.059 m3·m−3) for the entire experimental period. Among the LPRM datasets, C1 and C2 products behaved similarly (R = 0.621) with a ubRMSE of 0.068 and 0.081, respectively. The X-band product showed a relatively poor performance compared to the other LPRM products. Seasonal performance analysis revealed a higher correlation for all the satellite SM products during monsoon season, indicating a strong seasonality of precipitation. The TC analysis indicated the lowest error variance (0.02 ± 0.003 m3·m−3) for the SMAP-L3. In the end, we introduced Spearman’s rank correlation to assess the dynamic response of SM observations to climatic and vegetation parameters.

Improvement of AMSR2 Soil Moisture Retrieval Using a Soil-Vegetation Temperature Decomposition Algorithm

It is well documented that soil moisture can be retrieved from passive microwave observations. A basic assumption of most passive microwave-based soil moisture retrieval algorithms is that vegetation temperatures (Tv) and soil temperatures (Ts) are equal (i.e., Tv=Ts), which however is not well satisfied in some cases, especially during daytime. In this study, we proposed a soil-vegetation temperature decomposition (SVTD) approach to avoid such an assumption, which can improve the accuracy of soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) data. First, the SVTD was used to decompose the vegetation and soil temperatures of the soil-vegetation mixed pixels in the Tibetan Plateau (TP). Subsequently, the decomposed temperature was integrated into the soil moisture retrieval algorithm to correct the effects of soil and vegetation temperatures, and soil moisture is then retrieved following the same strategy adopted in the land parameter retrieval model (LPRM). Finally, the algorithm was validated against densely-instrumented soil moisture networks (Maqu, Naqu, and Ngari) built in the Tibetan Plateau, and was also compared with the LPRM AMSR2 soil moisture product. Results indicate the proposed algorithm performs much better than the original LPRM in soil-vegetation mixed areas. The proposed SVTD method is promising for soil moisture retrieval from passive microwave satellites, especially in the daytime when the difference between soil and vegetation temperatures is relatively large.

Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019

Abstract. High-quality and long-term soil moisture products are significant for hydrologic monitoring and agricultural management. However, the acquired daily Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture products are incomplete in global land (just about 30 %–80 % coverage ratio), due to the satellite orbit coverage and the limitations of soil moisture retrieval algorithms. To solve this inevitable problem, we develop a novel spatio-temporal partial convolutional neural network (CNN) for AMSR2 soil moisture product gap-filling. Through the proposed framework, we generate the seamless daily global (SGD) AMSR2 long-term soil moisture products from 2013 to 2019. To further validate the effectiveness of these products, three verification methods are used as follows: (1) in situ validation, (2) time-series validation, and (3) simulated missing-region validation. Results show that the seamless global daily soil moisture products have reliable cooperativity with the selected in situ values. The evaluation indexes of the reconstructed (original) dataset are a correlation coefficient (R) of 0.685 (0.689), root-mean-squared error (RMSE) of 0.097 (0.093), and mean absolute error (MAE) of 0.079 (0.077). The temporal consistency of the reconstructed daily soil moisture products is ensured with the original time-series distribution of valid values. The spatial continuity of the reconstructed regions is in accordance with the spatial information (R: 0.963–0.974, RMSE: 0.065–0.073, and MAE: 0.044–0.052). This dataset can be downloaded at https://doi.org/10.5281/zenodo.4417458 (Zhang et al., 2021).

