Search

Two L-band passive microwave satellite sensors, onboard the Soil Moisture and Ocean Salinity (SMOS) launched in 2009 and Soil Moisture Active Passive (SMAP) launched in 2015, are specifically designed for surface soil moisture (SM) monitoring. The first global continuous fused L-band satellite SM product based on SMOS and SMAP observations (SMOS-SMAP-INRAE-BORDEAUX, the so-called Fused-IB) was recently released to the public. Currently, the performance of Fused-IB has only been evaluated collectively over the entire data records in the study period, without specific evaluation for individual seasons. To fill this gap, this study intercompared the Fused-IB and the enhanced SMAP-L3 version 6 (SMAP-E) SM products against in situ SM data from the International Soil Moisture Network (ISMN) from 2016 to 2020 regarding the whole period and different seasons. We aim to investigate the performance of these two products at different time scales and to explore the potential eco-hydrological factors (i.e., precipitation and vegetation) driving their seasonal variations. Results show that both SM products are in good agreement with the in situ measurements, demonstrating high median correlation (R) and low ubRMSD (median R = 0.70 and ubRMSD = 0.058 m3/m3 for Fused-IB vs. R = 0.68 and ubRMSD = 0.059 m3/m3 for SMAP-E) during 2016–2020. For most land use and land cover (LULC) types, Fused-IB outperformed SMAP-E with higher accuracy and lower errors, particularly in forests, partly due to the advantage of the robust SMAP-IB (SMAP-INRAE-BORDEAUX) algorithm used to generate Fused-IB in forests, which avoids the pronounced saturation effects of vegetation optical depth caused by relying on optical information. Besides, both products had superior performances across most LULC types in summer (JJA) and autumn (SON), yet increased uncertainties were observed in forests, grasslands, and croplands during spring (MAM) and winter (DJF). These uncertainties could be mainly attributed to the effects of vegetation growth in forests, grasslands and croplands, and the interception of water from rainfall events in grasslands. The results of this study can serve as a reference for algorithm developers to enhance the accuracy of SM and thus promote hydro-meteorological applications that benefit from L-band radiometer soil moisture products.

Vegetation Optical Depth (VOD), a vegetation parameter that quantifies the extinction effect of microwaves penetrating the canopy, plays a crucial role in global-scale biomass monitoring and climate change research. However, the spatial gridding of existing long-term VOD products is relatively coarse (approximately 25 km), with restrictions on their application at a regional scale. High-resolution active-microwave proxies and optical vegetation indices can potentially be used to disaggregate coarse-resolution VOD, but it is unclear which proxy is optimal. In this paper, the Normalized Difference Vegetation Index (NDVI) and active-microwave proxies (VH, VV, and cross-polarization ratio CR) from Sentinel-1 were quantitatively assessed with VOD products at various frequencies (L-/C-/X-VOD) across the contiguous United States (U.S.). The results showed that VH (R = 0.80) and NDVI (R = 0.77) exhibit a high spatial correlation with L-VOD products. For temporal correlation, NDVI had the highest overall performances with all VOD products, but good correlations were also achieved with CR and, to a lesser extent, VH. Further comparisons of the performance between Brightness Temperature (TB) and VOD revealed that while TB displayed a strong temporal correlation with active-microwave proxies, its spatial correlations with such proxies were low. In contrast, VOD had good correlations both temporally and spatially with active-microwave proxies (e.g., VH). These evidences suggested that the downscaling of VOD using the combination of VH and other proxies could be an alternative promising method to estimate high-resolution VOD.

Over the past 4 decades, Southwest China has the fast vegetation growth and aboveground biomass carbon (AGC) accumulation, largely attributed to the active implementation of ecological projects. However, Southwest China has been threatened by frequent extreme drought events recently, potentially countering the expected large AGC increase caused by the ecological projects. Here, we used the L-band vegetation optical depth to quantify the AGC dynamics over Southwest China during the period 2013-2021. Our results showed a net AGC sink of 0.064 [0.057, 0.077] Pg C year−1 (the range represents the maximum and minimum AGC values), suggesting that Southwest China acted as an AGC sink over the study period. Note that the AGC loss of 0.113 [0.101, 0.136] Pg C year−1 was found during 2013-2014, which could mainly be attributed to the negative influence of extreme droughts on AGC changes in Southwest China, particularly in the Yunnan province. For each land use type (i.e., dense forests, persistent forests, nonforests, afforestation, and forestry), the largest AGC stock increase of 0.032 [0.028, 0.036] Pg C year−1 was found in nonforests, owing to their widespread land cover rate over Southwest China. For AGC density (i.e., AGC per unit area), the afforestation areas showed the largest AGC density increase of 0.808 [0.724, 0.985] Mg C ha−1 year−1, reflecting the positive effect of afforestation on AGC increase. Moreover, the karst areas exhibited a higher increasing rate of AGC density than nonkarst areas, suggesting that the karst ecosystems have a high carbon sink capacity over Southwest China.

