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Yufang Zhang
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

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Journal article
Published: 22 December 2017 in Remote Sensing
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The C-band radar instruments onboard the two-satellite GMES Sentinel-1 constellation provide global measurements with short revisit time (about six days) and medium spatial resolution (5 × 20 m), which are appropriate for watershed scale hydrological applications. This paper aims to explore the potential of Sentinel-1 for estimating surface soil moisture using a multi-temporal approach. To this end, a linear mixed effects (LME) model was developed over Poyang Lake ungauged zone, using time series Sentinel 1A and 1B images and soil moisture ground measurements from 15 automatic observation sites. The model assumed a linear relationship that varied with both time and space between soil moisture and backscattering coefficient (SM-σ0). Results showed that three LME models developed with different polarized σ0 images all meet the European Space Agency (ESA) accuracy requirement for GMES soil moisture product (≤5% in volume), with the vertical transmit and vertical receive (VV) polarized model achieving the best performance. However, the SM-σ0 relationship was found to depend strongly on space, making it difficult to predict absolute soil moisture for each grid. Therefore, a relative soil moisture index was then proposed to correct for site effect. When compared with those of the linear fixed effects model, the soil moisture indices predicted by the LME model captured the temporal dynamics of measured soil moisture better, with the overall R2 and cross-validated R2 being 0.68 and 0.64, respectively. These results indicate that the LME model can be effectively applied to estimate soil moisture from multi-temporal Sentinel-1 images, which is useful for monitoring flood and drought disasters, and for improving stream flow prediction over ungauged zones.

ACS Style

Yufang Zhang; Jianya Gong; Kun Sun; Jianmin Yin; Xiaoling Chen. Estimation of Soil Moisture Index Using Multi-Temporal Sentinel-1 Images over Poyang Lake Ungauged Zone. Remote Sensing 2017, 10, 12 .

AMA Style

Yufang Zhang, Jianya Gong, Kun Sun, Jianmin Yin, Xiaoling Chen. Estimation of Soil Moisture Index Using Multi-Temporal Sentinel-1 Images over Poyang Lake Ungauged Zone. Remote Sensing. 2017; 10 (2):12.

Chicago/Turabian Style

Yufang Zhang; Jianya Gong; Kun Sun; Jianmin Yin; Xiaoling Chen. 2017. "Estimation of Soil Moisture Index Using Multi-Temporal Sentinel-1 Images over Poyang Lake Ungauged Zone." Remote Sensing 10, no. 2: 12.

Journal article
Published: 24 March 2015 in Remote Sensing
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The Tizinafu watershed has a complex mountainous terrain in the western Kunlun Mountains; little study has been done on the spatial and temporal characteristics of snow cover in the region. Daily snow cover data of 10 hydrological years (October 2002 to September 2012) in the watershed were generated by combining MODIS Terra (MOD10A1) and Aqua (MYD10A1) snow cover products and employing a nine-day temporal filter for cloud reduction. The accuracy and window size of the temporal filter were assessed using a simulation approach. Spatial and temporal characteristics of snow cover in the watershed were then analyzed. Our results showed that snow generally starts melting in March and reaches the minimum in early August in the watershed. Snow cover percentages (SCPs) in all five elevation zones increase consistently with the rise of elevation. Slope doesn’t play a major role in snow cover distribution when it exceeds 10°. The largest SCP difference is between the south and the other aspects and occurs between mid-October and mid-November with decreasing SCP, indicating direct solar radiation may cause the reduction of snow cover. While both the mean snow cover durations (SCDs) of the hydrological years and of the snowmelt seasons share a similar spatial pattern to the topography of the watershed, the coefficient of variation of the SCDs exhibits an opposite spatial distribution. There is a significant correlation between annual mean SCP and annual total stream flow, indicating that snowmelt is a major source of stream runoff that might be predictable with SCP.

ACS Style

Jiangfeng She; Yufang Zhang; Xingong Li; Xuezhi Feng. Spatial and Temporal Characteristics of Snow Cover in the Tizinafu Watershed of the Western Kunlun Mountains. Remote Sensing 2015, 7, 3426 -3445.

AMA Style

Jiangfeng She, Yufang Zhang, Xingong Li, Xuezhi Feng. Spatial and Temporal Characteristics of Snow Cover in the Tizinafu Watershed of the Western Kunlun Mountains. Remote Sensing. 2015; 7 (4):3426-3445.

Chicago/Turabian Style

Jiangfeng She; Yufang Zhang; Xingong Li; Xuezhi Feng. 2015. "Spatial and Temporal Characteristics of Snow Cover in the Tizinafu Watershed of the Western Kunlun Mountains." Remote Sensing 7, no. 4: 3426-3445.

Articles
Published: 19 November 2013 in International Journal of Remote Sensing
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Snow and glaciers in the mountain watersheds of the Tarim River basin in western China provide the primary water resources to cover the needs of downstream oases. Remote sensing provides a practical approach to monitoring the change in snow and glacier cover in those mountain watersheds. This study investigated the change in snow and glacier cover in one such mountain watershed using multisource remote-sensing data, including the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat (Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+)), Corona, and Google EarthTM imagery. With 10 years’ daily MODIS snow-cover data from 2002 to 2012, we used two de-cloud methods before calculating daily snow-cover percentage (SCP), annual snow-cover frequency (SCF), and annual minimum snow-cover percentage (AMSCP) for the watershed. Mann–Kendall analysis showed no significant trend in any of those snow-cover characterizations. With a total of 22 Landsat images from 1967 to 2011, we used band ratio and supervised classification methods for snow classification for Landsat TM/ETM+ images and MSS images, respectively. The Landsat snow-cover data were divided into two periods (1976–2002 and 2004–2011). Statistical tests indicated no significant difference in either the variance or mean of SCPs between the two periods. Three glaciers were identified from Landsat images of 1998 and 2011, and their total area increased by 12.6%. In addition, three rock glaciers were also identified on both the Corona image of 1968 and the Google high-resolution image of 2007, and their area increased by 2.5%. Overall, based on multisource remote-sensing data sets, our study found no evidence of significant changes in snow and glacier cover in the watershed.

ACS Style

Jiangfeng She; Yufang Zhang; Xingong Li; Yaning Chen. Changes in snow and glacier cover in an arid watershed of the western Kunlun Mountains using multisource remote-sensing data. International Journal of Remote Sensing 2013, 35, 234 -252.

AMA Style

Jiangfeng She, Yufang Zhang, Xingong Li, Yaning Chen. Changes in snow and glacier cover in an arid watershed of the western Kunlun Mountains using multisource remote-sensing data. International Journal of Remote Sensing. 2013; 35 (1):234-252.

Chicago/Turabian Style

Jiangfeng She; Yufang Zhang; Xingong Li; Yaning Chen. 2013. "Changes in snow and glacier cover in an arid watershed of the western Kunlun Mountains using multisource remote-sensing data." International Journal of Remote Sensing 35, no. 1: 234-252.