In this work, remote sensing reflectance (Rrs) spectra of the Zhejiang coastal water in the East China Sea (ECS) were simulated by using the Hydrolight software with field data as input parameters. The seawater along the Zhejiang coast is typical Case II water with complex optical properties. A field observation was conducted in the Zhejiang coastal region in late May of 2016, and the concentration of ocean color constituents (pigment, SPM and CDOM), IOPs (absorption and backscattering coefficients) and Rrs were measured at 24 stations of 3 sections covering the turbid to clear inshore coastal waters. Referring to these ocean color field data, an ocean color model suitable for the Zhejiang coastal water was setup and applied in the Hydrolight. A set of 11 remote sensing reflectance spectra above water surface were modeled and calculated. Then, the simulated spectra were compared with the filed measurements. Finally, the spectral shape and characteristics of the remote sensing reflectance spectra were analyzed and discussed.
This paper evaluates the rain effects on Ku-band radar backscatter at low incidence angles. The data used consisted of the sea surface backscatter and averaged rain rates from Tropical Rainfall Mapping Mission precipitation radar (TRMM PR) measurements and the collocated 10-m height numerical prediction wind speeds from the European Centre for Medium-Range Weather Forecasts (ECMWF). The wind-induced backscatter was estimated by the Ku-band low incidence backscatter model (KuLMOD) and possible bias due to different wind speed inputs was considered. The rain effect was analysed by comparing the TRMM PR-measured surface backscatter for the rain-affected sea surface with the collocated wind-induced backscatter. We found that the surface backscatter decreases with increases in the averaged rain rate. The rain-induced backscatter was clearly dependent of the wind speed and was slightly dependent of the incidence angle. Results show that it is necessary to develop a wind and rain backscatter model instead of single wind backscatter model.
A Ku-band low incidence backscatter model (KuLMOD) for retrieving wind speeds from Tropical Rainfall Mapping Mission (TRMM) precipitation radar (PR) data is proposed. The data set consisted of TRMM PR observations and collocated National Data Buoy Center (NDBC) and Tropical Ocean Global Atmosphere program buoy-measured wind and wave data. The TRMM PR data properties were analyzed with regard to their dependence on spatial resolution, wind speed, relative wind direction, and significant wave height. The KuLMOD model was developed using incidence angles (0.5 to 6.5 deg) and wind speeds (1.5 to 16.5 m/s) as inputs. The model coefficients were derived by fitting the collocated data. The KuLMOD-derived normalized radar cross section, σ0, was compared with those obtained from the TRMM PR observations and a quasi-specular theoretical model and showed good agreement. With the KuLMOD, the wind speeds were retrieved from the TRMM PR data using the least squares method and validated with the buoy measurements, yielding a root mean square error of 1.45 m/s. The retrieval accuracies for the different incidence angles, wind speeds, and spatial resolutions were obtained.
KEYWORDS: Data modeling, Radar, Ku band, Data centers, Wind measurement, Satellites, Associative arrays, Systems modeling, Antennas, Atmospheric modeling
A new Ku-band low incidence model (KuLMOD) is proposed for retrieving wind speeds from Tropical Rainfall Mapping Mission (TRMM) precipitation radar (PR) data. The data set consisted of TRMM PR observations and collocated National Data Buoy Center (NDBC) buoy-measured wind and wave data. The TRMM PR data properties were analyzed regarding their dependence on the wind speed. The KuLMOD model was developed using incidence angles (0.5–6.5°) and wind speeds (1.5–16.5 m/s) as inputs. The model coefficients were derived by fitting the collocated data. With the KuLMOD, the wind speeds were retrieved from the TRMM PR data using the least squares method and validated with the NDBC buoy measurements, yielding a root mean square error of 1.57 m/s. The retrieval accuracies for the different incidence angles and wind speeds are presented.