Evaluation of SMAP, SMOS, and AMSR2 Soil Moisture Products Based on Distributed Ground Observation Network in Cold and Arid Regions of China

Long-term surface soil moisture (SM) data are increasingly needed in water budget and energy balance analysis of watersheds. The performance of nine remotely sensed SM products from Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) missions are evaluated based on observations collected from distributed observation networks in the Heihe River Basin (HRB) of China during 2013 to 2017. Results show that the SMAP Level 3 dual channel algorithm SM retrievals reflect the seasonal SM variations well with high temporal correlations of ∼0.7 and high accuracy within 0.04 m3/m3 in terms of unbiased root mean squared error (ubRMSE) over the grassland in the HRB. The SMOS level 3 SM retrievals present increased underestimation and ubRMSE of ∼0.10 m3/m3 as the vegetation increases. The newly published SMOS Institut National de la Recherche Agronomique–Centre d'Etudes Spatiales de la BIOsphere product in version 2 outperforms the SMOS level 3 product with improved temporal correlation coefficient above 0.4 and lower ubRMSE of ∼0.05 m3/m3. AMSR2 Land Parameter Retrieval Algorithm SM products show extremely large overestimation over the vegetated regions in HRB, especially the C-band products. Drastically high underestimation biases are observed in the Japan Aerospace Exploration Agency AMSR2 SM product. Parameter uncertainty analyses indicate that the different parameterization schemes of vegetation optical depth inputs could be one of the main reasons resulting in the systematic overestimation/underestimation biases in the AMSR2/SMOS/SMAP SM retrievals. The findings aim to provide insights into studies on algorithms refinements and data fusions of SM products in HRB.

Improving soil moisture retrieval accuracy of Advanced Microwave Scanning Radiometer 2 in vegetated areas using land surface parameters of Visible Infrared Imaging Radiometer Suite

The current remote sensing systems designed to measure soil moisture have a relatively coarse spatial resolution ranging from 25 to 50 km. The Advanced Microwave Scanning Radiometer 2 (AMSR2) is a passive sensor that measures soil moisture through C-band (6.9 and 7.3 GHz) observation of brightness temperature (BT). AMSR2 uses land parameter retrieval model to retrieve surface soil moisture and vegetation optical depth. This model partitions the microwave observation into its respective soil and vegetation emission components. AMSR2 loses sensitivity to soil moisture as vegetation density increases, during the growing season. Field observations show that AMSR2 tends to overestimate the soil moisture when the vegetation intensity increases and covers the soil. We address two existing issues in the use of soil moisture products of the AMSR2: (1) spatial resolution of the soil moisture product and (2) the impact of vegetation cover on the radiative transfer. We used a vegetative index to estimate when soil moisture retrieval is not sufficiently accurate and how the optical data can be used to improve soil moisture estimation. The land surface temperature and vegetation index products of the Visible Infrared Imaging Radiometer Suite are used to downscale the AMSR2 soil moisture products to 1 km. A series of soil moisture data collected in the field were used to analyze the accuracy of the downscaled soil moisture values and the results indicated that introduction of the BT in the downscaling model improves the accuracy of the soil moisture products over the vegetated areas. The mean absolute error (MAE) of the downscaled soil moisture values is ∼5.6 % —an improvement to 8.0% of original AMSR2 soil moisture products. In the presence of vegetation, however, the MAE is still greater than that of a similar analysis of AMSR2 product over semiarid areas.

Evaluation of the AMSR2 L2 soil moisture product of JAXA on the Mongolian Plateau over seven\xa0years (2012\u20132018)

This study evaluated the Advanced Microwave Scanning Radiometer 2 (AMSR2) L2 soil moisture product (ver. 3) using in situ hydrological observational data, acquired over 7 years (2012–2018), from a 50 × 50 km flat area of the Mongolian Plateau covered with bare soil, pasture and shrubs. Although AMSR2 slightly underestimated soil moisture content at 3-cm depth, satisfactory timing was observed in both the response patterns and the in situ soil moisture data, and the differences between these factors were not large. In terms of the relationship between AMSR2 soil moisture from descending orbits and in situ measured soil moisture at 3-cm depth, the values of the RMSE (m3/m3) and the bias (m3/m3) varied from 0.028 to 0.063 and from 0.011 to − 0.001 m3/m3, respectively. The values of the RMSE and bias depended on rainfall condition. The mean value of the RMSE for the 7-year period was 0.042 m3/m3, i.e., lower than the target accuracy 0.050 m3/m3. The validation results for descending orbits were found slightly better than for ascending orbits. Comparison of the Soil Moisture and Ocean Salinity (SMOS) soil moisture product with the AMSR2 L2 soil moisture product showed that AMSR2 could observe surface soil moisture with nearly same accuracy and stability. However, the bias of the AMSR2 soil moisture measurement was slightly negative and poorer than that of SMOS with deeper soil moisture measurement. It means that AMSR2 cannot effectively measure soil moisture at 3-cm depth. In situ soil temperature at 3-cm depth and surface vegetation (normalized difference vegetation index) did not influence the underestimation of AMSR2 soil moisture measurements. These results suggest that a possible cause of the underestimation of AMSR2 soil moisture measurements is the difference between the depth of the AMSR2 observations and in situ soil moisture measurements. Overall, this study proved the AMSR2 L2 soil moisture product has been useful for monitoring daily surface soil moisture over large grassland areas and it clearly demonstrated the high-performance capability of AMSR2 since 2012.

