scholarly journals Study of soil moisture change effects on L-band DInSAR phase

2021 ◽  
Vol 8 (4) ◽  
pp. 81-101
Author(s):  
Sadegh Ranjbar ◽  
Mehdi Akhoondzadeh ◽  
◽  
Keyword(s):  
Soil Moisture ◽  
Moisture Change ◽  
L Band

Estimation of soil moisture change with PALS's L-band radiometer

2003 ◽  
Author(s):  
Jiancheng Shi ◽  
E.G. Njoku ◽  
K.S. Chen ◽  
T. Jackson ◽  
P. O'neill
Keyword(s):  
Soil Moisture ◽  
Moisture Change ◽  
L Band ◽  
Band Radiometer

scholarly journals Field-Scale Soil Moisture Retrieval Using PALSAR-2 Polarimetric Decomposition and Machine Learning

Agronomy ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 35
Author(s):  
Xiaodong Huang ◽  
Beth Ziniti ◽  
Michael H. Cosh ◽  
Michele Reba ◽  
Jinfei Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Canopy Cover ◽  
The United States ◽  
Optical Data ◽  
Study Region ◽  
Model Based ◽  
Key Indicator ◽  
Ground Measurement ◽  
L Band

Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the surface soil moisture under the vegetation cover due to its strong penetration capability. This paper studies the soil moisture retrieval using the L-band polarimetric Phased Array-type L-band SAR 2 (PALSAR-2) data acquired over the study region in Arkansas in the United States. Both two-component model-based decomposition (SAR data alone) and machine learning (SAR + optical indices) methods are tested and compared in this paper. Validation using independent ground measurement shows that the both methods achieved a Root Mean Square Error (RMSE) of less than 10 (vol.%), while the machine learning methods outperform the model-based decomposition, achieving an RMSE of 7.70 (vol.%) and R2 of 0.60.

scholarly journals Soil moisture and vegetation optical depth retrievals over heterogeneous scenes using LEWIS L-band radiometer

2021 ◽  
Vol 102 ◽  
pp. 102424
Author(s):  
M. Barrée ◽  
A. Mialon ◽  
T. Pellarin ◽  
M. Parrens ◽  
R. Biron ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Optical Depth ◽  
L Band ◽  
Band Radiometer

scholarly journals Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques

2021 ◽  
Vol 261 ◽  
pp. 112485
Author(s):  
Hongtao Shi ◽  
Lingli Zhao ◽  
Jie Yang ◽  
Juan M. Lopez-Sanchez ◽  
Jinqi Zhao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Agricultural Fields ◽  
Decomposition Techniques ◽  
L Band

scholarly journals Analysis of a Least-Squares Soil Moisture Retrieval Algorithm from L-band Passive Observations

2010 ◽  
Vol 2 (1) ◽  
pp. 352-374 ◽  
Author(s):  
María Piles ◽  
Mercè Vall-llossera ◽  
Adriano Camps ◽  
Marco Talone ◽  
Alessandra Monerris
Keyword(s):  
Soil Moisture ◽  
Least Squares ◽  
Retrieval Algorithm ◽  
L Band

Combining Sentinel-1 and ALOS-2 observations for soil moisture retrieval

2021 ◽  
Author(s):  
Anna Balenzano ◽  
Giuseppe Satalino ◽  
Francesco Lovergine ◽  
Davide Palmisano ◽  
Francesco Mattia ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Temporal Resolution ◽  
Surface Soil ◽  
Earth Science ◽  
Observation System ◽  
Final Report ◽  
Surface Soil Moisture ◽  
Error Characterization ◽  
L Band

<p>One of the limitations of presently available Synthetic Aperture Radar (SAR) surface soil moisture (SSM) products is their moderated temporal resolution (e.g., 3-4 days) that is non optimal for several applications, as most user requirements point to a temporal resolution of 1-2 days or less. A possible path to tackle this issue is to coordinate multi-mission SAR acquisitions with a view to the future Copernicus Sentinel-1 (C&D and Next Generation) and L-band Radar Observation System for Europe (ROSE-L).</p><p>In this respect, the recent agreement between the Japanese (JAXA) and European (ESA) Space Agencies on the use of SAR Satellites in Earth Science and Applications provides a framework to develop and validate multi-frequency and multi-platform SAR SSM products. In 2019 and 2020, to support insights on the interoperability between C- and L-band SAR observations for SSM retrieval, Sentinel-1 and ALOS-2 systematic acquisitions over the TERENO (Terrestrial Environmental Observatories) Selhausen (Germany) and Apulian Tavoliere (Italy) cal/val sites were gathered. Both sites are well documented and equipped with hydrologic networks.</p><p>The objective of this study is to investigate the integration of multi-frequency SAR measurements for a consistent and harmonized SSM retrieval throughout the error characterization of a combined C- and L-band SSM product. To this scope, time series of Sentinel-1 IW and ALOS-2 FBD data acquired over the two sites will be analysed. The short time change detection (STCD) algorithm, developed, implemented and recently assessed on Sentinel-1 data [e.g., Balenzano et al., 2020; Mattia et al., 2020], will be tailored to the ALOS-2 data. Then, the time series of SAR SSM maps from each SAR system will be derived separately and aggregated in an interleaved SSM product. Furthermore, it will be compared against in situ SSM data systematically acquired by the ground stations deployed at both sites. The study will assess the interleaved SSM product and evaluate the homogeneous quality of C- and L-band SAR SSM maps.</p><p> </p><p> </p><p>References</p><p>Balenzano. A., et al., “Sentinel-1 soil moisture at 1km resolution: a validation study”, submitted to Remote Sensing of Environment (2020).</p><p>Mattia, F., A. Balenzano, G. Satalino, F. Lovergine, A. Loew, et al., “ESA SEOM Land project on Exploitation of Sentinel-1 for Surface Soil Moisture Retrieval at High Resolution,” final report, contract number 4000118762/16/I-NB, 2020.</p>

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