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Snow cover is highly critical for global water and energy cycles because of its wide areal extent, high reflectivity and good thermal insulation. Knowledge of snow conditions, e.g., snow water equivalent (SWE) and snow depth, is significant to hydrologic and climatologic processes. Spaceborne passive microwave (PMW) data, namely, brightness temperature (TB), have been in use for snow depth and SWE retrieval at the global scale since 1978. However, the sensitivity of TB to these parameters is complex due to snow metamorphism (e.g., snow grain size, GS), which limits the feasibility of many existing algorithms characterizing snow. This study presents a new methodology to retrieve snow depth over China by coupling a microwave snow emission model with a random forest (RF) machine learning (ML) technique. An effective GS value (effGS), a prior snowpack descriptor, was optimized utilizing the Helsinki University of Technology (HUT) model by minimizing the difference between AMSR2 observations (18.7 and 36.5 GHz) and HUT simulations. Five elaborately selected independent variables, including vertical polarized TB differences (TBD) between 18.7 and 36.5 GHz (TBD18.7V&36.5V), 10.65 and 36.5 GHz (TBD10.65V&36.5V), longitude, elevation and effGS, together with the target variable, snow depth, were applied to train the RF model, and then the 10-fold cross-validation (10-CV) approach was employed for performance validation using station data during the period from 2012 to 2018. The results indicated that (1) inclusion of effGS in RF greatly enhanced the overall performance in snow depth estimation; (2) the trained RF model performed better on a temporal scale than on a spatial scale, with unRMSEs of 1.81 cm and 3.17 cm, respectively; (3) specifically, the fitted RF algorithm partially addressed the overestimation in shallow (≤ 20 cm) snowpacks and underestimation in deep (> 20 cm) snow conditions when compared with the established RF algorithm based solely on predictor variables but without effGS. To evaluate the predictive power of the RF algorithm trained with samples in 2017 and 2018, spatially independent station measurements during the period from 2012 to 2016 and field survey data collected from January 2018 to March 2019 were used for validation. Additionally, the RF estimates were compared with two widely used satellite products (AMSR2 and GlobSnow-2). The validation results showed that RF estimates were closer to the in situ data than the other two satellite products. This study demonstrated the potential utility of combining the snow emission model with an ML approach to improve snow depth estimation.
J.W. Yang; L.M. Jiang; J. Lemmetyinen; J.M. Pan; K. Luojus; M. Takala. Improving snow depth estimation by coupling HUT-optimized effective snow grain size parameters with the random forest approach. Remote Sensing of Environment 2021, 264, 112630 .
AMA StyleJ.W. Yang, L.M. Jiang, J. Lemmetyinen, J.M. Pan, K. Luojus, M. Takala. Improving snow depth estimation by coupling HUT-optimized effective snow grain size parameters with the random forest approach. Remote Sensing of Environment. 2021; 264 ():112630.
Chicago/Turabian StyleJ.W. Yang; L.M. Jiang; J. Lemmetyinen; J.M. Pan; K. Luojus; M. Takala. 2021. "Improving snow depth estimation by coupling HUT-optimized effective snow grain size parameters with the random forest approach." Remote Sensing of Environment 264, no. : 112630.
Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture across large areas, but the coarse spatial resolution of microwave data imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05∘, monthly) for China from 2002 to 2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products – including AMSR-E and AMSR2 (Advanced Microwave Scanning Radiometer for Earth Observing System) JAXA (Japan Aerospace Exploration Agency) Level 3 products and SMOS-IC (Soil Moisture and Ocean Salinity designed by the Institut National de la Recherche Agronomique, INRA, and Centre d’Etudes Spatiales de la BIOsphère, CESBIO) products – calibrated with a consistent model in combination with ground observation data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates that the accuracy of the new dataset is satisfactory (bias: −0.057, −0.063 and −0.027 m3 m−3; unbiased root mean square error (ubRMSE): 0.056, 0.036 and 0.048; correlation coefficient (R): 0.84, 0.85 and 0.89 on monthly, seasonal and annual scales, respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past 17 years, China's soil moisture has shown cyclical fluctuations and a slight downward trend and can be summarized as wet in the south and dry in the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in Zenodo at https://doi.org/10.5281/zenodo.4738556 (Meng et al., 2021a).
Xiangjin Meng; Kebiao Mao; Fei Meng; Jiancheng Shi; Jiangyuan Zeng; Xinyi Shen; Yaokui Cui; Lingmei Jiang; Zhonghua Guo. A fine-resolution soil moisture dataset for China in 2002–2018. Earth System Science Data 2021, 13, 3239 -3261.
AMA StyleXiangjin Meng, Kebiao Mao, Fei Meng, Jiancheng Shi, Jiangyuan Zeng, Xinyi Shen, Yaokui Cui, Lingmei Jiang, Zhonghua Guo. A fine-resolution soil moisture dataset for China in 2002–2018. Earth System Science Data. 2021; 13 (7):3239-3261.
Chicago/Turabian StyleXiangjin Meng; Kebiao Mao; Fei Meng; Jiancheng Shi; Jiangyuan Zeng; Xinyi Shen; Yaokui Cui; Lingmei Jiang; Zhonghua Guo. 2021. "A fine-resolution soil moisture dataset for China in 2002–2018." Earth System Science Data 13, no. 7: 3239-3261.
