CORRELATION BETWEEN INSAR SATELLITE REMOTE SENSING AND IN-SITU MEASUREMENTS

Author(s):  
Adrian Andronic
Oceanologia ◽  
2010 ◽  
Vol 52 (2) ◽  
pp. 197-210 ◽  
Author(s):  
Claudia Giardino ◽  
Mariano Bresciani ◽  
Renata Pilkaitytė ◽  
Marco Bartoli ◽  
Artūras Razinkovas

2008 ◽  
Vol 26 (7) ◽  
pp. 2019-2035 ◽  
Author(s):  
Y. H. Ahn ◽  
P. Shanmugam ◽  
J. E. Moon ◽  
J. H. Ryu

Abstract. With the aim to map and monitor a low-salinity water (LSW) plume in the East China Sea (ECS), we developed more robust and proper regional algorithms from large in-situ measurements of apparent and inherent optical properties (i.e. remote sensing reflectance, Rrs, and absorption coefficient of coloured dissolved organic matter, aCDOM) determined in ECS and neighboring waters. Using the above data sets, we derived the following relationships between visible Rrs and absorption by CDOM, i.e. Rrs (412)/Rrs (555) vs. aCDOM (400) (m−1) and aCDOM (412) (m−1) with a correlation coefficient R2 0.67 greater than those noted for Rrs (443)/Rrs (555) and Rrs (490)/Rrs (555) vs. aCDOM (400) (m−1) and aCDOM (412) (m−1). Determination of aCDOM (m−1) at 400 nm and 412 nm is particularly necessary to describe its absorption as a function of wavelength λ using a single exponential model in which the spectral slope S as a proxy for CDOM composition is estimated by the ratio of aCDOM at 412 nm and 400 nm and the reference is explained simply by aCDOM at 412 nm. In order to derive salinity from the absorption coefficient of CDOM, in-situ measurements of salinity made in a wide range of water types from dense oceanic to light estuarine/coastal systems were used along with in-situ measurements of aCDOM at 400 nm, 412 nm, 443 nm and 490 nm. The CDOM absorption at 400 nm was better inversely correlated (R2=0.86) with salinity than at 412 nm, 443 nm and 490 nm (R2=0.85–0.66), and this correlation corresponded best with an exponential (R2=0.98) rather than a linear function of salinity measured in a variety of water types from this and other regions. Validation against a discrete in-situ data set showed that empirical algorithms derived from the above relationships could be successfully applied to satellite data over the range of water types for which they have been developed. Thus, we applied these algorithms to a series of SeaWiFS images for the derivation of CDOM and salinity in the context of operational mapping and monitoring of the springtime evolution of LSW plume in the ECS. The results were very encouraging and showed interesting features in surface CDOM and salinity fields in the vicinity of the Yangtze River estuary and its offshore domains, when a regional atmospheric correction (SSMM) was employed instead of the standard (global) SeaWiFS algorithm (SAC) which revealed large errors around the edges of clouds/aerosols while masking out the nearshore areas. Nevertheless, there was good consistency between these two atmospheric correction algorithms over the relatively clear regions with a mean difference of 0.009 in aCDOM (400) (m−1) and 0.096 in salinity (psu). This study suggests the possible utilization of satellite remote sensing to assess CDOM and salinity and thus provides great potential in advancing our knowledge of the shelf-slope evolution and migration of the LSW plume properties in the ECS.


2021 ◽  
pp. 105623
Author(s):  
Stefan Becker ◽  
Ramesh Prasad Sapkota ◽  
Binod Pokharel ◽  
Loknath Adhikari ◽  
Rudra Prasad Pokhrel ◽  
...  

2014 ◽  
Vol 7 (9) ◽  
pp. 3095-3112 ◽  
Author(s):  
P. Sawamura ◽  
D. Müller ◽  
R. M. Hoff ◽  
C. A. Hostetler ◽  
R. A. Ferrare ◽  
...  

Abstract. Retrievals of aerosol microphysical properties (effective radius, volume and surface-area concentrations) and aerosol optical properties (complex index of refraction and single-scattering albedo) were obtained from a hybrid multiwavelength lidar data set for the first time. In July 2011, in the Baltimore–Washington DC region, synergistic profiling of optical and microphysical properties of aerosols with both airborne (in situ and remote sensing) and ground-based remote sensing systems was performed during the first deployment of DISCOVER-AQ. The hybrid multiwavelength lidar data set combines ground-based elastic backscatter lidar measurements at 355 nm with airborne High-Spectral-Resolution Lidar (HSRL) measurements at 532 nm and elastic backscatter lidar measurements at 1064 nm that were obtained less than 5 km apart from each other. This was the first study in which optical and microphysical retrievals from lidar were obtained during the day and directly compared to AERONET and in situ measurements for 11 cases. Good agreement was observed between lidar and AERONET retrievals. Larger discrepancies were observed between lidar retrievals and in situ measurements obtained by the aircraft and aerosol hygroscopic effects are believed to be the main factor in such discrepancies.


2017 ◽  
Vol 38 (7) ◽  
pp. 535-545 ◽  
Author(s):  
Jae-Jin Park ◽  
◽  
Sangwoo Oh ◽  
Kyung-Ae Park ◽  
Pierre-Yves Foucher ◽  
...  

Author(s):  
D. Varade ◽  
O. Dikshit

<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>


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