Evaluation and Analysis of AMSR2 and FY3B Soil Moisture Products by an In Situ Network in Cropland on Pixel Scale in the Northeast of China

An in situ soil moisture observation network at pixel scale is constructed in cropland in the northeast of China for accurate regional soil moisture evaluations of satellite products. The soil moisture products are based on the Japan Aerospace Exploration Agency (JAXA) algorithm and the Land Parameter Retrieval Model (LPRM) from the Advanced Microwave Scanning Radiometer 2 (AMSR2), and the products from the FengYun-3B (FY3B) satellite are evaluated using synchronous in situ data collected by the EC-5 sensors at the surface in a typical cropland in the northeast of China during the crop-growing season from May to September 2017. The results show that the JAXA product provides an underestimation with a bias (b) of -0.094 cm3/cm3, and the LPRM soil moisture product generates an overestimation with a b of 0.156 cm3/cm3. However the LPRM product shows a better correlation with the in situ data, especially in the early experimental period when the correlation coefficient is 0.654, which means only the JAXA product in the early stage, with an unbiased root mean square error (ubRMSE) of 0.049 cm3/cm3 and a b of -0.043 cm3/cm3, reaches the goal accuracy (±0.05 cm3/cm3). The FY3B has consistently obtained microwave brightness temperature data, but its soil moisture product data in the study area is seriously missing during most of the experimental period. However, it recovers in the later period and is closer to the in situ data than the JAXA and LPRM products. The three products show totally different trends with vegetation cover, soil temperature, and actual soil moisture itself in different time periods. The LPRM product is more sensitive and correlated with the in situ data, and is less susceptible to interferences. The JAXA is numerically closer to the in situ data, but the results are still affected by temperature. Both will decrease in accuracy as the actual soil moisture increases. The FY3B seems to perform better at the end of the whole period after data recovery.

AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data

Soil moisture (SM) applications in terrestrial hydrology require higher spatial resolution soil moisture products than those provided by passive microwave remote sensing instruments (grid resolution of 9 km or larger). In this investigation, an innovative algorithm that uses visible/infrared remote sensing observations to downscale Advanced Microwave Scanning Radiometer 2 (AMSR2) coarse spatial resolution SM products was developed and implemented for use with data provided by the Advanced Microwave Scanning Radiometer 2 (AMSR2). The method is based on using the Normalized Difference Vegetation Index (NDVI) modulated relationships between day/night SM and temperature change at corresponding times. Land surface model output variables from the North America Land Data Assimilation System (NLDAS), remote sensing data from the Moderate-Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) were used in this methodology. The functional relationships developed using NLDAS data at a grid resolution of 12.5 km were applied to downscale AMSR2 JAXA (Japan Aerospace Exploration Agency) SM product (25 km) using MODIS land surface temperature (LST) and NDVI observations (1 km) to produce the 1 km SM estimates. The downscaled SM estimates were validated by comparing them with ISMN (International Soil Moisture Network) in situ SM in the Black Bear–Red Rock watershed, central Oklahoma between 2015–2017. The overall statistical variables of the downscaled AMSR2 SM validation R2, slope, RMSE and bias, demonstrate good accuracy. The downscaled SM better characterized the spatial and temporal variability of SM at watershed scales than the original SM product.