Synthetic aperture radar (SAR) sensors, such as Advanced Land Observing Satellite-2 (ALOS-2) and Sentinel-1, provide significant opportunities for soil moisture content (SMC) retrieval with relatively high spatial resolutions (10~30 m). In this work, an artificial neural network (ANN) SMC retrieval algorithm combined with the water cloud model, the advanced integral equation model, and the Oh model database was proposed. The SAR copolarization backscatter, the local incidence angle (LIA), and the normalized difference vegetation index were used in input vectors for the ANN algorithm for the retrieval and mapping of the ALOS-2 and Sentinel-1 SMC at a 30-m resolution. The results of the comparison between the SMC retrievals and the measured SMC show that Sentinel-1 and ALOS-2 SMC retrievals with high accuracy correspond to low-vegetation areas (crop, grass, and shrub), with a root mean square error (RMSE) of 0.021 and 0.033 cm³/cm³, respectively. ALOS-2 SMC retrievals provide higher accuracy (RMSE = 0.076 cm³/cm³) than Sentinel-1 SMC retrievals at high vegetation (e.g., forest). However, it remains challenging for soil moisture retrieval in forest land. The C-band and L-band SMC retrievals have higher RMSE (up to 0.047 cm³/cm³) at low incidence angle (50°). In addition, by considering the impact of rainfall on the SMC, it appears that the Sentinel-1 and ALOS-2 SMC have a good response to the rainfall events. Finally, the results of the comparison between the SMC retrievals and the Soil Moisture Active Passive (SMAP) L2 SMC product show that the correlation coefficients between Sentinel-1, ALOS-2, and SMAP are higher in September when the vegetation is drying than in July when the vegetation is growing.
Huizhen Cui; Lingmei Jiang; Simonetta Paloscia; Emanuele Santi; Simone Pettinato; Jian Wang; Xiyao Fang; Wanjin Liao. The Potential of ALOS-2 and Sentinel-1 Radar Data for Soil Moisture Retrieval With High Spatial Resolution Over Agroforestry Areas, China. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -17.
AMA StyleHuizhen Cui, Lingmei Jiang, Simonetta Paloscia, Emanuele Santi, Simone Pettinato, Jian Wang, Xiyao Fang, Wanjin Liao. The Potential of ALOS-2 and Sentinel-1 Radar Data for Soil Moisture Retrieval With High Spatial Resolution Over Agroforestry Areas, China. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-17.
Chicago/Turabian StyleHuizhen Cui; Lingmei Jiang; Simonetta Paloscia; Emanuele Santi; Simone Pettinato; Jian Wang; Xiyao Fang; Wanjin Liao. 2021. "The Potential of ALOS-2 and Sentinel-1 Radar Data for Soil Moisture Retrieval With High Spatial Resolution Over Agroforestry Areas, China." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-17.
Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture over large areas, but the coarse spatial resolution of microwave data imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05°, monthly) for China from 2002–2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products (including the AMSR-E/2 Level 3 products and the SMOS-INRA-CESBIO (SMOS-IC) products) calibrated with a consistent model in combination with ground observation data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates that the accuracy of the new dataset is satisfactory (bias: −0.024, −0.030 and −0.016 m3/m3, unbiased root mean square error (ubRMSE): 0.051, 0.048 and 0.042, correlation coefficient (R): 0.82, 0.88, and 0.90 on monthly, seasonal and annual scales, respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past 17 years, China's soil moisture has shown cyclical fluctuations and a downward trend (slope = −0.167, R = 0.750) and can be summarized as wet in the south and dry in the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in the Zenodo at http://doi.org/10.5281/zenodo.4049958 (Meng et al., 2020).
Xiangjin Meng; Kebiao Mao; Fei Meng; Jiancheng Shi; Jiangyuan Zeng; Xinyi Shen; Yaokui Cui; Lingmei Jiang; Zhonghua Guo. A fine-resolution soil moisture dataset for China in 2002–2018. 2020, 2020, 1 -28.
AMA StyleXiangjin Meng, Kebiao Mao, Fei Meng, Jiancheng Shi, Jiangyuan Zeng, Xinyi Shen, Yaokui Cui, Lingmei Jiang, Zhonghua Guo. A fine-resolution soil moisture dataset for China in 2002–2018. . 2020; 2020 ():1-28.
Chicago/Turabian StyleXiangjin Meng; Kebiao Mao; Fei Meng; Jiancheng Shi; Jiangyuan Zeng; Xinyi Shen; Yaokui Cui; Lingmei Jiang; Zhonghua Guo. 2020. "A fine-resolution soil moisture dataset for China in 2002–2018." 2020, no. : 1-28.
A key variable describing the mass of seasonal snow cover is snow water equivalent (SWE), which plays an important role in hydrological applications, weather forecasting and land surface process simulations. In this paper, the accuracy of an SWE product, GlobSnow-2, which combines microwave satellite data and in situ measurements, is assessed using three reference evaluation datasets north of 35°N in China. The GlobSnow-2 estimates are also compared with stand-alone satellite products (AMSR2, Chang and FY-3D SWE). The overall unbiased root mean square error (RMSE) and bias of the GlobSnow-2 SWE product validated with three reference datasets are 17.4 mm and 11.2 mm, respectively, which outperforms the AMSR2 SWE (39.3 mm and 37.3 mm, respectively) and Chang SWE (57.5 mm and 46.2 mm, respectively) products. The FY-3D SWE product performs better than the GlobSnow-2 estimate for shallow snow (SWE < 50 mm) and tends to underestimate snow cover, particularly when SWE exceeds 80 mm. A retrieval sensitivity analysis against land cover types shows that the highest SWE uncertainties for GlobSnow-2 are exhibited in grassland (unbiased RMSE, 27.8 mm), and the most serious overestimation occurs in forested areas (bias, 23.6 mm). The GlobSnow-2 performances at various elevations show an increasing bias trend, ranging from 5-61 mm with increasing elevation. The GlobSnow-2 estimate analyses under different snow regimes show that the GlobSnow-2 SWE product performs best in taiga snow, with high uncertainties (unbiased RMSE, 28.3 mm) in prairie snow and serious overestimations (bias, 23.2 mm) for alpine snow. The results of this study demonstrate that the GlobSnow-2 assimilation approach tends to overestimate SWE in China. One of the major reasons that overestimations occur is that the GlobSnow-2 SWE retrieval scheme utilizes a fixed density of 240 kg/m3, which is larger than the average value derived from ground measurements for China (180 kg/m3), which undoubtedly contributes to the observed SWE overestimation. Another reason is that forest effects on satellite signals remain challenging for SWE estimations in the GlobSnow-2 assimilation system. The retrieval errors in prairie and alpine are also higher than others due to the snowpack stratigraphy and complex topography. The GlobSnow-2 SWE product performance is evaluated over China in this study, and the major factors that affect the assimilation scheme accuracy are determined. These results will provide a reference to improve the GlobSnow-2 SWE product in future work.
