Snow Cover Monitoring in Qinghai-Tibetan Plateau Based on Chinese Fengyun-3/VIRR Data

Zhang, Yonghong; Kan, Xi; Ren, Wei; Cao, Ting; Tian, Wei; Wang, Jiangeng

doi:10.1007/s12524-015-0527-4

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…Among all of the frequency bands, the channels in the 10.65 GHz band are capable of penetrating clouds and rain and are mainly used to obtain global surface sea temperature (SST), wind speed, SM content, and other geophysical parameters under all weather conditions. FY-3C was launched in September 2013 and has been recognized as the first official service satellite and a suitable replacement for FY-3A as the new forenoon satellite [30]. FY-3C VSM products are retrieved from the L1 brightness temperature (BT) data collected by MWRI using a new inversion technique, i.e., the Q p model, which was built using advanced integral equation (AIE) model simulations of microwave emissions [14].…”

Section: Fy-3c Vsmmentioning

confidence: 99%

Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture

et al. 2020

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Land surface soil moisture (SM) monitoring is crucial for global water cycle and agricultural dryness research. The FengYun-3C Microwave Radiation Imager (FY-3C/MWRI) collects various Earth geophysical parameters, and the FY-3C/MWRI SM product (FY-3C VSM) has been widely applied to determine regional-scale surface SM contents. The FY-3C VSM retrieval accuracy in different seasons was evaluated by calculating the root mean square error (RMSE), unbiased RMSE (ubRMSE), mean absolute error (MAE), and correlation coefficient (R) values between the retrieved and measured SM. A lower accuracy in July (RMSE = 0.164 cm3/cm3, ubRMSE = 0.130 cm3/cm3, and MAE = 0.120 cm3/cm3) than in the other months was found due to the impacts of vegetation and climate variations. To show a detailed relationship between SM and multiple factors, including vegetation coverage, location, and elevation, quantile regression (QR) models were used to calculate the correlations at different quantiles. Except for the elevation at the 0.9 quantile, the QR models of the measured SM with the FY-3C VSM, MODIS NDVI, latitude, and longitude at each quantile all passed the significance test at the 0.005 level. Thus, the MODIS NDVI, latitude, and longitude were selected for error correction during the surface SM retrieval process using FY-3C VSM. Multivariate linear regression (MLR) and multivariate back-propagation neural network (MBPNN) models with different numbers of input variables were built to improve the SM monitoring results. The MBPNN model with three inputs (MBPNN-3) achieved the highest R (0.871) and lowest RMSE (0.034 cm3/cm3), MAE (0.026 cm3/cm3), and mean relative error (MRE) (20.7%) values, which were better than those of the MLR models with one, two, or three independent variables (MLR-1, -2, -3) and those of the MBPNN models with one or two inputs (MBPNN-1, -2). Then, the MBPNN-3 model was applied to generate the regional SM in the United States from January 2019 to October 2019. The estimated SM images were more consistent with the measured SM than the FY-3C VSM. This work indicated that combining FY-3C VSM data with the MBPNN-3 model could provide precise and reliable SM monitoring results.

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Section: Fy-3c Vsmmentioning

confidence: 99%

Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture

et al. 2020

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“…In fact, ATD uses ±1 d information and is similar to the works of Li et al (2008) and Gafurov and Bárdossy (2009), who used ±1 or ±2 d information. However, ATD is different from direct temporal replacement (Zhao and Fernandes, 2009), with the sacrifice of the 1 d temporal resolution.…”

Section: Adjacent Temporal Deduction (Atd)mentioning

confidence: 99%

The recent developments in cloud removal approaches of MODIS snow cover product

Jing

Shen

et al. 2019

Hydrol. Earth Syst. Sci.

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Abstract. The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.

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“…However, it is difficult to separate the reflectance from the MIR signal, and there is no such published algorithm for GF-4 IRS data so far. The brightness temperature could be a substitute for the reflectance in the MIR band for removing thick clouds [48]. However, the relative size of the temperature of cloud and snow cover is not stable, which may weaken the difference between the brightness temperature of cloud and snow cover.…”

Section: Cloud Contaminationmentioning

confidence: 99%

Snow Cover Monitoring with Chinese Gaofen-4 PMS Imagery and the Restored Snow Index (RSI) Method: Case Studies

et al. 2018

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Snow cover is an essential climate variable of the Global Climate Observing System. Gaofen-4 (GF-4) is the first Chinese geostationary satellite to obtain optical imagery with high spatial and temporal resolution, which presents unique advantages in snow cover monitoring. However, the panchromatic and multispectral sensor (PMS) onboard GF-4 lacks the shortwave infrared (SWIR) band, which is crucial for snow cover detection. To reach the potential of GF-4 PMS in snow cover monitoring, this study developed a novel method termed the restored snow index (RSI). The SWIR reflectance of snow cover is restored firstly, and then the RSI is calculated with the restored reflectance. The distribution of snow cover can be mapped with a threshold, which should be adjusted according to actual situations. The RSI was validated using two pairs of GF-4 PMS and Landsat-8 Operational Land Imager images. The validation results show that the RSI can effectively map the distribution of snow cover in these cases, and all of the classification accuracies are above 95%. Signal saturation slightly affects PMS images, but cloud contamination is an important limiting factor. Therefore, we propose that the RSI is an efficient method for monitoring snow cover from GF-4 PMS imagery without requiring the SWIR reflectance.

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Snow Cover Monitoring in Qinghai-Tibetan Plateau Based on Chinese Fengyun-3/VIRR Data

Cited by 9 publications

References 21 publications

Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture

Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture

The recent developments in cloud removal approaches of MODIS snow cover product

Snow Cover Monitoring with Chinese Gaofen-4 PMS Imagery and the Restored Snow Index (RSI) Method: Case Studies

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