GCOM-W AMSR2 Soil Moisture Product Validation Using Core Validation Sites

The Advanced microwave scanning radiometer 2 (AMSR2) is part of the global change observationmission-water (GCOM-W). AMSR2 has filled the gap in passive microwave observations left by the loss of theAMSR–earth observing system (AMSR-E) after almost ten years of observations. Both missions provide brightness temperature observations that are used to retrieve soil moisture estimates at the near surface. A merged AMSR-E and AMSR2 data product will help build a consistent long-term dataset; however, before this can be done, it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on the validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites around the world. A total of three soil moisture products that rely on different algorithms were evaluated; the Japan Aerospace Exploration Agency (JAXA) soil moisture algorithm, the land parameter retrieval model (LPRM), and the single channel algorithm (SCA). JAXA, SCA, and LPRM soil moisture estimates capture the overall climatological features. The spatial features of the three products have similar overall spatial structure. The JAXA soil moisture product shows a lower dynamic range in the retrieved soil moisture with a satisfactory performance matrix when compared to in situ observations [unbiased root mean square error (ubRMSE) = 0.059 m3/m3, Bias = −0.083 m3 /m3, R = 0.465]. The SCA performs well over low and moderately vegetated areas (ubRMSE = 0.053 m3/m3, Bias = −0.039 m3/m3, R = 0.549). The LPRM product has a large dynamic range compared to in situ observations with a wet bias (ubRMSE = 0.094 m3 /m3, Bias = 0.091 m3/m3, R = 0.577). Some of the error is due to the difference in observation depth between the in situ sensors (5 cm) and satellite estimates (1 cm). Results indicate that overall the JAXA and SCA have the best performance based upon the metrics considered.

Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products

Global-scale surface soil moisture (SSM) products retrieved from active and passive microwave remote sensing provide an effective method for monitoring near-real-time SSM content with nearly daily temporal resolution. In the present study, we first inter-compared global-scale error patterns and combined the Soil Moisture Active Passive (SMAP), Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products using a triple collocation (TC) analysis and the maximized Pearson correlation coefficient (R) method from April 2015 to December 2016. The Global Land Data Assimilation System (GLDAS) and global in situ observations were utilized to investigate and to compare the quality of satellite-based SSM products.The average R-values of SMAP, ASCAT, and AMSR2 were 0.74, 0.64, and 0.65 when they compared with in situ networks, respectively. The ubRMSD values were (0.0411, 0.0625, and 0.0708) m3m−3; and the bias values were (−0.0460, 0.0010, and 0.0418) m3m−3 for SMAP, ASCAT, and AMSR2, respectively. The highest average R-values from SMAP against the in situ results are very encouraging; only SMAP showed higher R-values than GLDAS in several in situ networks with low ubRMSD (0.0438m3m−3). Overall, SMAP showed a dry bias (−0.0460m3m−3) and AMSR2 had a wet bias (0.0418m3m−3); while ASCAT showed the least bias (0.0010m3m−3) among all the products.Each product was evaluated using TC metrics with respect to the different ranges of vegetation optical depth (VOD). Under vegetation scarce conditions (VOD<0.10), such as desert and semi-desert regions, all products have difficulty obtaining SSM information. In regions with moderately vegetated areas (0.10<VOD<0.40), SMAP showed the highest Signal-to-Noise Ratio. Over highly vegetated regions (VOD>0.40) ASCAT showed comparatively better performance than did the other products.Using the maximized R method, SMAP, ASCAT, and AMSR2 products were combined one by one using the GLDAS dataset for reference SSM values. When the satellite products were combined, R-values of the combined products were improved or degraded depending on the VOD ranges produced, when compared with the results from the original products alone.The results of this study provide an overview of SMAP, ASCAT, and AMSR2 reliability and the performance of their combined products on a global scale. This study is the first to show the advantages of the recently available SMAP dataset for effective merging of different satellite products and of their application to various hydro-meteorological problems.