J.W. Yang; L.M. Jiang; J. Lemmetyinen; K. Luojus; M. Takala; S.L. Wu; J.M. Pan. Validation of remotely sensed estimates of snow water equivalent using multiple reference datasets from the middle and high latitudes of China. Journal of Hydrology 2020, 590, 125499 .
AMA StyleJ.W. Yang, L.M. Jiang, J. Lemmetyinen, K. Luojus, M. Takala, S.L. Wu, J.M. Pan. Validation of remotely sensed estimates of snow water equivalent using multiple reference datasets from the middle and high latitudes of China. Journal of Hydrology. 2020; 590 ():125499.
Chicago/Turabian StyleJ.W. Yang; L.M. Jiang; J. Lemmetyinen; K. Luojus; M. Takala; S.L. Wu; J.M. Pan. 2020. "Validation of remotely sensed estimates of snow water equivalent using multiple reference datasets from the middle and high latitudes of China." Journal of Hydrology 590, no. : 125499.
We investigated the potential capability of the random forest (RF) machine learning (ML) model to estimate snow depth in this work. Four combinations composed of critical predictor variables were used to train the RF model. Then, we utilized three validation datasets from out-of-bag (OOB) samples, a temporal subset, and a spatiotemporal subset to verify the fitted RF algorithms. The results indicated the following: (1) the accuracy of the RF model is greatly influenced by geographic location, elevation, and land cover fractions; (2) however, the redundant predictor variables (if highly correlated) slightly affect the RF model; and (3) the fitted RF algorithms perform better on temporal than spatial scales, with unbiased root-mean-square errors (RMSEs) of ∼4.4 and ∼7.3 cm, respectively. Finally, we used the fitted RF2 algorithm to retrieve a consistent 32-year daily snow depth dataset from 1987 to 2018. This product was evaluated against the independent station observations during the period 1987–2018. The mean unbiased RMSE and bias were 7.1 and −0.05 cm, respectively, indicating better performance than that of the former snow depth dataset (8.4 and −1.20 cm) from the Environmental and Ecological Science Data Center for West China (WESTDC). Although the RF product was superior to the WESTDC dataset, it still underestimated deep snow cover (>20 cm), with biases of −10.4, −8.9, and −34.1 cm for northeast China (NEC), northern Xinjiang (XJ), and the Qinghai–Tibetan Plateau (QTP), respectively. Additionally, the long-term snow depth datasets (station observations, RF estimates, and WESTDC product) were analyzed in terms of temporal and spatial variations over China. On a temporal scale, the ground truth snow depth presented a significant increasing trend from 1987 to 2018, especially in NEC. However, the RF and WESTDC products displayed no significant changing trends except on the QTP. The WESTDC product presented a significant decreasing trend on the QTP, with a correlation coefficient of −0.55, whereas there were no significant trends for ground truth observations and the RF product. For the spatial characteristics, similar trend patterns were observed for RF and WESTDC products over China. These characteristics presented significant decreasing trends in most areas and a significant increasing trend in central NEC.
Jianwei Yang; Lingmei Jiang; Kari Luojus; Jinmei Pan; Juha Lemmetyinen; Matias Takala; Shengli Wu. Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach. The Cryosphere 2020, 14, 1763 -1778.
AMA StyleJianwei Yang, Lingmei Jiang, Kari Luojus, Jinmei Pan, Juha Lemmetyinen, Matias Takala, Shengli Wu. Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach. The Cryosphere. 2020; 14 (6):1763-1778.
Chicago/Turabian StyleJianwei Yang; Lingmei Jiang; Kari Luojus; Jinmei Pan; Juha Lemmetyinen; Matias Takala; Shengli Wu. 2020. "Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach." The Cryosphere 14, no. 6: 1763-1778.
The dataset presented in this article is related to the work “Evaluation and Analysis of SMAP, AMSR2, and MEaSUREs Freeze/Thaw Products in China [1]”. Soil moisture and temperature are important variables of land-atmosphere energy exchange, monitoring vegetation growth, predicting drought disasters and climate and hydrological modelling [2], [3], [4], [5], [6]. This work provides detailed information on in situ soil moisture and temperature data network established in the Genhe watershed and Saihanba area in China, respectively. The Genhe watershed represents the complex surface heterogeneity in Northeast China. Therefore, data from 22 in situ sites were established in the Genhe watershed since March 2016 to improve the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Saihanba is currently China's largest manmade forest and has a unique alpine wetland and a complete aquatic ecosystem. There are 29 in situ sites deployed in Saihanba since August 2018 for studying the cold temperate continental monsoon climate and estimating forest carbon storage capacity and carbon emissions from manmade forests. Soil temperature and permittivity data in the network were measured using ECH2O EC-5TM probes (Decagon Devices, Inc., Washington, USA, https://www.metergroup.com/) and XingShiTu (XST) probes (BEIJING XST Co., Ltd., www.xingshitu.com) every 30 min at depths of 3, 5, and 10 cm for the Genhe watershed continuous automatic observation network, and depths of 5 and 10 cm for the Saihanba continuous automatic observation network. In the Genhe watershed, soil moisture and soil temperature data in the network were automatically collected using the EM50 data collection system. The Saihanba area has the XST data collection system to record soil temperature and permittivity. The permittivity data collected with the XST data collector were transformed to soil moisture data (volumetric water content) based on the formula developed by [7]. The datasets of the Genhe watershed and Saihanba area consist of raw data acquired by the data collector and processed data of soil moisture and temperature. The Saihanba dataset also includes the calibration data based on soil texture. The result of temporal variations analysis in observed data in the Genhe Watershed and the processing in observed data in the saihanba area show that the long-term in situ soil moisture and temperature datasets can be used for the validation/calibration and improvement of the soil moisture and soil freeze/thaw algorithm.
