A decision tree algorithm was developed to classify the freeze/thaw status of the surface soil based on the cluster analysis of samples such as frozen soil,thawed soil,desert and snow,along with microwave emission and scattering characteristics of the frozen/thawed soil.The algorithm included five SSM/I channels(19V,19H,22V,37V,85V)and three crucial indices including scattering index,37GHz vertical polarization brightness temperature and 19GHz polarization difference,and took into consideration the scattering effect of desert and precipitation.The pureness of samples is essential to the analysis of the microwave brightness temperature characteristics,which is prior to deciding the thresholds of each node of the decision tree.We have selected four types of samples,including frozen soil,thawed soil,desert and snow.The frozen soil has some special microwave emission and scattering characteristics different from the thawed soil:① lower thermodynamic temperature and brightness temperature;② higher emissivity;③ stronger volume scattering,and the brightness temperature decreased with increasing frequency.The threshold of each node of the decision tree can be determined by using cluster analysis of three vital indices,and calculating the average and standard differences of each type and each index.The 4cm-depth soil temperature on the Qinghai-Tibetan Plateau observed by Soil Moisture and Temperature Measuring System of GEWEX-Coordinated Enhanced Observing Period,were used to validate the classification results.The total accuracy can reach about 87%.A majority of misclassification occurred near the freezing point of soil,about 40% and 73% of the misclassified cases appeared when the surface soil temperature is between-0.5—0.5℃ and-2.0—2.0℃,respectively.Furthermore,the misclassification mainly occurred during the transition period between warm and cold seasons,namely April-May and September-October.Based on this decision tree,a map of the number of frozen days during Oct.2002 to Sep.2003 in China was produced by composing 5 days classification results due to the swath coverage of SSM/I.The accuracy assessment for pixels with more than 15 frozen days(less than 15 meaning the short time frozen soil)was carried out with the regions of permafrost and seasonally frozen ground in map of geocryological regionalization and classification in China as reference data(Zhou et al.,2000),and the total classification accuracy was 91.66%,while the Kappa coefficient was 80.5%.The boundary between frozen and thawed soil was well consistent with the southern limit of seasonally frozen ground.A long time series surface frozen/thawed dataset can be produced using this decision tree,which may provide indicating information for regional climate change studies,regional and global scale carbon cycle models,hydrologic model and land surface model so on.
Snow is one of the most important elements in cryosphere.Most of snow cover is located in the remote regions where access is difficult and the climate and transportation conditions are poor.Thus remote sensing technology becomes a very useful and efficient method to obtain the snow information.Compared with optical remote sensing and passive microwave remote sensing,synthetic aperture radar(SAR) not only has the capabilities of penetrating clouds and providing day and night remote sensing data,but also has the capability of penetrating snow cover to retrieve subsurface information.In this review,the snow cover researches by SAR data in recent years,including several existing algorithms of SAR and InSAR to identify snow cover and estimate of snow water equivalent,snow depth,snow density and snow wetness,are described.
Abstract Climate warming is altering historical patterns of snow accumulation and ablation, hence threatening natural water resources. We evaluated the impact of climate warming on snowmelt rates using the GlobSnow v2.0 and the second Modern‐Era Retrospective analysis for Research and Applications data sets over the Northern Hemisphere (NH) during the past 38 years (1980–2017). Higher ablation rates were found in the locations with deeper snow water equivalent (SWE) because high snow melt rates occurred in late spring and early summer in deep snowpack regions. In addition, due to the reduction of SWE in deep snowpack regions, moderate and high snow ablation rates showed a decreasing trend. Therefore, slower snowmelt rates were found over the entire NH in a warmer climate in general. Based on projections of SWE in Representative Concentration Pathways 2.6, 4.5, and 8.5 climate scenarios, slower snowmelt rates in the NH may continue to happen in the future.
