Soil moisture estimation over vegetated agricultural areas: Tigris Basin, Turkey from Radarsat-2 data by polarimetric decomposition models and a generalized regression neural network
Yükleniyor...
Tarih
2017
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
MDPI
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Determining the soil moisture in agricultural fields is a significant parameter to use irrigation systems efficiently. In contrast to standard soil moisture measurements, good results might be acquired in a shorter time over large areas by remote sensing tools. In order to estimate the soil moisture over vegetated agricultural areas, a relationship between Radarsat-2 data and measured ground soil moistures was established by polarimetric decomposition models and a generalized regression neural network (GRNN). The experiments were executed over two agricultural sites on the Tigris Basin, Turkey. The study consists of four phases. In the first stage, Radarsat-2 data were acquired on different dates and in situ measurements were implemented simultaneously. In the second phase, the Radarsat-2 data were pre-processed and the GPS coordinates of the soil sample points were imported to this data. Then the standard sigma backscattering coefficients with the Freeman–Durden and H/A/α polarimetric decomposition models were employed for feature extraction and a feature vector with four sigma backscattering coefficients (σhh, σhv, σvh, and σvv) and six polarimetric decomposition parameters (entropy, anisotropy, alpha angle, volume scattering, odd bounce, and double bounce) were generated for each pattern. In the last stage, GRNN was used to estimate the regional soil moisture with the aid of feature vectors. The results indicated that radar is a strong remote sensing tool for soil moisture estimation, with mean absolute errors around 2.31 vol %, 2.11 vol %, and 2.10 vol % for Datasets 1–3, respectively; and 2.46 vol %, 2.70 vol %, 7.09 vol %, and 5.70 vol % on Datasets 1 & 2, 2 & 3, 1 & 3, and 1 & 2 & 3, respectively.
Açıklama
Anahtar Kelimeler
Feature extraction, Freeman–Durden, GRNN, H/A/α, Machine learning, Polarimetric decomposition, Radarsat-2, Remote sensing, Soil moisture
Kaynak
Remote Sensing
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
9
Sayı
4
Künye
Özerdem, M. S., Acar, E. ve Ekinci, R. (2017). Soil moisture estimation over vegetated agricultural areas: Tigris Basin, Turkey from Radarsat-2 data by polarimetric decomposition models and a generalized regression neural network. Remote Sensing, 9(4), 1-21.