Lingmei Jiang; Jian Wang; Huizhen Cui; Gongxue Wang; Tianjie Zhao; Shaojie Zhao; Linna Chai; Xiaojing Liu; Jianwei Yang. In situ soil moisture and temperature network in genhe watershed and saihanba area in China. Data in Brief 2020, 31, 105693 .
AMA StyleLingmei Jiang, Jian Wang, Huizhen Cui, Gongxue Wang, Tianjie Zhao, Shaojie Zhao, Linna Chai, Xiaojing Liu, Jianwei Yang. In situ soil moisture and temperature network in genhe watershed and saihanba area in China. Data in Brief. 2020; 31 ():105693.
Chicago/Turabian StyleLingmei Jiang; Jian Wang; Huizhen Cui; Gongxue Wang; Tianjie Zhao; Shaojie Zhao; Linna Chai; Xiaojing Liu; Jianwei Yang. 2020. "In situ soil moisture and temperature network in genhe watershed and saihanba area in China." Data in Brief 31, no. : 105693.
The moderate resolution imaging spectroradiometer (MODIS) snow algorithm has been used to generate global fractional snow cover (FSC) at a pixel size of 500 m using a linear regression relationship (called ``FRA6T'') between FSC and the normalized difference snow index (NDSI). However, the linear relationship is problematic because of the considerable NDSI variation in nonsnow conditions. In this letter, we propose a universal ratio snow index (URSI), which is the ratio of the visible reflectance and the sum of the near infrared and shortwave infrared reflectances. It is called ``universal'' because it has weak sensitivity under snow-free ground conditions and, therefore, can improve the stability of the linear snow index methodology. A comparison between NDSI and URSI with regard to estimate FSC using the linear snow index methodology is carried out for the Tibetan Plateau. The scatter plots of MODIS NDSI/URSI and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) FSC indicate that a linear relationship can be assumed for both NDSI and URSI for barren land conditions and is more appropriate for URSI than it is for NDSI in forested areas. Validation efforts show that the linear relationship using URSI (designated ``FracURSI'') achieves fewer errors in FSC estimation compared with the developed NDSI method (``FracNDSI''), particularly for forested areas and for moderate FSC values. Averaged over all comparisons, the root-mean-square error (RMSE) of FSC estimates for FRA6T is 0.13, and for FracNDSI is 0.12, whereas FracURSI RMSE is 0.11.
Gongxue Wang; Lingmei Jiang; Jiancheng Shi; Xu Su. A Universal Ratio Snow Index for Fractional Snow Cover Estimation. IEEE Geoscience and Remote Sensing Letters 2020, 18, 721 -725.
AMA StyleGongxue Wang, Lingmei Jiang, Jiancheng Shi, Xu Su. A Universal Ratio Snow Index for Fractional Snow Cover Estimation. IEEE Geoscience and Remote Sensing Letters. 2020; 18 (4):721-725.
Chicago/Turabian StyleGongxue Wang; Lingmei Jiang; Jiancheng Shi; Xu Su. 2020. "A Universal Ratio Snow Index for Fractional Snow Cover Estimation." IEEE Geoscience and Remote Sensing Letters 18, no. 4: 721-725.
The surface seasonal freeze/thaw (F/T) signal detected by passive microwave remote sensing is very important for the water cycle, carbon cycle and climate change research. In this study, we evaluated and analyzed the Soil Moisture Active Passive (SMAP) L3 F/T product, Advanced Microwave Scanning Radiometer 2 (AMSR2) F/T product and Making Earth System Data Records for Use in Research Environments (MEaSUREs) F/T product over different regions in China, including the Genhe area in Northeast China, the Saihanba area in North China, and the Qinghai-Tibet Plateau (QTP) area. The overall accuracy of F/T products assessed with the 5 cm depth soil temperature is 90.38% for SMAP, 90.23% for AMSR2 and 84.73% for MEaSUREs in cold and humid temperate forest climates and the plateau continental climate area (Genhe, Tianjun, and Qumalai) where permafrost is distributed, and 76.64% for SMAP, 83.67% for AMSR2 and 77.37% for MEaSUREs in the cold plateau mountain climate and plateau continental climate area (Saihanba and Chengduo) with frozen ground distributed seasonally, respectively. The overall accuracy is 69.05% for SMAP, 76.5% for AMSR2 and 81.4% for MEaSUREs in the Ngari, Naqu, and Dachaidan regions belonging to arid and semi-arid climates. It can be seen that SMAP and AMSR2 achieve the best performance in the distributed permafrost area, the second-best performance in the seasonal distributed permafrost area, but the worst performance in the areas with arid and semi-arid climate types due to inconsistent F/T signals between water with small changes and temperature with apparent changes during the F/T transition. The MEaSUREs product showed almost the same performance in different regions, indicating that it was less affected by climate types and the distribution of frozen soil than SMAP and AMSR2 products. SMAP F/T product detected by L-band with long penetration and AMSR2 F/T product calibrated with 5 cm soil temperature could represent the 5 cm F/T, but the MEaSUREs F/T product was more likely to describe the surface F/T state due to calibrated with air temperature and the short penetration of 36.5 GHz. In mid-low latitude areas (Tianjun and Qumalai) with a short duration of snow cover days and a fast snowmelt, the effect of snow melting on F/T products was negligible. Moreover, the spring snowmelt affects the three F/T products in Chengduo, but the SMAP product is not affected by the winter snowmelt, whereas the AMSR2 product is affected by the winter snowmelt.
