A Model-Based Inversion of Rough Soil Surface Parameters From Radar Measurements

2001 ◽  
Vol 15 (2) ◽  
pp. 173-200 ◽  
Author(s):  
K.S. Chen ◽  
T.D. Wu ◽  
J.C. Shi
Keyword(s):  
Soil Surface ◽  
Surface Parameters ◽  
Model Based ◽  
Radar Measurements

scholarly journals Potential of ASAR/ENVISAT for the characterization of soil surface parameters over bare agricultural fields

2005 ◽  
Vol 96 (1) ◽  
pp. 78-86 ◽  
Author(s):  
N. Holah ◽  
N. Baghdadi ◽  
M. Zribi ◽  
A. Bruand ◽  
C. King
Keyword(s):  
Soil Surface ◽  
Agricultural Fields ◽  
Surface Parameters

scholarly journals Estimating Rain Microphysical Characteristics Using S-Band Dual-Polarization Radar in South Korea

2020 ◽  
Vol 37 (6) ◽  
pp. 1067-1084 ◽  
Author(s):  
Hae-Lim Kim ◽  
Sung-Hwa Jung ◽  
Kun-Il Jang
Keyword(s):  
South Korea ◽  
Size Distribution ◽  
Empirical Relationship ◽  
Rain Gauge ◽  
Physical Parameters ◽  
Sampling Errors ◽  
Dual Polarization ◽  
Model Based ◽  
Microphysical Parameters ◽  
Radar Measurements

AbstractRaindrop size distribution (DSD) observed using a disdrometer can be represented by a constrained-gamma (C-G) DSD model based on the empirical relationship between shape (µ) and slope (Λ). The C-G DSD model can be used to retrieve DSDs and rain microphysical parameters from dual-polarization radar measurements of reflectivity (ZH) and differential reflectivity (ZDR). This study presents a new µ–Λ relationship to characterize rain microphysics in South Korea using a two-dimensional video disdrometer (2DVD) and Yong-in S-band dual-polarization radar. To minimize sampling errors from the 2DVD and radar measurements, measured size distributions are truncated by particle size and velocity-based filtering and compared with rain gauge measurement. The calibration biases of radar ZH and ZDR were calculated using the self-consistency constraint and vertical pointing measurements. The derived µ–Λ relationship was verified using the mass-weighted mean diameter (Dm) and standard deviation of the size distribution (σm), calculated from the 2DVD, for comparison with existing µ–Λ relationships for Florida and Oklahoma. The Dm–σm relationship derived from the 2DVD corresponded well with the µ–Λ relationship. The µ–Λ relationship derived for the Korean Peninsula was similar to Florida, and both generally had larger µ values than Oklahoma for the same Λ. The derived µ–Λ relationship was applied to retrieve DSD parameters from polarimetric radar data, and the retrieved DSDs and derived physical parameters were evaluated and compared with the 2DVD measurements. The polarization radar-based C-G DSD model characterized rain microphysics more accurately than the exponential DSD model. The C-G DSD model based on the newly derived µ–Λ relationship performed the best at retrieving rain microphysical parameters.

Retrieval of Soil Surface Parameters via a Polarimetric Two-Scale Model

2011 ◽  
Vol 49 (7) ◽  
pp. 2531-2547 ◽  
Author(s):  
Antonio Iodice ◽  
Antonio Natale ◽  
Daniele Riccio
Keyword(s):  
Soil Surface ◽  
Scale Model ◽  
Surface Parameters

Neural networks for the inversion of soil surface parameters from synthetic aperture radar satellite data

2004 ◽  
Vol 31 (1) ◽  
pp. 95-108 ◽  
Author(s):  
Mahmod Reza Sahebi ◽  
Ferdinand Bonn ◽  
Goze B Bénié
Keyword(s):  
Neural Networks ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Synthetic Aperture Radar ◽  
Satellite Data ◽  
Input Data ◽  
Soil Surface ◽  
Synthetic Aperture ◽  
Surface Parameters ◽  
Aperture Radar

This paper presents an application of neural networks to the extraction of bare soil surface parameters such as roughness and soil moisture content using synthetic aperture radar (SAR) satellite data. It uses a fast learning algorithm for training a multilayer feedforward neural network using the Kalman filter technique. Two different databases (theoretical and empirical) were used for the learning stage. Each database was configured as single and multiangular sets of input data (data acquired at two different incidence angles) that are compatible with data from one and two satellite images, respectively. All the configurations are trained and then evaluated using RADARSAT-1 and simulated data. The empirical (measured) database with the multiangular set of input data configuration had the best accuracy with a mean error of 1.54 cm for root mean square (rms) height of the surface roughness and 2.45 for soil dielectric constant in the study area. Based on these results the proposed approach was applied on RADARSAT-1 images from the Chateauguay watershed area (Quebec, Canada) and the final results are presented in the form of roughness and humidity maps.Key words: neural networks, Kalman filter, RADARSAT, SAR, soil roughness, soil moisture.

Sign in / Sign up

Export Citation Format

Share Document