The Evaluation of SMAP Enhanced Soil Moisture Products Using High-Resolution Model Simulations and In-Situ Observations on the Tibetan Plateau

Drying–Wetting Changes of Surface Soil Moisture and the Influencing Factors in Permafrost Regions of the Qinghai-Tibet Plateau, China

Soil moisture (SM), an important variable in water conversion between the atmosphere and terrestrial ecosystems, plays a crucial role in ecological processes and the evolution of terrestrial ecosystems. Analyzing and exploring SM’s processes and influencing factors in different permafrost regions of the Qinghai-Tibet Plateau (QTP) can better serve the regional ecological security, disaster warning, water management, etc. However, the changes and future trends of SM on the QTP in recent decades are uncertain, and the main factors affecting SM are not fully understood. The study used SM observations, the Global Land Evapotranspiration Amsterdam Model (GLEAM) SM products, meteorological and vegetation data, Mann–Kendall test, Theil–Sen estimation, Ensemble Empirical Mode Decomposition (EEMD), and correlation methods to analyze and explore the characteristics and influencing factors of SM change in different permafrost regions of the QTP. The results show that: (1) At the pixel scale, GLEAM SM products can better reflect SM changes in the QTP in the warm season. The seasonal permafrost region is closer to the real SM than the permanent region, with a median correlation coefficient (R) of 0.738, median bias of 0.043 m3 m−3, and median unbiased root mean square errors (ubRMSE) of 0.031 m3 m−3. (2) The average SM in the QTP warm season increased at a rate of 0.573 × 10−3 m3 m−3 yr−1 over the recent 40 years, and the trend accelerated from 2005–2020. In 64.31% of the region, the soil was significantly wetted, mainly distributed in the permafrost region, which showed that the wetting rate in the dry region was faster than in the wet region. However, the wetting trend does not have a long-term continuity and has a pattern of “wetting–drying-wetting” on interannual and decadal levels, especially in the seasonal permafrost region. (3) More than 65% of the SM wetting trend on the QTP is caused by temperature, precipitation, and vegetation. However, there is apparent spatial heterogeneity in the different permafrost regions and vegetation cover conditions, and the three factors have a more substantial explanatory power for SM changes in the seasonal permafrost region. With the global climate change, the synergistic SM–Climate–Vegetation effect on the QTP tends to be more evident in the seasonal permafrost region.

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.

A Two-Source Normalized Soil Thermal Inertia Model for Estimating Field-Scale Soil Moisture from MODIS and ASTER Data

Soil moisture (SM) is a crucial component for understanding, modeling, and forecasting terrestrial water cycles and energy budgets. However, estimating field-scale SM based on thermal infrared remote-sensing data is still a challenging task. In this study, an improved Flexible Spatiotemporal DAta Fusion (FSDAF) method based on land-surface Diurnal Temperature Cycle (DTC) model (DFSDAF) was proposed to fuse Moderate Resolution Imaging Spectroradiometer (MODIS) and Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) land-surface temperature (LST) data to generate ASTER-like LST during the night. The reconstructed diurnal LST data at a high spatial resolution (90 m) was then utilized to drive a two-source normalized soil thermal inertia model (TNSTI) for the vegetated surfaces to estimate field-scale SM. The results of the proposed methods were validated at different observation depths (2, 4, 10, 20, 40, 60, and 100 cm) over the Zhangye oasis in the middle region of the Heihe River basin in the northwest of China and were compared with the SM estimates from the TNSTI model and other SM products, including AMSR2/AMSR-E, GLDAS-Noah, and ERA5-land. The results showed the following: (1) The DFSDAF method increased the accuracy of LST prediction, with the determination coefficient (R2) increasing from 0.71 to 0.77, and root mean square error (RMSE) decreasing from 2.17 to 1.89 K. (2) the estimated SMs had the best correlation with the observations at the 10 cm depth (with R2 of 0.657; RMSE of 0.069 m3/m3), but the worst correlation with observations at the 40 cm depth (with R2 of 0.262; RMSE of 0.092 m3/m3); meanwhile, the modeled SMs were significantly underestimated above 40 cm (2, 4, 10, and 20 cm) and slightly overestimated below 40 cm (60 and 100 cm); in addition, the field-scale SM series at high spatial resolution (90 m) showed significant spatiotemporal variation. (3) The SM estimates based on the TNSTI for the vegetated surfaces are more capable of characterizing the SM status in the root zone (~80 cm) or even deeper, while the SMs from AMSR2/AMSR-E, GLDAS-Noah, or ERA5-land products are closer to the SM in the surface layer (the depth is less than 5 cm). The TNSTI provided favorable data supports for hydrological model simulations and showed potential advantages for agricultural refinement managements and smart agriculture.