Jian Wang; Lingmei Jiang; Huizhen Cui; Gongxue Wang; Jianwei Yang; Xiaojing Liu; Xu Su. Evaluation and analysis of SMAP, AMSR2 and MEaSUREs freeze/thaw products in China. Remote Sensing of Environment 2020, 242, 111734 .
AMA StyleJian Wang, Lingmei Jiang, Huizhen Cui, Gongxue Wang, Jianwei Yang, Xiaojing Liu, Xu Su. Evaluation and analysis of SMAP, AMSR2 and MEaSUREs freeze/thaw products in China. Remote Sensing of Environment. 2020; 242 ():111734.
Chicago/Turabian StyleJian Wang; Lingmei Jiang; Huizhen Cui; Gongxue Wang; Jianwei Yang; Xiaojing Liu; Xu Su. 2020. "Evaluation and analysis of SMAP, AMSR2 and MEaSUREs freeze/thaw products in China." Remote Sensing of Environment 242, no. : 111734.
Daily snow-covered area retrieval using the imagery in solar reflective bands often encounters extensive data gaps caused by cloud obscuration. With the inception of geostationary satellites carrying advanced multispectral imagers of high temporal resolution, such as Japan’s geostationary weather satellite Himawari–8, considerable progress can now be made towards spatially-complete estimation of daily snow-covered area. We developed a dynamic snow index (normalized difference snow index for vegetation-free background and normalized difference forest–snow index for vegetation background) fractional snow cover estimation method using Himawari–8 Advanced Himawari Imager (AHI) observations of the Tibetan Plateau. This method estimates fractional snow cover with the pixel-by-pixel linear relationship of snow index observations acquired under snow-free and snow-covered conditions. To achieve reliable snow-covered area mapping with minimal cloud contamination, the daily fractional snow cover can be represented as the composite of the high temporal resolution fractional snow cover estimates during daytime. The comparison against reference fractional snow cover data from Landsat–8 Operational Land Imager (OLI) showed that the root–mean–square error (RMSE) of the Himawari–8 AHI fractional snow cover ranged from 0.07 to 0.16, and that the coefficient of determination (R2) reached 0.81–0.96. Results from the 2015/2016 and 2016/2017 winters indicated that the daily composite of Himawari–8 observations obtained a 14% cloud percentage over the Tibetan Plateau, which was less than the cloud percentage (27%) from the combination of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua.
Gongxue Wang; Lingmei Jiang; Jiancheng Shi; Xiaojing Liu; Jianwei Yang; Huizhen Cui. Snow-Covered Area Retrieval from Himawari–8 AHI Imagery of the Tibetan Plateau. Remote Sensing 2019, 11, 2391 .
AMA StyleGongxue Wang, Lingmei Jiang, Jiancheng Shi, Xiaojing Liu, Jianwei Yang, Huizhen Cui. Snow-Covered Area Retrieval from Himawari–8 AHI Imagery of the Tibetan Plateau. Remote Sensing. 2019; 11 (20):2391.
Chicago/Turabian StyleGongxue Wang; Lingmei Jiang; Jiancheng Shi; Xiaojing Liu; Jianwei Yang; Huizhen Cui. 2019. "Snow-Covered Area Retrieval from Himawari–8 AHI Imagery of the Tibetan Plateau." Remote Sensing 11, no. 20: 2391.
Cold regions, characterized by the presence of permafrost and extensive snow and ice cover, are significantly affected by changing climate
Jinyang Du; Jennifer D. Watts; Hui Lu; Lingmei Jiang; Paolo Tarolli. Editorial for Special Issue: “Remote Sensing of Environmental Changes in Cold Regions”. Remote Sensing 2019, 11, 2165 .
AMA StyleJinyang Du, Jennifer D. Watts, Hui Lu, Lingmei Jiang, Paolo Tarolli. Editorial for Special Issue: “Remote Sensing of Environmental Changes in Cold Regions”. Remote Sensing. 2019; 11 (18):2165.
Chicago/Turabian StyleJinyang Du; Jennifer D. Watts; Hui Lu; Lingmei Jiang; Paolo Tarolli. 2019. "Editorial for Special Issue: “Remote Sensing of Environmental Changes in Cold Regions”." Remote Sensing 11, no. 18: 2165.
Snow depth data time series are valuable for climatological and hydrological applications. Passive microwave (PMW) sensors are advantageous for estimating spatially and temporally continuous snow depth. However, PMW estimate accuracy has several problems, which results in poor performances of traditional snow depth estimation algorithms. Machine learning (ML) is a common method used in many research fields, and its early application in remote sensing is promising. In this study, we propose a new and accurate approach based on the ML technique to estimate real-time snow depth and reconstruct historical snow depth from 1987–2018. First, we trained the random forest (RF) model with advanced microwave scanning radiometer 2 (AMSR2) brightness temperatures (TB) at 10.65, 18.7, 36.5 and 89 GHz, land cover fraction (forest, shrub, grass, farm and barren), geolocation (latitude and longitude) and station observation from 2014–2015. Then, the trained RF model was used to retrieve a reference dataset with 2012–2018 AMSR2 TB data as the accurate snow depth. With this reference snow depth dataset, we developed the pixel-based algorithm for the Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMI/S). Finally, the pixel-based method was used to reconstruct a consistent 31-year daily snow depth dataset for 1987–2018. We validated the trained RF model using the weather station observations and AMSR2 TB during 2012–2013. The results showed that the RF model root mean square error (RMSE) and bias were 4.5 cm and 0.04 cm, respectively. The pixel-based algorithm’s accuracy was evaluated against the field sampling experiments dataset (January–March, 2018) and station observations in 2017–2018, and the RMSEs were 2.0 cm and 5.1 cm, respectively. The pixel-based method performs better than the previous regression method fitted in China (RMSEs are 4.7 cm and 8.4 cm, respectively). The high accuracy of the pixel-based method can be attributed to the spatial dynamic retrieval coefficients and accurate snow depth estimates of the RF model. Additionally, the 1987–2018 long-term snow depth dataset was analyzed in terms of temporal and spatial variations. On the spatial scale, daily maximum snow depth tends to occur in Xinjiang and the Himalayas during 1992–2018. However, the daily mean snow depth in Northeast China is the largest. For the temporal characteristics, the February mean snow depth is the thickest during snowy winter seasons. Interestingly, the January mean snow depth represents the annual mean snow depth, which plays an important role in snow depth prediction and hydrological management. In conclusion, through step-by-step validation using in situ observations, our pixel-based approach is available in real-time snow depth retrievals and historical data reconstruction.