Comparison and Downscale of AMSR2 Soil Moisture Products with In Situ Measurements from the SCAN–NRCS Network over Puerto Rico

A continuous spatio-temporal database of accurate soil moisture (SM) measurements is an important asset for agricultural activities, hydrologic studies, and environmental monitoring. The Advanced Microwave Scanning Radiometer 2 (AMSR2), which was launched in May 2012, has been providing SM data globally with a revisit period of two days. It is imperative to assess the quality of this data before performing any application. Since resources of accurate SM measurements are very limited in Puerto Rico, this research will assess the quality of the AMSR2 data by comparing it with ground-based measurements, as well as perform a downscaling technique to provide a better description of how the sensor perceives the surface soil moisture as it passes over the island. The comparison consisted of the evaluation of the mean error, root mean squared error, and the correlation coefficient. Two downscaling techniques were used, and their performances were studied. The results revealed that AMSR2 products tend to underestimate soil moisture. This is due to the extreme heterogeneous distributions of elevations, vegetation densities, soil types, and weather events on the island. This research provides a comprehensive study on the accuracy and potential of the AMSR2 products over Puerto Rico. Further studies are recommended to improve the AMSR2 products.

Improvement of AMSR2 Soil Moisture Products Over South Korea

Soil moisture (SM) is a critical parameter for interpreting the status of land surfaces and vegetation because it has an important role in the exchange of water and heat energy between the land surface and atmosphere. SM can be retrieved with satellite microwave sensors, which offer several advantages in that they are cost-effective, give rapid results, and provide data for inaccessible or isolated areas. In addition, these sensors cover large areas and perform periodic multi-channel observations. The advanced microwave scanning radiometer-2 (AMSR2), a successor of Advanced Microwave Scanning Radiometer for the Earth Observing System, provides regular global SM data products, which the Korea Meteorological Administration uses to calculate daily mean and seven-day moving window SM do not agree well with ground measurements in South Korea, and must be corrected for reliable drought monitoring. In this study, we used Global Land Data Assimilation System SM data to calibrate AMSR2 SM. In addition, we applied and combined two methods to correct AMSR2 SM: 1) linear regression with input variables, including insolation, daily precipitation, and the normalized difference vegetation index and 2) the cumulative distribution function (CDF) matching method. The results of the combined linear regression and CDF matching method were in better agreement with the in situ data than those of either method alone. Correcting AMSR2 SM by using the combined method improved the correlation coefficient (R) from 0.19 to 0.59 and the root-mean-square error from 0.23 to 0.09.

Evaluation of SMAP, SMOS, and AMSR2 soil moisture retrievals against observations from two networks on the Tibetan Plateau

AbstractTwo soil moisture and temperature monitoring networks were established in the Tibetan Plateau (TP) during recent years. One is located in a semihumid area (Naqu) of central TP and consists of 56 soil moisture and temperature measurement (SMTM) stations, the other is located in a semiarid area (Pali) of southern TP and consists of 21 SMTM stations. In this study, the station data are used to evaluate soil moisture retrievals from three microwave satellites, i.e., the Soil Moisture Active Passive (SMAP) of NASA, the Soil Moisture and Ocean Salinity (SMOS) of European Space Agency, and the Advanced Microwave Scanning Radiometer 2 (AMSR2) of Japan Aerospace Exploration Agency. It is found that the SMAP retrievals tend to underestimate soil moisture in the two TP networks, mainly due to the negative biases in the effective soil temperature that is derived from a climate model. However, the SMAP product well captures the amplitude and temporal variation of the soil moisture. The SMOS product performs well in Naqu network with acceptable error metrics but fails to capture the temporal variation of soil moisture in Pali network. The AMSR2 products evidently exaggerate the temporal variation of soil moisture in Naqu network but dampen it in Pali network, suggesting its retrieval algorithm needs further improvements for the TP.

