Relationship Between Global Warming and Hurricanes Wind Speed Based on Analyzing MODIS Remote Sensing Data

2019 ◽  
pp. 23-28 ◽  
Author(s):  
Xiaoli Chen
Keyword(s):  
Remote Sensing ◽  
Global Warming ◽  
Wind Speed ◽  
Remote Sensing Data ◽  
Sensing Data

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 Toward the GMF for Wind Speed and Surface Stress Retrieval in Hurricanes Based on the Collocated GPS-Dropsonde and Remote Sensing Data

2020 ◽  
Vol 13 ◽  
pp. 4803-4808
Author(s):  
Olga Stanislavovna Ermakova ◽  
Daniil Alexandrovich Sergeev ◽  
Nikita Sergeevich Rusakov ◽  
Evgeny Ivanovich Poplavsky ◽  
Galina Nikolaevna Balandina ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Wind Speed ◽  
Surface Stress ◽  
Remote Sensing Data ◽  
Sensing Data

On the use of cross-polarized SAR and GPS-sonde measurements for wind speed retrieval in tropical cyclones

2020 ◽  
Author(s):  
Evgeny Poplavsky ◽  
Nikita Rusakov ◽  
Olga Ermakova ◽  
Yuliya Troitskaya ◽  
Daniil Sergeev ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Wind Speed ◽  
Atmospheric Boundary Layer ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Cross Polarization ◽  
Sar Images ◽  
Sensing Data ◽  
High Level

<p>The current investigation is concerned with the study of the dependence of the scattered cross-polarized microwave signal from the Sentinel-1 satellite on the parameters of the marine atmospheric boundary layer based on data obtained from falling NOAA GPS-sondes under tropical cyclone conditions.<br>Field measurements and remote sensing data for hurricanes in the Atlantic and Pacific oceans were analyzed for the period 2016 - 2018. Based on the analysis of data measured by GPS-sondes, averaged wind speed profiles were obtained, while the parameters of the atmospheric boundary layer (drag coefficient and wind friction velocity) were retrieved using the self-similarity property of velocity profiles from measurements in the “wake” part.<br>Sentinel-1 SAR images were used as remote sensing data. Images with cross polarization have a high level of thermal noise (NESZ), which leads to errors when retrieving the NRCS. In this regard, preliminary image processing was performed in the SNAP application.<br>Using the obtained parameters of the atmospheric boundary layer, the data of GRS-sonde measurements and Sentinel-1 SAR images on cross polarization were collocated and the dependences of the NRCS on the parameters of the atmospheric boundary layer were obtained.</p><div>This work was supported by the RFBR projects No. 19-05-00249, 19-05-00366, 18-35-20068 (remote sensing data analysis) and RSF No. 19-17-00209 (GPS-sondes data assimilation and processing).</div>

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market

Processing of remote sensing data for detection and recognition of object classes

2004 ◽  
Vol 10 (5-6) ◽  
pp. 175-177
Author(s):  
S.L. Kravtsov ◽  
◽  
L.V. Oreshkina ◽  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Object Classes ◽  
Detection And Recognition

A procedure for integrated assessment of landscape fire risk using remote sensing data

2011 ◽  
Vol 17 (6) ◽  
pp. 30-44
Author(s):  
Yu.V. Kostyuchenko ◽  
◽  
M.V. Yushchenko ◽  
I.M. Kopachevskyi ◽  
S. Levynsky ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Integrated Assessment ◽  
Remote Sensing Data ◽  
Fire Risk ◽  
Sensing Data
Sign in / Sign up

Export Citation Format

Share Document