Soil temperature plays an essential role in the permafrost thermal state and degradation process. Especially the soil temperatures at 10 cm and 50 cm depths in the active layer, which are much easier to be observed in situ, have great effects on the surface water cycles and vegetation, and could be used as the upper boundary for permafrost models to simulate the thermal state of the permafrost and active layer thicknesses. However, due to the limitations of the observation data, there are still large uncertainties in the soil temperature data, including at these two depths, in the permafrost region of Qinghai–Tibet Plateau (QTP). In this study, we evaluated and calibrated the applicability of four daily shallow soil temperature datasets (i.e., MERRA-2, GLDAS-Noah, ERA5-Land, and CFSR) by using the in situ soil temperature data from eight observation sites from 2004 to 2018 in the permafrost region along the Qinghai–Tibet Engineering Corridor. The results revealed that there were different uncertainties for all four sets of reanalysis data, which were the largest (Bias = −2.44 °C) in CFSR and smallest (Bias= −0.43 °C) in GLDAS-Noah at depths of 10 cm and 50 cm. Overall, the reanalysis datasets reflect the trends of soil temperature, and the applicability of reanalysis data at 50 cm depth is better than at 10 cm depth. Furthermore, the GLDAS-Noah soil temperatures were recalibrated based on our observations using multiple linear regression and random forest models. The accuracy of the corrected daily soil temperature was significantly improved, and the RMSE was reduced by 1.49 °C and 1.28 °C at the depth of 10 cm and 50 cm, respectively. The random forest model performed better in the calibration of soil temperature data from GLDAS-Noah. Finally, the warming rates of soil temperature were analyzed, which were 0.0994 °C/a and 0.1005 °C/a at 10 cm and 50 cm depth from 2004 to 2018, respectively.

Soil moisture (SM) products presently available in permafrost regions, especially on the Qinghai–Tibet Plateau (QTP), hardly meet the demands of evaluating and modeling climatic, hydrological, and ecological processes, due to their significant bias and low spatial resolution. This study developed an algorithm to generate high-spatial-resolution SM during the thawing season using Sentinel-1 (S1) and Sentinel-2 (S2) temporal data in the permafrost environment. This algorithm utilizes the seasonal backscatter differences to reduce the effect of surface roughness and uses the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) to characterize vegetation contribution. Then, the SM map with a grid spacing of 50 m × 50 m in the hinterland of the QTP with an area of 505 km × 246 km was generated. The results were independently validated based on in situ data from active layer monitoring sites. It shows that this algorithm can retrieve SM well in the study area. The coefficient of determination (R2) and root-mean-square error (RMSE) are 0.82 and 0.06 m3/m3, respectively. This study analyzed the SM distribution of different vegetation types: the alpine swamp meadow had the largest SM of 0.26 m3/m3, followed by the alpine meadow (0.23), alpine steppe (0.2), and alpine desert (0.16), taking the Tuotuo River basin as an example. We also found a significantly negative correlation between the coefficient of variation (CV) and SM in the permafrost area, and the variability of SM is higher in drier environments and lower in wetter environments. The comparison with ERA5-Land, GLDAS, and ESA CCI showed that the proposed method can provide more spatial details and achieve better performance in permafrost areas on QTP. The results also indicated that the developed algorithm has the potential to be applied in the entire permafrost regions on the QTP.

Vegetation optical depth (VOD), as a microwave-based estimate of vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. However, current global operational VOD products have a coarse spatial resolution (~25 km), which limits their use for agriculture management and vegetation dynamics monitoring at regional scales (1–5 km). This study aims to retrieve high-resolution VOD from the C-band Sentinel-1 backscatter data over a grassland of the Heihe River Basin in northwestern China. The proposed approach used an analytical solution of a simplified Water Cloud Model (WCM), constrained by given soil moisture estimates, to invert VOD over grassland with 1 km spatial resolution during the 2018–2020 period. Our results showed that the VOD estimates exhibited large spatial variability and strong seasonal variations. Furthermore, the dynamics of VOD estimates agreed well with optical vegetation indices, i.e., the mean temporal correlations with normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) were 0.76, 0.75, and 0.75, respectively, suggesting that the VOD retrievals could precisely capture the dynamics of grassland.