Inter-comparison of SMOS and AMSR-E soil moisture products during flood years (2010–2011) over Pakistan

ABSTRACTGlobal soil moisture products retrieved from various remote sensing sensors are becoming readily available with a nearly daily temporal resolution. With lack of ground observations in Pakistan, microwave sensors are viable technologies for retrieving soil moisture from space. Thus, this study compares soil moisture data from Soil Moisture and Ocean Salinity (SMOS) with Advanced Microwave Scanning Radiometer (AMSR-E) for 2010 and 2011, which were years where massive flooding occurred. We find suitable correlations (r = 0.47) for Level-2 soil moisture values for both satellites and report temporal variability during the pre-monsoon, monsoon, post-monsoon and winter seasons. This study motivates using satellite retrievals as a start towards more comprehensive studies in Pakistan.

위성기반 토양수분 자료의 한반도 지역 적용성 평가: AMSR2 LPRM 알고리즘과 지점관측 자료를 이용하여

Paper number: 16-014Received: 4 February 2016; Revised: 17 March 2016; Accepted: 17 March 2016AbstractThis study aims at assessing the quality of the Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture products onboard GCOM-W1 satellite based on Land Parameter Retrieval Model (LPRM) soil moisture retrieval algorithm with field measurements in South Korea from March to September, 2014. Results of mean bias and root mean square error between AMSR2 LPRM soil moisture products (X-band) and ground measurements showed reasonable value of 0.03 and 0.16. Also, the maximum of the Pearson correlation coefficients was 0.67, which showed good agreement in terms of temporal variability with ground measurements. By comparing AMSR2 soil moisture with in-situ measurement according to the overpass time and band frequency, X-band products on the ascending time outperformed than those of C1-band and C2-band. Furthermore, this study offers an insight into the applicability of the AMSR2 soil moisture products for monitoring various natural disasters at a large scale such as drought and flood.Keywords: Soil moisture, Microwave, AMSR2, LPRM

Performance of Global Soil Moisture Products in Crop Growing Region of Central India

The study was carried out to evaluate the global soil moisture product of Advanced Microwave Scanning Radiometer-2 (AMSR-2), Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), Soil Moisture and Ocean Salinity (SMOS) and Tropical Rainfall Measuring Mission’s (TRMM) Microwave Imager (TMI) in central region of India for monsoon period of 2011 and 2013. The evaluation was done using 100, 75 in-situ soil moisture measurements of 2011, 2013 respectively. The in-situ measurements were spread across ~10,000 sq.km area which is roughly equal to 12 pixels of SMOS, AMSR-2 AMSR-E and TMI data. The result shows reasonable correlation of r 2 = 0.506, 0.467 between SMOS and in-situ soil moisture for 2011, 2013 respectively. TMI exhibits good correlation of r 2 = 0.55 in 2013 but failed to do the same in 2011. AMSR-2 soil moisture products shows moderate correlation r 2 = 0.32 in 2011 but poorly correlated in 2013.

Spatial Downscaling of AMSR2 Soil Moisture Content using Soil Texture and Field Measurements

Soil moisture content is generally accepted as an important factor to understand the process of crop growth and is the basis of earth system models for analysis and prediction of the crop condition. To continuously monitor soil moisture changes at kilometer scale, it is demanded to create high resolution data from the current, several tens of kilometers. In this paper we described a downscaling method for Advanced Microwave Scanning Radiometer 2 (AMSR2) Soil Moisture Content (SMC) from 10 km to 30 m resolution using a soil texture and field measurements that have a high correlation with the SMC. As a result, the soil moisture variations of both data (before and after downscaling) were identical, and the Root Mean Square Error (RMSE) of SMC exhibited the low values. Also, time series analyses showed that three kinds of SMC data (field measurement, original AMSR2, and downscaled AMSR2) had very similar temporal variations. Our method can be applied to downscaling of other soil variables and can contribute to monitoring small-scale changes of soil moisture by providing high resolution data.