scholarly journals Monitoring Soil Salinization in Keriya River Basin, Northwestern China Using Passive Reflective and Active Microwave Remote Sensing Data

2015 ◽  
Vol 7 (7) ◽  
pp. 8803-8829 ◽  
Author(s):  
Ilyas Nurmemet ◽  
Abduwasit Ghulam ◽  
Tashpolat Tiyip ◽  
Racha Elkadiri ◽  
Jian-Li Ding ◽  
...  
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Soil Salinization ◽  
Northwestern China ◽  
Sensing Data ◽  
Active Microwave Remote Sensing

scholarly journals Effect of Land Cover Heterogeneity on Soil Moisture Retrieval Using Active Microwave Remote Sensing Data

2009 ◽  
Vol 1 (2) ◽  
pp. 80-91 ◽  
Author(s):  
Tarendra Lakhankar ◽  
Hosni Ghedira ◽  
Marouane Temimi ◽  
Amir Azar ◽  
Reza Khanbilvardi
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Sensing Data ◽  
Active Microwave Remote Sensing

scholarly journals Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia

2020 ◽  
Vol 12 (1) ◽  
pp. 1666-1678
Author(s):  
Mohammed H. Aljahdali ◽  
Mohamed Elhag
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Wind Speed ◽  
Red Sea ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Optical Data ◽  
Mean Values ◽  
Sensing Data ◽  
The Mean

AbstractRabigh is a thriving coastal city located at the eastern bank of the Red Sea, Saudi Arabia. The city has suffered from shoreline destruction because of the invasive tidal action powered principally by the wind speed and direction over shallow waters. This study was carried out to calibrate the water column depth in the vicinity of Rabigh. Optical and microwave remote sensing data from the European Space Agency were collected over 2 years (2017–2018) along with the analog daily monitoring of tidal data collected from the marine station of Rabigh. Depth invariant index (DII) was implemented utilizing the optical data, while the Wind Field Estimation algorithm was implemented utilizing the microwave data. The findings of the current research emphasis on the oscillation behavior of the depth invariant mean values and the mean astronomical tides resulted in R2 of 0.75 and 0.79, respectively. Robust linear regression was established between the astronomical tide and the mean values of the normalized DII (R2 = 0.81). The findings also indicated that January had the strongest wind speed solidly correlated with the depth invariant values (R2 = 0.92). Therefore, decision-makers can depend on remote sensing data as an efficient tool to monitor natural phenomena and also to regulate human activities in fragile ecosystems.

scholarly journals ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

2018 ◽  
Vol IV-5 ◽  
pp. 223-228 ◽  
Author(s):  
D. Varade ◽  
O. Dikshit
Keyword(s):  
Remote Sensing ◽  
Water Resource Management ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Physical Relationship ◽  
Sensing Data ◽  
Surface Snow ◽  
Sentinel 2

<p><strong>Abstract.</strong> Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.</p>

Research on estimation crop planting area by integrating the optical and microwave remote sensing data

2015 ◽  
Author(s):  
Jiang Liu ◽  
Fan Yu ◽  
Dandan Liu ◽  
Jing Tian ◽  
Weicheng Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Sensing Data ◽  
Crop Planting
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