Jianwei Yang; Lingmei Jiang; Kari Luojus; Jinmei Pan; Juha Lemmetyinen; Matias Takala; Shengli Wu. Real-Time Snow Depth Estimation and Historical Data Reconstruction Over China Based on a Random Forest Machine Learning Approach. 2019, 2019, 1 -35.
AMA StyleJianwei Yang, Lingmei Jiang, Kari Luojus, Jinmei Pan, Juha Lemmetyinen, Matias Takala, Shengli Wu. Real-Time Snow Depth Estimation and Historical Data Reconstruction Over China Based on a Random Forest Machine Learning Approach. . 2019; 2019 ():1-35.
Chicago/Turabian StyleJianwei Yang; Lingmei Jiang; Kari Luojus; Jinmei Pan; Juha Lemmetyinen; Matias Takala; Shengli Wu. 2019. "Real-Time Snow Depth Estimation and Historical Data Reconstruction Over China Based on a Random Forest Machine Learning Approach." 2019, no. : 1-35.
The sensing depth of passive microwave remote sensing is a significant factor in quantitative frozen soil studies. In this paper, a microwave radiation response depth (MRRD) was proposed to describe the source of the main signals of passive microwave remote sensing. The main goal of this research was to develop a simple and accurate parameterized model for estimating the MRRD of frozen soil. A theoretical model was introduced first to describe the emission characteristics of a three-layer case, which incorporates multiple reflections at the two boundaries. Based on radiative transfer theory, the total emission of the three layers was calculated. A sensitivity analysis was then performed to demonstrate the effects of soil properties and frequency on the MRRD based on a simulation database comprising a wide range of soil characteristics and frequencies. Sensitivity analysis indicated that soil temperature, soil texture, and frequencies are three of the primary variables affecting MRRD, and a definite empirical relationship existed between the three parameters and the MRRD. Thus, a parameterized model for estimating MRRD was developed based on the sensitivity analysis results. A controlled field experiment using a truck-mounted multi-frequency microwave radiometer (TMMR) was designed and performed to validate the emission model of the soil freeze–thaw cycle and the parameterized model of MRRD developed in this work. The results indicated that the developed parameterized model offers a relatively accurate and simple way of estimating the MRRD. The total root mean square error (RMSE) between the calculated and measured MRRD of frozen loam soil was approximately 0.5 cm for the TMMR’s four frequencies.
Tao Zhang; Lingmei Jiang; Shaojie Zhao; Linna Chai; Yunqing Li; Yuhao Pan. Development of a Parameterized Model to Estimate Microwave Radiation Response Depth of Frozen Soil. Remote Sensing 2019, 11, 2028 .
AMA StyleTao Zhang, Lingmei Jiang, Shaojie Zhao, Linna Chai, Yunqing Li, Yuhao Pan. Development of a Parameterized Model to Estimate Microwave Radiation Response Depth of Frozen Soil. Remote Sensing. 2019; 11 (17):2028.
Chicago/Turabian StyleTao Zhang; Lingmei Jiang; Shaojie Zhao; Linna Chai; Yunqing Li; Yuhao Pan. 2019. "Development of a Parameterized Model to Estimate Microwave Radiation Response Depth of Frozen Soil." Remote Sensing 11, no. 17: 2028.
Cold regions, including high-latitude and high-altitude landscapes, are experiencing profound environmental changes driven by global warming. With the advance of earth observation technology, remote sensing has become increasingly important for detecting, monitoring, and understanding environmental changes over vast and remote regions. This paper provides an overview of recent achievements, challenges, and opportunities for land remote sensing of cold regions by (a) summarizing the physical principles and methods in remote sensing of selected key variables related to ice, snow, permafrost, water bodies, and vegetation; (b) highlighting recent environmental nonstationarity occurring in the Arctic, Tibetan Plateau, and Antarctica as detected from satellite observations; (c) discussing the limits of available remote sensing data and approaches for regional monitoring; and (d) exploring new opportunities from next-generation satellite missions and emerging methods for accurate, timely, and multi-scale mapping of cold regions.
Jinyang Du; Jennifer D. Watts; Lingmei Jiang; Hui Lu; Xiao Cheng; Claude Duguay; Mary Farina; Yubao Qiu; Youngwook Kim; John S. Kimball; Paolo Tarolli. Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges. Remote Sensing 2019, 11, 1952 .
AMA StyleJinyang Du, Jennifer D. Watts, Lingmei Jiang, Hui Lu, Xiao Cheng, Claude Duguay, Mary Farina, Yubao Qiu, Youngwook Kim, John S. Kimball, Paolo Tarolli. Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges. Remote Sensing. 2019; 11 (16):1952.
Chicago/Turabian StyleJinyang Du; Jennifer D. Watts; Lingmei Jiang; Hui Lu; Xiao Cheng; Claude Duguay; Mary Farina; Yubao Qiu; Youngwook Kim; John S. Kimball; Paolo Tarolli. 2019. "Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges." Remote Sensing 11, no. 16: 1952.
