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Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data
- Source :
- Remote Sensing, Vol 13, Iss 2102, p 2102 (2021), Remote Sensing, Remote Sensing, MDPI, 2021, 13 (11), ⟨10.3390/rs13112102⟩, Remote Sensing, 2021, 13 (11), ⟨10.3390/rs13112102⟩, Remote Sensing; Volume 13; Issue 11; Pages: 2102
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- International audience; Surface soil moisture (SSM) is a key variable for many environmental studies, including hydrology and agriculture. Synthetic aperture radar (SAR) data in the C-band are widely used nowadays to estimate SSM since the Sentinel-1 provides free-of-charge C-band SAR images at high spatial resolution with high revisit time, whereas the use of L-band is limited due to the low data availability. In this context, the main objective of this paper is to develop an operational approach for SSM estimation that mainly uses data in the C-band (Sentinel-1) with L-bands (ALOS/PALSAR) as additional data to improve SSM estimation accuracy. The approach is based on the use of the artificial neural networks (NNs) technique to firstly derive the soil roughness (Hrms) from the L-band (HH polarization) to then consider the L-band-derived Hrms and C-band SAR data (VV and VH polarizations) in the input vectors of NNs for SSM estimation. Thus, the Hrms estimated from the L-band at a given date is assumed to be constant for a given times series of C-band images. The NNs were trained and validated using synthetic and real databases. The results showed that the use of the L-band-derived Hrms in the input vector of NN in addition to C-band SAR data improved SSM estimation by decreasing the error (bias and RMSE), mainly for SSM values lower than 15 vol.% and regardless of Hrms values. Based on the synthetic database, the NNs that neglect the Hrms underestimate and overestimate the SSM (bias ranges between −8.0 and 4.0 vol.%) for Hrms values lower and higher than 1.5 cm, respectively. For Hrms 2.0 cm and SSM between 8 to 22 vol.%, the accuracy on the SSM estimation improved and the overestimation decreased by 2.2 vol.% (from 4.5 to 2.3 vol.%). From the real database, the use of Hrms estimated from the L-band brought a significant improvement of the SSM estimation accuracy. For in situ SSM less than 15 vol.%, the RMSE decreased by 1.5 and 2.2 vol.% and the bias by 1.2 and 2.6 vol.%, for Hrms values lower and higher than 1.5 cm, respectively.
- Subjects :
- Synthetic aperture radar
L band
010504 meteorology & atmospheric sciences
Mean squared error
C band
Science
0211 other engineering and technologies
Context (language use)
02 engineering and technology
01 natural sciences
Surface roughness
C-band
Water content
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
Mathematics
soil moisture
surface roughness
SAR
L-band
ALOS/PALSAR
Sentinel-1
artificial neural networks
[SDE]Environmental Sciences
General Earth and Planetary Sciences
Soil roughness
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 13
- Issue :
- 2102
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....72e2531af1e9ae2c0123df8c2b833cb7