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The understanding and prediction of the variability of the hydrological state of watersheds across the planet is growing, as water is a fundamental human need and therefore important for science and society. In the last 20 years, significant advances have been made toward hydrological modeling of large river basins, and also continental to global-scale land areas. Remote sensing has been widely used in hydrology because, in addition to being a clear advantage in regions with a poor monitoring network, it has proven to be suitable for use in global and continental hydrological applications.The estimation of river discharge is of paramount importance, as it is considered an aggregator of all water cycle processes in the basin. On the one hand, estimates of discharge solely from space remain limited because they are not the primary focus of current satellite missions. On the other hand, simulations of hydraulic variables have been performed with large-scale hydrologic and hydrodynamic models, but the accuracy of their estimates can be improved with recent techniques such as data assimilation (DA). DA techniques have been developed to use remotely sensed datasets to obtain the best estimate of the current state of a system by optimally combining observations and large-scale hydrological models. Recent studies have also demonstrated the advantages of assimilating several types of datasets at the same time, which can help to further constrain the model state variables to be more physically representative.Thus, the main objective of this research is to develop a proof-of-concept for estimating hydraulic variables such as discharge and water level by assimilating multiple remotely sensed datasets into a large-scale hydrologic and hydrodynamic model. Experiences on the assimilation of different mission datasets into a large-scale hydrological model are discussed, including radar altimetry-derived water level from JASON, ENVISAT and Sentinel missions, terrestrial water storage from GRACE mission, flooded area extent from SWAMPS database and soil moisture from the SMOS mission.To develop our proof-of-concept, the Amazon as the study area. We used the hydrologic-hydrodynamic MGB model and the Local Ensemble Kalman Filter as the DA method as it has been commonly used in hydrologic models. Different localization and multivariable assimilation techniques were implemented to improve the effectiveness of the DA.The results indicate that the multi-mission assimilation approach is able to smooth/average the improvement of the state variables of the model, such as discharge and water level anomaly, compared to the experiment of assimilating the mission datasets individually. This proof-of-concept allows us to spatialize the improvement of the dynamics of hydrological-hydrodynamic variables based on large-scale hydrologic modeling and DA from global remote sensing sources only, without requiring in-situ data. As our proof-of-concept is based on datasets globally available and a hydrologic-hydrodynamic model that can be applied almost everywhere, it is fully replicable in any region of the world and represents a great potential for regional to continental studies.

Agriculture is the largest consumer of water worldwide, accounting for about 70% of the global freshwater withdrawals. Thus, crop water use efficiency and impacts of water stress on crop water consumption are the key concerns for agricultural water management.Present study investigates the variability of evapotranspiration (ET) and crop water use efficiency by integrating very high spatial resolution (1 – 4 m) thermal infrared (TIR) data from airborne measurements and visible to near infrared data from Planet satellite with a numerical water-energy balance model and a diagnostic surface energy balance model. The analysis is done for an intensive agriculture area in central Italy near the city of Modena, where several fruit trees fields are present along with fresh vegetables. An intensive airborne campaign was organized in the summer of 2022 for three consecutive days in July. A hyperspectral TIR camera (Telops Hyper-Cam LW) has been operated at a spectral resolution of 8 cm-1, resulting in 64 wavebands, and covering a wavelength range of 850 cm-1 to 1350 cm-1 (7,39 µm – 11,8 µm). During the 3 days of flight acquisitions, three overpasses per day are planned: 9:00, 12:30 and 16:00 h, respectively and two areas were intensively surveyed at both 4 and 1 m spatial resolution. Planet data at 3.7 m spatial resolution were used to derive different vegetation indices, such as vegetation fraction coverage, NDVI and leaf area index. During airborne overpasses ground data of spectral reflectance, vegetation variables, LST and soil water content (SM) were collected in different fields. In addition, two different pear trees fields were monitored with an eddy covariance station and soil moisture profile measurements, respectively.To investigate the diurnal and spatial patterns of evapotranspiration, soil moisture variability and crop water use efficiency, we used two numerical models: the surface energy balance model STIC based on Penman-Monteith and Shuttleworth-Wallace (Mallick et al., 2018) and the water-energy balance model FEST-EWB which computes continuously in time and is distributed in space soil moisture and evapotranspiration fluxes solving for a land surface temperature that closes the energy–water balance equations (Corbari et al., 2011).Differences and similarities in ET estimates have been analysed from the two models for different soil moisture conditions and crop types, considering crop water use efficiency and water stress, and have been compared to eddy covariance measurements for accuracy evaluation considering both instantaneous and daily data. The assimilation of instantaneous estimates of ET into the water-energy balance model allowed to directly derived soil moisture maps at high spatial resolution which have been found in agreement with ground SM measurements.

