Accurately determining the freeze/thaw state (FT) is crucial for understanding land-atmosphere interactions, with significant implications for climate change, ecological systems, agriculture, and water resource management. This article introduces a novel approach to assess FT dynamics by comparing the new diurnal amplitude variations (DAV) algorithm with the traditional seasonal threshold algorithm (STA) based on the soil moisture active passive (SMAP) brightness temperature data. Utilizing soil temperature profiles from 44 sites recorded by the National Ecological Observatory Network between July 2019 and June 2022. The results reveal that the DAV algorithm demonstrates a remarkable potential for capturing FT signals, achieving an average accuracy of 0.82 (0.89 for the SMAP-FT product) across all sites and a median accuracy of 0.94 (0.92 for the SMAP-FT product) referring to soil temperature at 0.02 m. Notably, the DAV algorithm outperforms the SMAP-adopted STA in 25 out of 44 sites. The accuracy of the DAV algorithm is affected by daily temperature fluctuations and geographical latitudes, while the STA exhibits limitations in certain regions, particularly those with complex terrains or variable climatic patterns. This article's innovative contribution lies in systematically comparing the performance of the DAV and STA algorithms, providing valuable insights into their respective strengths and weaknesses.

Estimating the landscape and soil freeze-thaw (FT) dynamics in the Northern Hemisphere (NH) is crucial for understanding permafrost response to global warming and changes in regional and global carbon budgets. A new framework for surface FT-cycle retrievals using L-band microwave radiometry based on a deep convolutional autoencoder neural network is presented. This framework defines the landscape FT-cycle retrieval as a time-series anomaly detection problem, considering the frozen states as normal and the thawed states as anomalies. The autoencoder retrieves the FT-cycle probabilistically through supervised reconstruction of the brightness temperature (TB) time series using a contrastive loss function that minimizes (maximizes) the reconstruction error for the peak winter (summer). Using the data provided by the Soil Moisture Active Passive (SMAP) satellite, it is demonstrated that the framework learns to isolate the landscape FT states over different land surface types with varying complexities related to the radiometric characteristics of snow cover, lake-ice phenology, and vegetation canopy. The consistency of the retrievals is assessed over Alaska using in situ observations, demonstrating an 11% improvement in accuracy and reduced uncertainties compared to traditional methods that rely on thresholding the normalized polarization ratio (NPR).

The soil moisture active passive (SMAP) satellite mission distributes a product of CO2 flux estimates (SPL4CMDL) derived from a terrestrial carbon flux model, in which SMAP brightness temperatures are assimilated to update soil moisture (SM) and constrain the carbon cyclemodeling. While the SPL4CMDL product has demonstrated promising performance across the continental USA and Australia, a detailed assessment over the arctic and subarctic zones (ASZ) is still missing. In this study, SPL4CMDL net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (R-E) are evaluated against measurements from 37 eddy covariance towers deployed over the ASZ, spanning from 2015 to 2022. The assessment indicates that the NEE unbiased root-mean-square error falls within the targeted accuracy of 1.6 gC.m(-2).d(-1), as defined for the SPL4CMDL product. However, modeled GPP and R-E are overestimated at the beginning of the growing season over evergreen needleleaf forests and shrublands, while being underestimated over grasslands. Discrepancies are also found in the annual net CO2 budgets. SM appears to have a minimal influence on the GPP and R-E modeling, suggesting that ASZ vegetation is rarely subjected to hydric stress, which contradicts some recent studies. These results highlight the need for further carbon cycle process understanding and model refinements to improve the SPL4CMDL CO2 flux estimatesover the ASZ.

Soil Moisture (SM) is a key parameter in northern Arctic and sub-Arctic (A-SA) environments that are highly vulnerable to climate change. We evaluated six SM satellite passive microwave datasets using thirteen ground-based SM stations across Northwestern America. The best agreement was obtained with SMAP (Soil Moisture Active Passive) products with the lowest RMSD (Root Mean Square Difference) (0.07 m$3$3 m${-3}$-3) and the highest R (0.55). ESA CCI (European Space Agency Climate Change Initiative) also performed well in terms of correlation with a similar R (0.55) but showed a strong variation among sites. Weak results were obtained over sites with high water body fractions. This study also details and evaluates a dedicated retrieval of SM from SMOS (Soil Moisture and Ocean Salinity) brightness temperatures based on the $\tau -\omega$tau-omega model. Two soil dielectric models (Mironov and Bircher) and a dedicated soil roughness and single scattering albedo parameterization were tested. Water body correction in the retrieval shows limited improvement. The metrics of our retrievals (RMSD = 0.08 m$3$3 m${-3}$-3 and R = 0.41) are better than SMOS but outperformed by SMAP. Passive microwave satellite remote sensing is suitable for SM retrieval in the A-SA region, but a dedicated approach should be considered.