The long-term variations in snow depth are important in hydrological, meteorological, and ecological implications and climatological studies. The series of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) instruments onboard the Defense Meteorological Satellite Program (DMSP) platforms has provided a consistent 30+ year data record of global observations that is well-suited for the estimation of snow cover, snow depth, and snow water equivalent (SWE). To maximize the use of this continuous microwave observation dataset in long-term snow analysis and obtain an objective result, consistency among the SSM/I and SSMIS sensors is required. In this paper, we evaluated the consistency between the SSM/I and SSMIS concerning the observed brightness temperature (Tb) and the retrieved snow cover area and snow depth from January 2007 to December 2008, where the F13 SSM/I and the F17 SSMIS overlapped. Results showed that Tb bias at 19 GHz spans from −2 to −3 K in snow winter seasons, and from −4 to −5 K in non-snow seasons. There is a slight Tb bias at 37 GHz from −2 to 2 K, regardless of season. For 85 (91) GHz, the bias presents some uncertainty from the scattering effect of the snowpack and atmospheric emission. The overall consistency between SSM/I and SSMIS with respect to snow cover detection is between 80% and 100%, which will result in a maximum snow cover area difference of 25 × 104 km2 in China. The inconsistency in Tb between SSM/I and SSMIS can result in a −2 and −0.67 cm snow depth bias for the dual-channel and multichannel algorithms, respectively. SSMIS tends to yield lower snow depth estimates than SSM/I. Moreover, there are notable bias differences between SSM/I- and SSMIS-estimated snow depths in the tundra and taiga snow classes. Our results indicate the importance of considering the Tb bias in microwave snow cover detection and snow depth retrieval and point out that, due to the sensitivity of bias to seasons, it is better to do the intercalibration with a focus on snow-covered winter seasons. Otherwise, the bias in summer will disturb the calibration coefficients and introduce more error into the snow retrievals if the seasonal difference is not carefully evaluated and separated.
Jianwei Yang; Lingmei Jiang; Liyun Dai; Jinmei Pan; Shengli Wu; Gongxue Wang. The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China. Remote Sensing 2019, 11, 1879 .
AMA StyleJianwei Yang, Lingmei Jiang, Liyun Dai, Jinmei Pan, Shengli Wu, Gongxue Wang. The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China. Remote Sensing. 2019; 11 (16):1879.
Chicago/Turabian StyleJianwei Yang; Lingmei Jiang; Liyun Dai; Jinmei Pan; Shengli Wu; Gongxue Wang. 2019. "The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China." Remote Sensing 11, no. 16: 1879.
Launched on 15 November 2017, China’s FengYun-3D (FY-3D) has taken over prime operational weather service from the aging FengYun-3B (FY-3B). Rather than directly implementing an FY-3B operational snow depth retrieval algorithm on FY-3D, we investigated this and four other well-known snow depth algorithms with respect to regional uncertainties in China. Applicable to various passive microwave sensors, these four snow depth algorithms are the Environmental and Ecological Science Data Centre of Western China (WESTDC) algorithm, the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) algorithm, the Chang algorithm, and the Foster algorithm. Among these algorithms, validation results indicate that FY-3B and WESTDC perform better than the others. However, these two algorithms often result in considerable underestimation for deep snowpack (greater than 20 cm), while the other three persistently overestimate snow depth, probably because of their poor representation of snowpack characteristics in China. To overcome the retrieval errors that occur under deep snowpack conditions without sacrificing performance under relatively thin snowpack conditions, we developed an empirical snow depth retrieval algorithm suite for the FY-3D satellite. Independent evaluation using weather station observations in 2014 and 2015 demonstrates that the FY-3D snow depth algorithm’s root mean square error (RMSE) and bias are 6.6 cm and 0.2 cm, respectively, and it has advantages over other similar algorithms.
Jianwei Yang; Lingmei Jiang; Shengli Wu; Gongxue Wang; Jian Wang; Xiaojing Liu. Development of a Snow Depth Estimation Algorithm over China for the FY-3D/MWRI. Remote Sensing 2019, 11, 977 .
AMA StyleJianwei Yang, Lingmei Jiang, Shengli Wu, Gongxue Wang, Jian Wang, Xiaojing Liu. Development of a Snow Depth Estimation Algorithm over China for the FY-3D/MWRI. Remote Sensing. 2019; 11 (8):977.
Chicago/Turabian StyleJianwei Yang; Lingmei Jiang; Shengli Wu; Gongxue Wang; Jian Wang; Xiaojing Liu. 2019. "Development of a Snow Depth Estimation Algorithm over China for the FY-3D/MWRI." Remote Sensing 11, no. 8: 977.
The snow cover distribution is crucial to Earth's climate and hydrological systems. Snow cover maps from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely utilized in climate research and water-source management. Three MODIS-derived fractional snow cover (FSC) products, namely, MOD10A1 version 6, MODSCAG, and MODAGE, are assessed to evaluate the accuracy of FSC data over the Tibetan Plateau from May 2013 to April 2015. A Landsat-8/ Operational Land Imager (OLI) snow map that is based on linear spectral mixture analysis is regarded as the “reference dataset.” A total of 149 OLI images that span the Tibetan Plateau are used. The performance of these three datasets is compared to the “reference dataset” by using binary and fractional metrics over forest, grass, and bare soil, and in the Himalaya region. The binary classification assessment reveals that larger local illumination angles and vegetation coverage at the snow boundaries resulted in omission errors in MOD10A1, and snow patchiness caused this model to miss snow. MODSCAG correctly identified snow pixels, but overestimated snow at the snow boundaries and at local illumination angles that exceeded 30°. MODAGE precisely recognized snow-free areas, but underestimated snow coverage because of snow patchiness. MODSCAG and MODAGE produced smaller root mean square errors (RMSE) than MOD10A1, likely because spectral mixture analysis is superior to the normalized difference snow index-based empirical method. The behaviors of these products differed at various spatial scales. The RMSE significantly declined with decreasing spatial resolution from 500 m to 2 km. However, when aggregated to 5 km, the RMSE increased because fewer pixels were involved in the calculation.