Soil carbon storage is a well identified climate change mitigation solution. The extensive in-situ monitoring of the soil carbon storage in cropland for agricultural policy and offset carbon markets is prohibitive, especially at intra-field scale. For this reason, comprehensive Monitoring, Reporting and Verification (MRV) of soil carbon and its explanatory variables at large scale needs to rely on remote sensing and modelling tools that provide the spatio-temporal dynamics of the carbon budget and it’s components at high resolution with associated uncertainties. In this paper, we present AgriCarbon-EO v1.0: an end-to-end processing chain that enables the estimation of carbon budget components of major crops and cover crops at intra-field resolution (10 m) and large scale (over 110×110 km) by assimilating remote sensing data in physically-based radiative transfert and agronomic models. The data assimilation in AgriCarbon-EO is based on a novel Bayesian approach that combines Normalised Importance Sampling (NIS) and Look-Up Table (LUT) generation. This approach propagates the 10 m uncertainties across the processing chain from the reflectances to the output variables. The chain considers as input a land cover map, multi-spectral reflectance maps from the Sentinel-2 and Landsat-8 satellites, and daily weather forcing. The PROSAIL radiative transfer model is inversed in a first step to obtain Green Leaf Area Index (GLAI). The GLAI time series are then assimilated into the SAFYE-CO2 crop model taking into consideration their uncertainty. The chain is applied over winter wheat in the south-west of France during the cropping seasons 2017 and 2019. We compare the results against the net ecosystem exchange measured at the FR-AUR ICOS site (RMSE = 1.69 - 2.4 gC m−2 , R2 = 0.88 - 0.88), biomass (RMSE = 250 g m−2 , R2 = 0.9), and combine harvester yield maps. We quantify the difference between pixel and field and pixel scale simulations of biomass (bias = -47 g m−2 , -39 % variability), and the impact of the number of remote sensing acquisitions on the outputs (-66 % of mean uncertainty of biomass). Finally, we conduct a coherency analysis at regional scale to test the consistency of the observed patterns with soil texture, altitude and exposition variability. Results show higher biomass for higher clay soils and earlier emergence and senescence for south western exposition.

The agricultural sector is the biggest and least efficient water user, accounting for around 80% of total water use in South Europe, which will be further impacted by climate change in the incoming years. Precision agriculture tools are then needed to increase water use efficiency.Here, the proposed system couples together remotely sensed land surface temperature (LST), leaf area index (LAI) and ground soil moisture data (SM) with a pixel wise crop-water-energy balances model, for improving irrigation management. The SAFY (Simple Algorithm for Yield) crop model has been fully coupled with the energy water balance FEST-EWB model, exchanging in a double direction the LAI evolution in time from SAFY, which is used by FEST-EWB for evapotranspiration computation, while FEST-EWB provides soil moisture (SM) and LST to SAFY model for constraining crop growth.A data assimilation framework, based on the Ensemble Kalman filter approach, is implemented to reduce the requirements for parameters calibration, either for soil assimilating satellite LST and for crop growth using LAI. This framework allows overcoming the issues related to crop exposure to shocks due extreme events non-reproducible by the model alone, as well as nutrient lack, crops hybrids or precise amount of irrigation water.The FEST-EWB-SAFY model has been applied in two Irrigation Consortia in the North and South of Italy which differ for climate and agricultural practices, using data from Sentinel2, Landsat 7 and 8 satellites. The model has then been validated in specific fields where ground measurements of evapotranspiration, soil moisture and crop yields are available.Overall, the results suggested that the under-calibrated model estimates of LST, LAI, SM and yield are enhanced through the assimilation of satellite data, suggesting the potential for improving irrigation management at both field and Irrigation Consortium scales.