Multi-Scale Assessment of SMAP Level 3 and Level 4 Soil Moisture Products over the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin, China

The Soil Moisture Active Passive (SMAP) mission with high-precision soil moisture (SM) retrieval products provides global daily composites of SM at 3, 9, and 36 km earth grids measured by L-band active and passive microwave sensors. The capability of passive microwave remote sensing has been recognized for the estimation of SM variations. The purpose of this work was to establish an interaction between the highly variable SM spatial distribution on the ground and the SMAP’s coarse resolution radiometer-based SM retrievals. In this work, SMAP Level 3 (L3) and Level 4 (L4) SM products are validated with in situ datasets observed from the different locations of the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin over the period of January 2018 to December 2019. The values of the unbiased root mean square error (ubRMSE) for L3 (SPL3SMP_E) SM retrievals are close to the standard SMAP mission SM accuracy requirement of 0.04 m3/m3 at the 9-km scale, with an averaged ubRMSE value of 0.041 m3/m3 (0.050 m3/m3) for descending (ascending) SM with the correlation (R) values of 0.62 (0.42) against the sparse network sites. The L4 (SPL4SMGP) Surface and Root-zone SM (RZSM) estimates show less error (ubRMSE < 0.04) and high correlation (R > 0.60) values, and are consistent with the previous SMAP-based SM estimations. The SMAP L4 SM products (SPL4SMGP) performed well compared to the L3 SM retrieval products (SPL3SMP_E). In vegetated land, the variability and compatibility of the SMAP SM estimates with the evaluation metrics for both products (L3 and L4) showed a good performance in the grassland, then in the farmland, and worst in the woodlands. Finally, SMAP algorithm parameters sensitivity analysis of the satellite products was conducted to produce time-series and highly precise SM datasets in China.

Evaluating and improving simulations of diurnal variation in land surface temperature with the Community Land Model for the Tibetan Plateau

This study evaluated and improved the ability of the Community Land Model version 5.0 (CLM5.0) in simulating the diurnal land surface temperature (LST) cycle for the whole Tibetan Plateau (TP) by comparing it with Moderate Resolution Imaging Spectroradiometer satellite observations. During daytime, the model underestimated the LST on sparsely vegetated areas in summer, whereas cold biases occurred over the whole TP in winter. The lower simulated daytime LST resulted from weaker heat transfer resistances and greater soil thermal conductivity in the model, which generated a stronger heat flux transferred to the deep soil. During nighttime, CLM5.0 overestimated LST for the whole TP in both two seasons. These warm biases were mainly due to the greater soil thermal inertia, which is also related to greater soil thermal conductivity and wetter surface soil layer in the model. We employed the sensible heat roughness length scheme from Zeng, Wang & Wang (2012), the recommended soil thermal conductivity scheme from Dai et al. (2019), and the modified soil evaporation resistance parameterization, which was appropriate for the TP soil texture, to improve simulated daytime and nighttime LST, evapotranspiration, and surface (0-10 cm) soil moisture. In addition, the model produced lower daytime LST in winter because of overestimation of the snow cover fraction and an inaccurate atmospheric forcing dataset in the northwestern TP. In summary, this study reveals the reasons for biases when simulating LST variation, improves the simulations of turbulent fluxes and LST, and further shows that satellite-based observations can help enhance the land surface model parameterization and unobservable land surface processes on the TP.

