Wheat cycle monitoring using radar data and a neural network trained by a model

被引：36

作者：

Del Frate, F ^{[1
]}

Ferrazzoli, P

Guerriero, L

Strozzi, T

Wegmüller, U

Cookmartin, G

Quegan, S

机构：

[1] Univ Roma Tor Vergata, DISP, Fac Ingn, I-00133 Rome, Italy

[2] Gamma Remote Sensing, CH-3074 Muri BE, Switzerland

[3] Univ Sheffield, Sheffield Ctr Earth Observat Sci, Sheffield S3 7RH, S Yorkshire, England

来源：

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2004年 / 42卷 / 01期

关键词：

crops; neural networks; radar; retrieval; scattering model;

D O I：

10.1109/TGRS.2003.817200

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

This paper describes an algorithm aimed at monitoring the soil moisture and the growth cycle of wheat fields using radar data. The algorithm is based on neural networks trained by model simulations and multitemporal ground data measured on fields taken as a reference. The backscatter of wheat canopies is modeled by a discrete approach, based on the radiative transfer theory and including multiple scattering effects. European Remote Sensing satellite synthetic aperture radar signatures and detailed ground truth, collected over wheat fields at the Great Driffield (U.K.) site, are used to test the model and train the networks. Multitemporal, multifirequency data collected by the Radiometer-Scatterometer (RASAM) instrument at the Central Plain site are used to test the retrieval algorithm.

引用

页码：35 / 44

页数：10