The flow conditions (e.g., river network) for rivers and open channels are often forced into hydrogeological models that use a constant horizontal grid resolution without correction for grid mismatching. As a result, the flow velocity will be significantly underestimated if the width of rivers is substantially narrower than the grid size of these models. Furthermore, the exchange between the river and the subsurface is overestimated, resulting in an erroneously large vertical exchange.In response to this challenge, in this work, the subscale channel flow is approximated in the kinematic wave equation by a scaled roughness coefficient. A relationship between grid cell size and river width is used for this purpose, which follows a simplified modification of the Manning-Strickler equation. In addition, the exchange between the subsurface and the river, as well as the rate of ex- and in-filtration, are scaled across river beds based on grid resolution. As a result, even though the grid size is relatively large, the exchange rates are corrected across river beds. The effectiveness of the scaling of river parametrization is validated against groundwater gauges and remote sensing-based surface soil moisture in a fully coupled subsurface-land surface ParFlow-CLM at a spatial resolution of 0.055° (~6 km) over the Upper Rhine Basin. The validity of the results is examined through an innovative application of the First Order Reliability Method (FORM) for the time period 2012-2014. Results indicate that the scaling approach improves the estimates of soil moisture, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. This improvement is achieved (SM RMSE reduction from 0.03 to 0.005) due to the effective impacts of the scaling river parametrization on SM estimation. FORM results show that the accuracy of ParFlow-CLM soil moisture simulations by using scaling approach is more than 95, 89, 85 and 92 percent for Autumn, Winter, April and Summer, respectively. The scaling river parametrization also shows overall improvements in groundwater level estimation, particularly over the central and northern regions where the groundwater level is shallow.

Achieving the United Nations Sustainable Development Goal 2 that addresses food security and sustainable agriculture requires the promotion of readily transferable and scalable agronomical solutions. The combination of high-resolution remote sensing data, field information, and physical models is identified as a robust way of answering this requirement. Here, we present the AgriCarbon-EO tool, a decision support system that provides the yield, biomass, water and carbon budget components of agricultural fields at a 10m resolution and at a regional scale. The tool assimilates high resolution optical remote sensing data from Copernicus Sentinel-2 satellites into a radiative transfer model and a crop model. First, the application of a spatial Bayesian retrieval approach to the PROSAIL radiative transfer model provides Leaf Area Index (LAI) with its associated uncertainty. Second, LAI is assimilated into the SAFYE-CO2 crop model using a temporal Bayesian retrieval that enables the calculation of the yield, biomass, carbon and water budgets components with their associated uncertainties. In addition to remote sensing data, input datasets of crop types, weather and soil data are used to constrain the system. The concise weather data is provided from local weather stations or weather forecasts and is used to force the crop model (SAFYE-CO2) dynamics. The soil data are used in two folds. First to better parametrize the soil emissions in the radiative model retrievals and second to parametrise the water infiltration in the soil module of the crop model. The AgriCarbon-EO tool has been optimized to enable the computation of the yield, carbon, and water budget at high spatial resolution (10m) and large scale (100km2). The model is applied over the South-West of France covered by 3 Sentinel-2 tiles for major crops (wheat, maize, sunflower). The outputs are validated over experimental plots for biomass, yield, soil moisture, and CO2 fluxes located all in the South-West of France. The experimental sites include the FR-AUR and FR-LAM ICOS sites and 22 cropland fields (biomass sampling). The validation exercise is done for the 2017-2018 and 2019-2020 cultural years. We show the added value of the use of high resolution in driving the crop model to take into account the impact of complex processes that are embedded in the LAI signal like vegetation water stress, disease, and agricultural practices. We show that the system is capable of providing the yield, carbon, and water budget of major crops accurately. At the regional scale, we give global estimates of the carbon budget, water needs, and yields per crop type. We present the impact of intra-plot heterogeneity in the estimation of yield and the annual carbon and water budget showing the added value for high-resolution intra-plot modeling.

Increasing soil carbon stocks has been identified as a major climate change mitigation solution. As a consequence, an objective of 4/1000 yearly increment in soil carbon stocks has been proposed at the COP21. Sustainable agriculture provides several solutions to meet this objective and among those solutions, the implementation of cover crops has been identified as most efficient. Currently, a comprehensive modeling tool that takes into account the major bio-geophysical processes with associated uncertainties, while assimilating frequent high-resolution observations at large scale could allow accounting for the effect of cover crops on the carbon budget in a realistic way. In this study, we quantify the components of the carbon budget at high resolution and we analyse the effect of cover crops. Computations are based on the newly developed AgriCarbon-EO tool which assimilates full resolution (10-20m) Sentinel-2 optical data into a radiative transfer model (PROSAIL), and a crop model (SAFYE-CO2). The assimilation scheme is based on a Bayesian approach which provides the retrieved biogeophysical variables with their associated uncertainties. Uncertainties are essential when determining the carbon stocks. For instance, the future European Common Agricultural Practice (CAP) may take into consideration the uncertainty of the determination of the soil carbon stocks changes in the evaluation of the subsidies. The main inputs of the computations are weather data, soil texture maps, crop maps and surface reflectances. The Sentinel-2 Leaf Area Index (LAI) are obtained from those Sentinel-2 surface reflectance by inverting the PROSAIL model. These are then assimilated into the SAFY_CO2 model to determine the carbon budget components. To validate our approach, we implemented the AgriCarbon-EO tool over a set of plots in south-western France over which we dispose of biomass measurements for cover crops in wheat/cover crop/maize rotations for 2017-2018 and 2019-2020 agricultural seasons. Also, the CO2 fluxes are validated against eddy covariance flux measurements in the same context. Our study shows that the cover crops allow on average 250gC/m² of organic carbon with a high spatial heterogeneity. This has important implications regarding the dynamic of carbon storage in agronomic soils and demonstrates the importance of high-resolution agronomic modeling.