The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard Joint Polar Satellite System (JPSS) satellites will replace the Moderate-Resolution Imaging Spectroradiometer (MODIS) to prolong data recording in the future. Therefore, it is a fundamental task to analyze the consistency and assess the accuracy of the snow cover products retrieved from the two sensors. In this study, snow cover products from MODIS/Terra, MODIS/Aqua, VIIRS/SNPP and VIIRS/JPSS-1, were evaluated in terms of Normalized Difference Snow Index (NDSI) consistency and accuracy assessment using higher resolution images of Landsat and Sentinel-2 snow cover products. Paired comparisons were performed among the four products in five major snow distribution regions over the world: Northeast China (NE), Northwest China (NW), the Qinghai–Tibet Plateau (QT), Northern America (NA), and European Union (EU). The two categories of snow products are utilized: The L3 Daily Tiled products, referenced by their Earth Science Data Type (ESDT) names of VJ110A1, VNP10A1, MOD10A1, MYD10A1, and L3 Daily Cloud-Gap-Filled (CGF) products, VJ110A1F, VNP10A1F, MOD10A1F, MYD10A1F. The important conclusions demonstrated as follows.(1) During the snow season, the four types of 10A1 snow products demonstrated good consistency, with higher R values and smaller BIAS under clear sky. VIIRS exhibited a higher snow cover percentage than MODIS. By combining the four 10A1snow products, it is effective and feasible to produce cloud-free snow products.(2) The consistency of the four 10A1F snow products was lower than that of the 10A1 products under clear skies. SNPP showed good consistency with JPSS-1, and the same to TERRA with AQUA.(3) In the 10A1F products based on the previous day's clear-sky cloud-filling algorithm, VJ1 and VNP products exhibited larger fluctuations compared to MOD and MYD products. Among the 10A1F products, the smaller fluctuations and higher snow cover percentage of MODIS, along with a cloud persistence duration higher than VIIRS, led to an overestimation in MODIS's 10A1F snow products.(4) The snow-cloud confusion is existing both in products with the same sensor and with different sensors for the 10A1products, and the latter is much larger than the former, the percentage of which is approximately 10% in the five regions.(5) High-resolution snow product validation indicates that VIIRS has higher accuracy in both snow products than MODIS. (6) The newest JPSS-1 snow cover products display good agreement with that of SNPP. The pixels with the flag of ‘no decision’ in VNP10A1, MOD10A1, MYD10A1 are labelled as land, waterbody, and mostly clouds in VJ110A1 product, respectively.               Above all, in spite of existing sensor differences affecting consistency of snow cover products, the paired comparisons indicated that under clear skies, the four snow products exhibit good consistency, with higher consistency observed in snow products from the same sensor. The evaluations by higher resolution snow products assured the high accuracy. It is effective and feasible to produce cloud-free snow products considering the overestimation of 10A1F products.
Abstract Vegetation phenology is a sensitive indicator of climate change and has significant effects on the exchange of carbon, water, and energy between the terrestrial biosphere and the atmosphere. The Tibetan Plateau, the Earth's “third pole,” is a unique region for studying the long‐term trends in vegetation phenology in response to climate change because of the sensitivity of its alpine ecosystems to climate and its low‐level human disturbance. There has been a debate whether the trends in spring phenology over the Tibetan Plateau have been continuously advancing over the last two to three decades. In this study, we examine the trends in the start of growing season (SOS) for alpine meadow and steppe using the Global Inventory Modeling and Mapping Studies (GIMMS)3g normalized difference vegetation index (NDVI) data set (1982–2014), the GIMMS NDVI data set (1982–2006), the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data set (2001–2014), the Satellite Pour l'Observation de la Terre Vegetation (SPOT‐VEG) NDVI data set (1999–2013), and the Sea‐viewing Wide Field‐of‐View Sensor (SeaWiFS) NDVI data set (1998–2007). Both logistic and polynomial fitting methods are used to retrieve the SOS dates from the NDVI data sets. Our results show that the trends in spring phenology over the Tibetan Plateau depend on both the NDVI data set used and the method for retrieving the SOS date. There are large discrepancies in the SOS trends among the different NDVI data sets and between the two different retrieval methods. There is no consistent evidence that spring phenology (“green‐up” dates) has been advancing or delaying over the Tibetan Plateau during the last two to three decades. Ground‐based budburst data also indicate no consistent trends in spring phenology. The responses of SOS to environmental factors (air temperature, precipitation, soil temperature, and snow depth) also vary among NDVI data sets and phenology retrieval methods. The increases in winter and spring temperature had offsetting effects on spring phenology.