The freeze–thaw (F-T) cycle of the active layer (AL) causes the “frost heave and thaw settlement” deformation of the terrain surface. Accurately identifying its amplitude and time characteristics is important for climate, hydrology, and ecology research in permafrost regions. We used Sentinel-1 SAR data and small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain the characteristics of F-T cycles in the Zonag Lake-Yanhu Lake permafrost-affected endorheic basin on the Qinghai-Tibet Plateau from 2017 to 2019. The results show that the seasonal deformation amplitude (SDA) in the study area mainly ranges from 0 to 60 mm, with an average value of 19 mm. The date of maximum frost heave (MFH) occurred between November 27th and March 21st of the following year, averaged in date of the year (DOY) 37. The maximum thaw settlement (MTS) occurred between July 25th and September 21st, averaged in DOY 225. The thawing duration is the thawing process lasting about 193 days. The spatial distribution differences in SDA, the date of MFH, and the date of MTS are relatively significant, but there is no apparent spatial difference in thawing duration. Although the SDA in the study area is mainly affected by the thermal state of permafrost, it still has the most apparent relationship with vegetation cover, the soil water content in AL, and active layer thickness. SDA has an apparent negative and positive correlation with the date of MFH and the date of MTS. In addition, due to the influence of soil texture and seasonal rivers, the seasonal deformation characteristics of the alluvial-diluvial area are different from those of the surrounding areas. This study provides a method for analyzing the F-T cycle of the AL using multi-temporal InSAR technology.

Spatial information of particle size fractions (PSFs) is primary for understanding the thermal state of permafrost in the Qinghai-Tibet Plateau (QTP) in response to climate change. However, the limitation of field observations and the tremendous spatial heterogeneity hamper the digital mapping of PSF. This study integrated log-ratio transformation approaches, variable searching methods, and machine learning techniques to map the surficial soil PSF distribution of two typical permafrost regions. Results showed that the Boruta technique identified different covariates but retained those covariates of vegetation and land surface temperature in both regions. Variable selection techniques effectively decreased the data redundancy and improved model performance. In addition, the spatial distribution of soil PSFs generated by four log-ratio models presented similar patterns. Isometric log-ratio random forest (ILR-RF) outperformed the other models in both regions (i.e., R2 ranged between 0.36 to 0.56, RMSE ranged between 0.02 and 0.10). Compared with three legacy datasets, our prediction better captured the spatial pattern of PSFs with higher accuracy. Although this study largely improved the accuracy of spatial distribution of soil PSFs, further endeavors should also be made to improve model accuracy and interpretability for a better understanding of the interaction and processes between environmental predictors and soil PSFs at permafrost regions.

Permafrost is important for the climatic, hydrological, and ecological processes on the Qinghai- Xizang (Tibet) Plateau (QXP). The changing permafrost and its impact have been attracting great attention worldwide never before, and more observational and modeling approaches are need to promote an understanding of permafrost thermal state and climatic conditions on the QXP. However, there were almost no synthesis dataset on the permafrost thermal state and climate background on the QXP, but were sporadically reported in different literatures due to the difficulties to access to and work in this region, where the weather is severe, and environmental conditions are harsh and the topographic and morphological features are complex. In this study, a comprehensive dataset under quality controlled consisting of long-term meteorological, ground temperature, soil moisture and soil temperature data were compiled from an integrated, distributed and multiscale observation network in the permafrost regions of the Cryosphere Research Station on the QXP. Meteorological data were observation by automatic meteorological stations from a comprehensive observation network. The soil temperature and moisture data were collected from an integrated observation system in the active layer. Deep ground temperature was observed from boreholes. These datasets were helpful for the scientists with multiple study fields (i.e., climate, cryospheric, ecology and hydrology, meteorology science), which will greatly promote the verification, development and improvement of the hydrological model, land surface process model and climate model on the QXP. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn/en/disallow/789e838e-16ac-4539-bb7e-906217305a1d/, doi: 10.11888/Geocry.tpdc.271107). [ABSTRACT FROM AUTHOR]