The conflicting use of water is becoming more and more evident, also in regions that are traditionally rich in water. With the world’s population projected to increase to 8.5 billion by 2030, the simultaneous growth in income will imply a substantial increase in demand for both water and food. Climate change impacts will further stress the water availability enhancing also its conflictual use. The agricultural sector is the biggest and least efficient water user, accounts for around 24% of total water use in Europe, peaking at 80% in the southern regions.This paper shows the implementation of a system for real-time operative irrigation water management at high spatial and temporal able to monitor the crop water needs reducing the irrigation losses and increasing the water use efficiency, according to different agronomic practices supporting different level of water users from irrigation consortia to single farmers. The system couples together satellite (land surface temperature LST and vegetation information) and ground data, with pixel wise hydrological crop soil water energy balance model. In particular, the SAFY (Simple Algorithm for Yield) crop model has been coupled with the pixel wise energy water balance FEST-EWB model, which assimilate satellite LST for its soil parameters calibration. The essence of this coupled modelling is that the SAFY provides the leaf area index (LAI) evolution in time used by the FEST-EWB for evapotranspiration computation while FEST-EWB model provides soil moisture (SM) to SAFY model for computing crop grow for assigned water content.The FEST-EWB-SAFY has been firstly calibrated in specific fields of Chiese (maize crop) and Capitanata (tomatoes) where ground measurements of evapotranspiration, soil moisture and crop yields are available, as well as LAI from Sentinel2-Landsat 7 and 8 data. The FEST-EWB-SAFY model has then been validated also on several fields of the RICA farms database in the two Italian consortia, where the economic data are available plus the crop yield. Finally, the modelled maps of LAI have then been validated over the whole Consortium area (Chiese and Capitanata) against satellite data of LAI from Landsat 7 and 8, and Sentinel-2.Optimized irrigation volumes are assessed based on a soil moisture thresholds criterion, allowing to reduce the passages over the field capacity threshold reducing the percolation flux with a saving of irrigation volume without affecting evapotranspiration and so that the crop production. The implemented strategy has shown a significative irrigation water saving, also in this area where a traditional careful use of water is assessed.The activity is part of the European project RET-SIF (www.retsif.polimi.it).

The Soil Moisture and Ocean Salinity (SMOS) satellite, launched in 2009 by ESA, has provided, for the first time, systematic passive L-band (1.4 GHz) measurements from space with a spatial resolution of ~ 40 km. SMOS data are an essential component of the ESA Climate Change Initiative (CCI) for ocean salinity and soil moisture and they are used by the CCI biomass. A specific SMOS neural network soil moisture product is assimilated operationally at the European Centre for Medium Range Weather Forecasts (ECMWF). L-band surface SM measurements have also been used to estimate root zone soil moisture, to derive drought indices, to enable food security monitoring and to improve satellite precipitation estimates. SMOS data have also been used to detect frozen soils, thin ice-sheets over the ocean and ice melting in Antarctica and Greenland.Different studies on scientific and operational applications of L-band radiometry have shown the need of the continuity of L-band observations with an increased resolution with respect to the current generation of sensors. Resolutions from 1 km to 10 km would be a breakthrough for many applications over ocean, land and ice. One approach to obtain those resolutions could be downscaling coarse resolution data using an auxiliary dataset with higher resolution. However, using airborne data, we will show that the accuracy of the data downscaled to 1 km decreases significantly when the initial native resolution is 40 km with respect to downscaling from initial resolutions of 5-10 km. We will present two instrumental concepts to reach native resolutions of 5-10 km.The SMOS-HR mission project, completed the Phase 0 study at the French Centre National d’Etudes Spatiales (CNES) with contributions from Airbus Defence & Space and CESBIO. The goal was to ensure the continuity of L-band measurements while increasing the spatial resolution to ~10 km, which requires a typical antenna size of ~18 meters. Taking into account the difficulty of deploying a real aperture of this size in space and the successful alternative approach used by SMOS, SMOS-HR will perform aperture synthesis using an array of 230 small antennas distributed in a cross with four 12 m arms. During the Phase A study (ongoing at CNES) a mission concept with a central carrier surrounded by a swarm of nanosatellites will also be studied.