Abstract. The snow water equivalent (SWE) is an important parameter of surface hydrological and climate systems, and it has a profound impact on Arctic amplification and climate change. However, there are great differences among existing SWE products. In the land region above 45ââN, the existing SWE products are associated with a limited time span and limited spatial coverage, and the spatial resolution is coarse, which greatly limits the application of SWE data in cryosphere change and climate change studies. In this study, utilizing the ridge regression model (RRM) of a machine learning algorithm, we integrated various existing SWE products to generate a spatiotemporally seamless and high-precision RRM SWE product. The results show that it is feasible to utilize a ridge regression model based on a machine learning algorithm to prepare SWE products on a global scale. We evaluated the accuracy of the RRM SWE product using hemispheric-scale snow course (HSSC) observational data and Russian snow survey data. The mean absolute error (MAE), RMSE, R, and R2 between the RRM SWE products and observed SWEs are 0.21, 25.37âmm, 0.89, and 0.79, respectively. The accuracy of the RRM SWE dataset is improved by 28â%, 22â%, 37â%, 11â%, and 11â% compared with the original AMSR-E/AMSR2 (SWE), ERA-Interim SWE, Global Land Data Assimilation System (GLDAS) SWE, GlobSnow SWE, and ERA5-Land SWE datasets, respectively, and it has a higher spatial resolution. The RRM SWE product production method does not rely heavily on an independent SWE product; it takes full advantage of each SWE dataset, and it takes into consideration the altitude factor. The MAE ranges from 0.16 for areas within <100âm elevation to 0.29 within the 800â900âm elevation range. The MAE is best in the Russian region and worst in the Canadian region. The RMSE ranges from 4.71âmm for areas within <100âm elevation to 31.14âmm within the >1000âm elevation range. The RMSE is best in the Finland region and worst in the Canadian region. This method has good stability, is extremely suitable for the production of snow datasets with large spatial scales, and can be easily extended to the preparation of other snow datasets. The RRM SWE product is expected to provide more accurate SWE data for the hydrological model and climate model and provide data support for cryosphere change and climate change studies. The RRM SWE product is available from âA Big Earth Data Platform for Three Polesâ (https://doi.org/10.11888/Snow.tpdc.271556) (Li et al., 2021).
Current snow depth datasets demonstrate large discrepancies in the spatial pattern in Eurasia, and the lagging updates of datasets do not meet the operational requirements of the meteorological service department. This study developed a dynamic retrieval method for daily snow depth over Eurasia based on cross-sensor calibrated microwave brightness temperatures to enhance retrieval accuracy and meet the requirements of operational work. These brightness temperatures were detected by microwave radiometer imager carried on the FengYun 3 (FY-3) satellite and the special sensor microwave imager/sounder carried on the USA Defense Meteorological Satellite Program series satellites, which use the fewest sensors to provide the longest data and consequently introduce minimal errors during inter-sensor calibration. Firstly, inter-sensor calibration was conducted amongst brightness temperatures collected by the three sensors. A spatiotemporal dynamic relationship between snow depth and microwave brightness temperature gradient was then established, overcoming the large uncertainties induced by varying snow characteristics. This relationship can be utilised in FY-3 satellite data for operational service to obtain real-time snow depth. The generated daily snow depth dataset from 1988 to 2021 presents similar spatial patterns of snow depth to those observed in situ. Against in situ snow depth, the overall bias and root mean square error are −2.04 and 6.49 cm, respectively, facilitating considerable improvements in accuracy compared with the Advanced Microwave Scanning Radiometer 2 snow depth product, which adopts the static algorithm. Further analysis shows an overall decreasing trend from 1988 to 2021 for annual and monthly mean snow depths, demonstrating a noticeable reduction since around 2000. The reduction in monthly mean snow depth started earlier in shallow snow months than in deep snow months.
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