The tau -omega model is expanded to properly simulate L -band microwave emission of the soil-snow-vegetation continuum through a closed -form solution of Maxwell's equations, considering the intervening dry snow layer as a loss -less medium. The error standard deviations of a least -squared inversion are 0.1 and 3.5 for VOD and ground permittivity, over moderately dense vegetation and a snow density ranging from 100 to 400 kg m -3 , considering noisy brightness temperatures with a standard deviation of 1 kelvin. Using the Soil Moisture Active Passive (SMAP) satellite observations, new global estimates of VOD and ground permittivity are presented over the Arctic boreal forests and permafrost areas. In the absence of dense in situ observations of ground permittivity and VOD, the retrievals are causally validated using ancillary variables including ground temperature, above -ground biomass, tree height, and net ecosystem exchange of carbon dioxide. Time -series analyses promise that the new data set can expand our understanding of the land-atmosphere interactions and exchange of carbon fluxes over Arctic landscapes.

The soil freeze/thaw (FT) state has emerged as a critical role in the ecosystem, hydrological, and biogeochemical processes, but obtaining representative soil FT state datasets with a long time sequence, fine spatial resolution, and high accuracy remains challenging. Therefore, we propose a decision-level spatiotemporal data fusion algorithm based on Convolutional Long Short-Term Memory networks (ConvLSTM) to expand the SMAP-enhanced L3 landscape freeze/thaw product (SMAP_E_FT) temporally. In the algorithm, the Freeze/Thaw Earth System Data Record product (ESDR_FT) is sucked in the ConvLSTM and fused with SMAP_E_FT at the decision level. Eight predictor datasets, i.e., soil temperature, snow depth, soil moisture, precipitation, terrain complexity index, area of open water data, latitude and longitude, are used to train the ConvLSTM. Direct validation using six dense observation networks located in the Genhe, Maqu, Naqu, Pali, Saihanba, and Shandian river shows that the fusion product (ConvLSTM_FT) effectively absorbs the high accuracy characteristics of ESDR_FT and expands SMAP_E_FT with an overall average improvement of 2.44% relative to SMAP_E_FT, especially in frozen seasons (averagely improved by 7.03%). The result from indirect validation based on categorical triple collocation also shows that ConvLSTM_FT performs stable regardless of land cover types, climate types, and terrain complexity. The findings, drawn from preliminary analyses on ConvLSTM_FT from 1980 to 2020 over China, suggest that with global warming, most parts of China suffer from different degrees of shortening of the frozen period. Moreover, in the Qinghai-Tibet region, the higher the permafrost thermal stability, the faster the degradation rate.

A number of global surface soil moisture (SM) datasets have been retrieved from the L-band frequency Soil Moisture Active Passive (SMAP) and the Soil Moisture and Ocean Salinity (SMOS) missions to study the terrestrial water, energy, and carbon cycles. This paper presents the performance of the recently developed 9 km global SMAP product (hereafter SMAP-INRAE-BORDEAUX, SMAP-IB9). The product retrieves SM from the 9 km SMAP radiometric products using the forward model (L-MEB, L-band Microwave Emission of the Biosphere) of SMOS INRA-CESBIO (SMOS-IC) and SMOS L2 algorithms. We inter-compared SMAP-IB9 with two other products with a similar grid resolution (similar to 10 km): the SMAP Enhanced Level-3 SM dataset (SMAP-E) and the enhanced global dataset for the land component of the fifth generation of European reanalysis (ERA5-Land) with the main objective of assessing the discrepancy in accuracy between remotely sensed and model SM datasets. We found that ERA5-Land and SMAP-IB9 SM had the overall highest correlations (R = 0.62(+/- 0.15) for ERA5-Land vs. 0.60 (+/- 0.17) for SMAP-IB9 and 0.50(+/- 0.15) for SMAP-E) by comparing with the International Soil Moisture Network (ISMN) in-situ measurements from 22 networks. ERA5-Land showed better performances in the forest areas where SMAP-IB9 and SMAP-E still showed high potential in detecting the time variations of the observed SM, particularly in terms of median correlation values (0.62(+/- 0.18) for SMAP-IB9 vs. 0.66(+/- 0.16) for ERA5-and). The discrepancy in R between satellite and model SM products that were reported in some past studies has decreased to statistically insignificant levels over time. For instance, in the non-forest areas, we found that the latest versions of the SMAP SM products (SMAP-E and SMAP-IB9) had relatively comparable performances with ERA5-Land with regard to median ubRMSE (0.07(+/- 0.02) m(3)/m(3) for both SMAP-E and ERA5-Land) and R (0.59 (+/- 0.16) for SMAP-IB9 vs. 0.61(+/- 0.15) for ERA5-Land), respectively.