Downscaling of L-Band microwave using Sentinel-3 land surface temperatureA large number of agricultural and water management applications require sub-kilometric frequent revisit surface Soil Moisture (SM) observations. L-band passive radiometer acquisitions are especially suited for soil moisture retrieval since they are less susceptible to attenuation by vegetation than active methods and are less sensitive to surface roughness than C or X – bands. However, while providing a 3 days global coverage for ascending and descending orbits with the currently available missions (SMOS/SMAP) the spatial resolution of the space-borne L-band radiometers is of ~40 km. Downscaling technics have been extensively used to increase the resolution of the SM products by combining data from optical (Merlin et al. 2012) and SAR sensors (Tomer et al. 2015). Here, we use land surface temperature data from the Sentinel-3 sensors to disaggregate the SMOS SM product into the DISPATCH algorithm. DISPATCH is based on the link between the evaporative efficiency and the SM (Merlin et al. 2010). The exercices is applied over Italy and compared to in-situ SM observations and model outputs over two sites in Northern and southrn Italy (Chiese and Capitanata). The algorithm is run using MODIS and the Sentinel-3 data for a comparative results. The potential of the combined use of Sentienl-3/MODIS and SMOS/SMAP is also investigate. The current study extends the application of an existing algorithm to new operational data from the Copernicus programe while accessing the advantages and ceavates.

Numerous surface soil moisture (SSM) products are available from remote sensing, ranging different spatial and temporal resolutions. Varying techniques are employed to retrieve SSM and different spatial scales highlight different distributions. Notwithstanding this variety between the available data, all of them should be coherent with the recorded rainfall and irrigation.In this work we have crossed recorded precipitations with a number of SSM products deriving from remote sensing: Soil Moisture Ocean Salinity (SMOS) mission, Soil Moisture Active Passive (SMAP) mission, European Space Agency Climate Change Initiative (ESA-CCI) products, Copernicus Global Land Operations product, a Neural Network SSM retrieval algorithm and AMSR-2 data.All the dataset products have been compared with recorded precipitation from on-ground stations over two agricultural sites in Italy: one in the north, near Lake Garda (Chiese Irrigation Consortium) and the other in the south-east in the Apulia region (Capitanata Irrigation Consortium).In both cases, a first SSM-rain comparison through well-established indexes (Pearson and Spearman correlations) has not yielded encouraging results.Then, a methodology has been developed to determine whether the variation of SSM is consistent with the presence/absence of precipitation. An Agreement Index (AI) has been derived as a way to measure the coherency between SSM and precipitation. Any time a measure of SSM is available, a positive or negative value for the AI is recorded, according to the rainfall registered since the previous measurement. During the irrigation season (March through September), the presence of this artificial input of water into the system is also taken into account. For every year, the proportion between “coherent” SSM-rainfall pairings (positive AIs) and “non-coherent” pairings (negative AIs) has been computed.This method is applied to all SSM products in the dataset, and results are compared. When aggregating the results for all the pixels within the irrigation consortia, all seem to align to a similar proportion between “coherent” and “non-coherent” SSM-rainfall pairings, notwithstanding the wide variety of data types, spatial resolutions and retrieval methods. However, even if the overall performances of the products are similar, each shows different spatial distributions, as each product is influenced differently by the physical features of the different areas.

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infra-red vegetation indices. This study is devoted to the characterisation of a new VOD data set obtained from SMOS (Soil Moisture and Ocean Salinity) satellite observations at L-band (1.4 GHz). Three different SMOS L-band VOD (L-VOD) data sets (SMOS Level 2, Level 3 and SMOS-IC) were compared with data sets on tree height, visible/infra-red indexes (NDVI, EVI), cumulated precipitation, and above ground biomass (AGB) for the African continent. For all relationships, SMOS-IC showed the lowest dispersion and highest correlation. Overall, we found a strong (R > 0.85) correlation with no clear sign of saturation between L-VOD and four AGB data sets. The relationship linking L-VOD to tree height (R = 0.87) and Baccini's AGB (R = 0.94) was strong and linear. The relationships between L-VOD and three other AGB data sets were linear per land cover class, but with a changing slope depending on the land cover type. For low vegetation classes, the annual mean of L-VOD spans a range from 0 to 0.7 and it is linearly correlation with the amount of the average annual precipitations. SMOS L-VOD showed a higher sensitivity to AGB as compared to NDVI and K/X/C-VOD (VOD measured, respectively, at 19, 10.7, and 6.9 GHz). The results showed that although the spatial resolution of L-VOD is coarse (~ 40 km), the high temporal frequency and sensitivity to AGB makes SMOS L-VOD a very promising index for large scale monitoring of the vegetation status, in particular biomass.