Evaluation of SMAP Level 2, 3, and 4 Soil Moisture Datasets over the Great Lakes Region

Satellite sensor systems for soil moisture measurements have been continuously evolving. The Soil Moisture Active Passive (SMAP) mission represents one of the latest advances in this regard. Thus far, much of our knowledge of the accuracy of SMAP soil moisture over the Great Lakes region of North America has originated from evaluation studies using in situ data from the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service Soil Climate Analysis Network and/or the U.S. Climate Reference Network, which provide only several in situ sensor stations for this region. As such, these results typically underrepresent the accuracy of SMAP soil moisture in this region, which is characterized by a relatively large soil moisture variability and is one of the least studied regions. In this work, SMAP Level 2‒4 soil moisture products: SMAP/Sentinel-1 L2 Radiometer/Radar Soil Moisture (SPL2SMAP_S), SMAP Enhanced L3 Radiometer Soil Moisture (SPL3SMP_E), and SMAP L4 Surface and Root-Zone Soil Moisture Analysis Update (SPL4SMAU) are evaluated over the southern portion of the Great Lakes region using in situ measurements from Michigan State University’s Enviro-weather Automated Weather Station Network. The unbiased root-mean-square error (ubRMSE) values for both SPL4SMAU surface and root zone soil moisture estimates are below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of 0.045 m3 m−3 (0.037 m3 m−3) for the surface (root-zone) soil moisture against the sparse network. The ubRMSE values for SPL3SMP_E a.m. (i.e., descending overpasses) soil moisture retrievals are close to or below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of ~0.06 m3 m−3 against the sparse network. The average ubRMSE values are ~0.05‒0.06 m3 m−3 for high-resolution SPL2SMAP_S soil moisture retrievals against the sparse network, with the skill of the baseline algorithm-based soil moisture retrievals exceeding that of the optional algorithm-based counterparts. Clearly, the skill of SPL4SMAU surface soil moisture exceeds that of the SPL3SMP_E and SPL2SMAP_S soil moisture retrievals.

An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

A method is proposed for the production of downscaled soil moisture active passive (SMAP) soil moisture (SM) data by combining optical/infrared data with synthetic aperture radar (SAR) data based on the random forest (RF) model. The method leverages the sensitivity of active microwaves to surface SM and the triangle/trapezium feature space among vegetation indexes (VIs), land surface temperature (LST), and SM. First, five RF architectures (RF1–RF5) were trained and tested at 9 km. Second, a comparison was performed for RF1–RF5, and were evaluated against in situ SM measurements. Third, two SMAP-Sentinel active–passive SM products were compared at 3 km and 1 km using in situ SM measurements. Fourth, the RF5 model simulations were compared with the SMAP L2_SM_SP product based on the optional algorithm at 3 km and 1 km resolutions. The results showed that the downscaled SM based on the synergistic use of optical/infrared data and the backscatter at vertical–vertical (VV) polarization was feasible in semi-arid areas with relatively low vegetation cover. The RF5 model with backscatter and more parameters from optical/infrared data performed best among the five RF models and was satisfactory at both 3 km and 1 km. Compared with L2_SM_SP, RF5 was more superior at 1 km. The input variables in decreasing order of importance were backscatter, LST, VIs, and topographic factors over the entire study area. The low vegetation cover conditions probably amplified the importance of the backscatter and LST. A sufficient number of VIs can enhance the adaptability of RF models to different vegetation conditions.

Soil Moisture Remote Sensing across Scales

Soil moisture plays an important role in the water, carbon, and energy cycles. We summarize the 13 articles collected in this Special Issue on soil moisture remote sensing across scales in terms of the spatial, temporal, and frequency scales studied. We also review these papers regarding the data, the methods, and the different applications discussed.