Shirui Hao; Lingmei Jiang; Jiancheng Shi; Gongxue Wang; Xiaojing Liu. Assessment of MODIS-Based Fractional Snow Cover Products Over the Tibetan Plateau. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2018, 12, 533 -548.
AMA StyleShirui Hao, Lingmei Jiang, Jiancheng Shi, Gongxue Wang, Xiaojing Liu. Assessment of MODIS-Based Fractional Snow Cover Products Over the Tibetan Plateau. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018; 12 (2):533-548.
Chicago/Turabian StyleShirui Hao; Lingmei Jiang; Jiancheng Shi; Gongxue Wang; Xiaojing Liu. 2018. "Assessment of MODIS-Based Fractional Snow Cover Products Over the Tibetan Plateau." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 2: 533-548.
Satellite microwave radiometer measurements have been used to extract snow depth information for approximately three to four decades. However, the coarse spatial resolution of satellite microwave radiobrightness observations affects the snow depth retrieval accuracy and hinders the application of snow depth data in studies on water, energy, and carbon cycles. Therefore, snow depth measurements require better spatial resolution to improve the retrieval accuracy and enhance the resolution of the data. In this paper, an improved linear unmixing method is proposed to improve the accuracy of existing methods and downscale brightness temperatures from satellite observations to improve snow depth estimates. Tests conducted on a dataset consisting of both simulated and satellite data show the effectiveness of the proposed method. The unmixing method is then applied to FengYun-3B satellite/microwave radiation imager measurements for snow depth retrievals, and the resulting snow depths are compared with weather station observations from January to February 2011. The results show that the snow depth estimated from downscaled brightness temperatures performs better than the original data and presents higher correlations and lower root mean square errors.
Xiaojing Liu; Lingmei Jiang; Gongxue Wang; Shirui Hao; Zhizhong Chen. Using a Linear Unmixing Method to Improve Passive Microwave Snow Depth Retrievals. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2018, 11, 4414 -4429.
AMA StyleXiaojing Liu, Lingmei Jiang, Gongxue Wang, Shirui Hao, Zhizhong Chen. Using a Linear Unmixing Method to Improve Passive Microwave Snow Depth Retrievals. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018; 11 (11):4414-4429.
Chicago/Turabian StyleXiaojing Liu; Lingmei Jiang; Gongxue Wang; Shirui Hao; Zhizhong Chen. 2018. "Using a Linear Unmixing Method to Improve Passive Microwave Snow Depth Retrievals." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 11: 4414-4429.
High resolution and long-term soil moisture products play an important role in estimating forest carbon storage and carbon emissions in Genhe, China. In order to obtain the high spatial and temporal resolution of soil moisture datasets in China, this paper proposed a downscaling method for the revised QP model with Dual-Channel Algorithm (QDCA) soil moisture product based on microwave polarization difference index (MPDI) from AMSR2 and Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) from Moderate resolution Imaging Spectroradiometer (MODIS) to derive high resolution of soil moisture data (1km). The downscaling method is validated with the in situ soil moisture data over Genhe in China, and the results showed that the R, bias, RMSE between downscaling revised QDCA soil moisture and in situ measurements is 0.176~0.4156, 009~0.050m 3 m- 3 and 0.056~0.087m 3 m- 3 , respectively. With the different land surface, the accuracy of downscaling soil moisture in grass land cover is higher than the forest land cover.
Huizhen Cui; Lingmei Jiang; Zhuang Zhou; Shirui Hao; Jian Wang; Gongxue Wang. Downscaling of QP Model with Dual-Channel Soil Moisture Retrievals Over Genhe Area in China. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 9118 -9121.
AMA StyleHuizhen Cui, Lingmei Jiang, Zhuang Zhou, Shirui Hao, Jian Wang, Gongxue Wang. Downscaling of QP Model with Dual-Channel Soil Moisture Retrievals Over Genhe Area in China. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():9118-9121.
Chicago/Turabian StyleHuizhen Cui; Lingmei Jiang; Zhuang Zhou; Shirui Hao; Jian Wang; Gongxue Wang. 2018. "Downscaling of QP Model with Dual-Channel Soil Moisture Retrievals Over Genhe Area in China." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 9118-9121.
The high-resolution freeze/thaw (F/T) monitoring plays an important role in studying carbon-nitrogen cycle, soil erosion and climate change in Genhe, China. In this paper, high-resolution downscaled land surface temperature (LST) retrieved from AMSR2 [1] were obtained from previous study [1]. And then were used to downscale the passive microwave (PMW) brightness temperature (TB) from 0.25° to 0.01° through downscaling method of PMW TB [2]. Finally, the downscaled TB data and F/T discriminant function algorithms [3], [4] were adopted to discriminate the surface freeze/thaw status. A comparison between high-resolution F/T state and soil temperature measured at 0~5 cm over Genhe area turned out that the F/T discriminant function algorithm [3] has a total classification accuracy higher than and 70%, and the improved F/T discriminant function algorithm [4] has a total classification accuracy higher than and 60%. From the perspective of orbit, both algorithms had freezing distinguished accuracy high than 90% at ascending and descending orbits. At last, we discussed and analyzed the possible problems of F/T discriminant function algorithms and downscaling method of TB.
Jian Wang; Lingmei Jiang; Xiaokang Kou; Huizhen Cui; Shirui Hao. Verification of Downscaling Method for Near-Surface Freeze/Thaw State Monitoring in Genhe Area of China. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 7168 -7171.
AMA StyleJian Wang, Lingmei Jiang, Xiaokang Kou, Huizhen Cui, Shirui Hao. Verification of Downscaling Method for Near-Surface Freeze/Thaw State Monitoring in Genhe Area of China. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():7168-7171.
Chicago/Turabian StyleJian Wang; Lingmei Jiang; Xiaokang Kou; Huizhen Cui; Shirui Hao. 2018. "Verification of Downscaling Method for Near-Surface Freeze/Thaw State Monitoring in Genhe Area of China." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 7168-7171.