Oceanic internal waves are often observed by SAR. So SAR provides a new technique for measuring internal wave in a large area. And it is complementary to traditional measurements. The procedures are given in this paper for extracting the direction, wavelength, amplitude, speed and depth of internal waves. ENVISAT ASAR and Radarsat-2 SAR images of South China Sea are used to extract the parameters. And HJ-1 optical images are used to assist. Then some in-situ data from buoy is used to verifying the extraction results. The times of in-situ data and SAR image are similar. The results are shown that: 1) The internal wave parameter can be extracted from SAR images, although sometime the extraction needs other data. 2) The error of wave direction between SAR and in-situ is less than 15 degree. The error of wave amplitude between SAR and in-situ is less than 15m, the relative error is less than 20%. 3) The wavelength of internal wave can’t be measured by buoy. The wave depth, measured by buoy, is the depth where the velocity of flow is maximum. It isn’t the depth of internal wave.
This paper primarily proposes simple C-band empirical models between ocean wave significant wave
height (Hs) and SAR azimuth cutoff using RADARSAT-2 fine quad-polarization mode data. The
empirical models of VV, HH and VH polarization relate the Hs to the cutoff divided by
range-velocity-ratio with approximatly linear relationships. Compared from NDBC buoy data,
retrieved Hs by empirical models have the root mean square (Rms) errors of 0.62 m, 0.52 m and 0.70 m
for VV, HH and VH polarization, respectively. Particularly, HH polarization presents the best Hs
retrieval performance.
This paper compares the wind speed retrieval methods on C-band multi-polarization SAR measurements to find out the most appropriate one for each polarization data. The RADARSAT-2 SAR quad-polarization (VV+HH+VH+HV) data and NDBC buoy wind data were collocated. For VVpolarization, the retrieved wind speed are compared among four geophysical model function (GMF). For HH polarization, the retrieved wind speed are compared among four polarization ratio model (PR) based on CMOD5 GMF. For VH polarization, the retrieved wind speed are compared between two linear models. Comparisons show all of three polarimetric SAR data have the ability of retrieving wind speed. Based on the error analysis, the commendatory methods are proposed for each polarization.
A new method is proposed to extract the directional ocean wave spectra from the dual-polarization SAR imagery. Firstly,
a new SAR - ocean transform is constructed by combining the quasi-linear transforms of the dual-polarization image
spectra and cross spectra. The modulation transfer function (MTF) of the transform depends only on the tilt MTF. The
uncertainty of MTFs in hydrodynamic and velocity bunching modulation can’t bring extra errors. Secondly, the 180°
ambiguity of wave spectra is removed by the imaginary part of dual-polarization cross spectra. Finally, Radarsat-2 quad-polarization SAR imagery was collected to validate the performance. The extracted wave parameters are compared with
the ones from buoy. Comparisons preliminarily show the potential of wave spectra extraction from dual-polarization
SAR imagery. More cases will be studied.
In this paper the significant wave height (SWH) and the wind field products of ECMWF re-analysis data are used to
derive the location of typhoon center, to analyze the temporal and spatial features of the SWH induced by typhoons, and
to study the relationships between the SWH and wind speed. The results are compared with merged SWH data from
several satellite altimeters (GFO, TOPEX/Poseidon, Jason-1 and Envisat) and wind vectors from QuikSCAT. Typhoon
eyes are observed by using SAR and MODIS data. It is shown that (1) the spatial distribution of wind fields from
ECMWF re-analysis data is almost in accordance with that of wind fields from QuikSCAT; (2) the spatial distribution of
SWH from ECMWF re-analysis data is almost in accordance with that of SWH from merged SWH data; (3) the
distribution of higher wind speed and higher wave height are consistent with the SWH and the wind field product of
ECMWF re-analysis data; (4) the centers of typhoon waves lag behind the centers of typhoons; (5) the top of typhoon
move faster that the bottom in the case of Saomai.
This paper focuses on the coefficients in the retrieval model of wet troposphere path delay. The kind of microwave
radiometers with three frequency channels, such as TOPEX/Poseidon microwave radiometer (TMR) and Jason-1
microwave radiometer (JMR), is discussed. A process of extracting these retrieval coefficients from the data of bright
temperature and relevant physical quantities is presented. The data of JMR are used to extract the retrieval coefficients
and validate this extracting process. A good agreement is shown between the data retrieved with the retrieval coefficients
and the data of JMR.
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