We present a unique meteorological and snow observational dataset in Mount-Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km2 and an elevation up to 3088 m). The dataset consists of: (1) continuous meteorological and snow height observations; (2) snowpack field measurements; and (3) medium resolution satellite snow cover data. The continuous meteorological measurements at three automatic weather stations (MZA 2296 m, LAQ 1840 m, and CED 2834 m a.s.l.) include surface air temperature and humidity, precipitation, wind speed and direction, incoming and reflected shortwave irradiance, and snow height, at 30 minute intervals for the snow seasons (November–June) between 2011 and 2016 for MZA and 2014–2016 for CED and LAQ. Precipitation data was filtered and corrected for Geonor undercatch. Observations of snow height (HS), snow water equivalent, and snow density were collected at 30 snow courses located at elevations between 1300 and 2900 m a.s.l. during the two snow seasons 2014–2016 with an average revisit time of 11 days. Daily gap-free snow cover extent (SCA) and snow cover duration (SCD) maps derived from MODIS snow products are provided for the same period (2011–2016). We used the dataset to characterize mean snow height, snow water equivalent (SWE), and density for the first time in Mount-Lebanon. Snow seasonal variability was characterized with high HS and SWE variance and a relatively high snow density mean equal to 467 kg m−3. We find that the relationship between snow depth and snow density is specific to the Mediterranean climate. The current model explained 34 % of the variability in the entire dataset (all regions between 1300 and 2900 m a.s.l.) and 62 % for high mountain regions (elevation 2200–2900 m a.s.l.). The dataset is suitable for the investigation of snow dynamics and for the forcing and validation of energy balance models. Therefore, this data set bears the potential to greatly improve the quantification of snowmelt and mountain hydrometeorological processes in this data-scarce region of the Eastern Mediterranean. The doi for the data is: 10.5281/zenodo.291405.

Monitoring near-surface soil freeze/thaw patterns is becoming essential under the context of global changes as it is more sensitive to climatic fluctuation compared with subsurface thermal characteristics and its evolution could be an early warning of changes in near-surface permafrost. It requires continuous long term and stable data record for understanding hydrological, ecological and biogeochemical responses of permafrost to global climate change. AMSR2 (Advanced Microwave Scanning Radiometer 2) is designed as a successor of AMSR-E (Advanced Microwave Scanning Radiometer – Earth Observing System) to ensure continuity of such observation. In this study, a linear regression is used to inter-calibrate the AMSR-E and AMSR2 brightness temperatures. Then discriminant function algorithm is adopted to produce a long term freeze/thaw data record. It is compared with in situ air temperature measurements from both the temporal and spatial aspects. The results show that the accuracy is consistent between AMSR-E and AMSR2 with a value above 85 %, according to the result of spatial distribution accuracy. Analysis is conducted with this data record to explore the spatial distribution of frost days, its changing trend and the frost probability of each pixel on a specific date. The mean annual frost days of high northern latitude (HNL, > 45° N) zone is 214.2 ± 69.5 days and the trend of frost days indicates that the frost period is decreasing at a rate of −0.0065 day/month in 27 % of the domain which is defined by significance level of the F-test, and most of which are concentrated in the high latitude area specifically over the Northeast of Canada, Central and Eastern Russia and most part of Eastern Europe. The significant changes in frost days mostly occur in regions of discontinuous permafrost and transient permafrost. The spatial distribution of the frost days and its trend variations are found to be consistent with the minimum temperature anomalies trend. It indicates that the global warming is not constant at different regions over the globe. Further analysis over the Qinghai-Tibetan Plateau where discontinuous permafrost, island permafrost, seasonally frozen ground exist demonstrated that the frost period is shortening slightly over the past decade, and the last frost date is advanced over more than half of the region. It is considered to be a remarkable indication for permafrost degradation in this area.