This paper presents a convolutional autoencoder deep learning framework for probabilistic characterization of the ground freeze-thaw (FT) dynamics in the Northern Hemisphere to enhance our understanding of permafrost response to global warming and shifts in the high-latitude carbon cycle, using Soil Moisture Active Passive (SMAP) satellite brightness temperatures (TB) observations. The autoencoder recasts the FT-cycle retrieval as an anomaly detection problem in which the peak winter (summer) represents the normal (anomaly) segments of the TB time series. The results demonstrate that the new framework outperforms the widely used fixed-thresholding of the Normalized Polarization Ratio (NPR) by learning the land surface structural and radiometric complexities that might arise in TB times series due to snow cover and vegetation. Validation against ground-based measurements over Alaska shows that the accuracy of the FT-cycle retrievals can be improved by 12%, primarily due to a marked reduction in false detection of short snowmelt episodes as ground thawing by the NPR thresholding approach.

为研究青藏高原冻土流域土壤温湿度变化特征，在唐古拉冻土流域基于多源遥感(SMAP、ESA CCI)监测数据进行表层土壤温湿度的时空分布刻画，基于地面实测站点(唐古拉冻土气象站、DFIR冻土积雪气象站)数据进行不同深度土壤温湿度的动态分析，并进行遥感与地面站点表层土壤温湿度的对比研究。结果表明，唐古拉冻土流域海拔较低处土壤温度高于海拔较高处，流域东部土壤湿度高于西部；土壤温湿度变化随着埋深加深明显滞后，且冻结期土壤湿度呈现出明显的两段式下降趋势；SMAP土壤温湿度数据与地面站点监测数据相关性较好，相比ESA CCI数据，SMAP数据准确度更高。

The freeze-thaw (F/T) process plays a significant role in climate change and ecological systems. The soil F/T state can now be determined using microwave remote sensing. However, its monitoring capacity is constrained by its low spatial resolution or long revisit intervals. In this study, spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data with high temporal and spatial resolutions were used to detect daily soil F/T cycles, including completely frozen (CF), completely thawed (CT), and F/T transition states. First, the calibrated Cyclone Global Navigation Satellite System (CYGNSS) reflectivity was used for soil F/T classification. Compared with those of soil moisture active and passive (SMAP) F/T data and in situ data, the detection accuracies of CYGNSS reach 75.1% and 81.4%, respectively. Subsequently, the changes in spatial characteristics were quantified, including the monthly occurrence days of the soil F/T state. It is found that the CF and CT states have opposite spatial distributions, and the F/T transition states distribute from the east to the west and then back to the east of the Qinghai-Tibet Plateau, which may be due to varying diurnal temperatures in different seasons. Finally, the first day of thawing (FDT), last day of thawing, and thawing period of the F/T year were analyzed in terms of the changes in temporal characteristics. The temporal variation of thawing is mainly different between the western and eastern parts of the Tibetan Plateau, which is in agreement with the spatial variation characteristics. The results demonstrate that the CYGNSS can accurately detect the F/T state of near-surface soil on a daily scale. Moreover, it can complement traditional remote sensing missions to improve the F/T detection capability. It can also expand the applications of GNSS-R technology and provide new avenues for cryosphere research.