SMSYNTH: AN IMAGERY SYNTHESIS SYSTEM FOR SOIL MOISTURE RETRIEVAL
Soil moisture (SM) is a important variable in various research areas, such as weather and climate forecasting, agriculture, drought and flood monitoring and prediction, and human health. An ongoing challenge in estimating SM via synthetic aperture radar (SAR) is the development of the retrieval SM m...
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doaj-e2af4f41084e4b6f9de5bbdad92d5f7b2020-11-24T22:15:52ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342018-04-01XLII-312713110.5194/isprs-archives-XLII-3-127-2018SMSYNTH: AN IMAGERY SYNTHESIS SYSTEM FOR SOIL MOISTURE RETRIEVALY. Cao0L. Xu1J. Peng2Dept. of Land science and technology, China University of Geosciences, Xueyuan Road, Beijing, ChinaDept. of Land science and technology, China University of Geosciences, Xueyuan Road, Beijing, ChinaDept. of Land science and technology, China University of Geosciences, Xueyuan Road, Beijing, ChinaSoil moisture (SM) is a important variable in various research areas, such as weather and climate forecasting, agriculture, drought and flood monitoring and prediction, and human health. An ongoing challenge in estimating SM via synthetic aperture radar (SAR) is the development of the retrieval SM methods, especially the empirical models needs as training samples a lot of measurements of SM and soil roughness parameters which are very difficult to acquire. As such, it is difficult to develop empirical models using realistic SAR imagery and it is necessary to develop methods to synthesis SAR imagery. To tackle this issue, a SAR imagery synthesis system based on the SM named SMSynth is presented, which can simulate radar signals that are realistic as far as possible to the real SAR imagery. In SMSynth, SAR backscatter coefficients for each soil type are simulated via the Oh model under the Bayesian framework, where the spatial correlation is modeled by the Markov random field (MRF) model. The backscattering coefficients simulated based on the designed soil parameters and sensor parameters are added into the Bayesian framework through the data likelihood where the soil parameters and sensor parameters are set as realistic as possible to the circumstances on the ground and in the validity range of the Oh model. In this way, a complete and coherent Bayesian probabilistic framework is established. Experimental results show that SMSynth is capable of generating realistic SAR images that suit the needs of a large amount of training samples of empirical models.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/127/2018/isprs-archives-XLII-3-127-2018.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Y. Cao L. Xu J. Peng |
spellingShingle |
Y. Cao L. Xu J. Peng SMSYNTH: AN IMAGERY SYNTHESIS SYSTEM FOR SOIL MOISTURE RETRIEVAL The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
Y. Cao L. Xu J. Peng |
author_sort |
Y. Cao |
title |
SMSYNTH: AN IMAGERY SYNTHESIS SYSTEM FOR SOIL MOISTURE RETRIEVAL |
title_short |
SMSYNTH: AN IMAGERY SYNTHESIS SYSTEM FOR SOIL MOISTURE RETRIEVAL |
title_full |
SMSYNTH: AN IMAGERY SYNTHESIS SYSTEM FOR SOIL MOISTURE RETRIEVAL |
title_fullStr |
SMSYNTH: AN IMAGERY SYNTHESIS SYSTEM FOR SOIL MOISTURE RETRIEVAL |
title_full_unstemmed |
SMSYNTH: AN IMAGERY SYNTHESIS SYSTEM FOR SOIL MOISTURE RETRIEVAL |
title_sort |
smsynth: an imagery synthesis system for soil moisture retrieval |
publisher |
Copernicus Publications |
series |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
1682-1750 2194-9034 |
publishDate |
2018-04-01 |
description |
Soil moisture (SM) is a important variable in various research areas, such as weather and climate forecasting, agriculture, drought and flood monitoring and prediction, and human health. An ongoing challenge in estimating SM via synthetic aperture radar (SAR) is the development of the retrieval SM methods, especially the empirical models needs as training samples a lot of measurements of SM and soil roughness parameters which are very difficult to acquire. As such, it is difficult to develop empirical models using realistic SAR imagery and it is necessary to develop methods to synthesis SAR imagery. To tackle this issue, a SAR imagery synthesis system based on the SM named SMSynth is presented, which can simulate radar signals that are realistic as far as possible to the real SAR imagery. In SMSynth, SAR backscatter coefficients for each soil type are simulated via the Oh model under the Bayesian framework, where the spatial correlation is modeled by the Markov random field (MRF) model. The backscattering coefficients simulated based on the designed soil parameters and sensor parameters are added into the Bayesian framework through the data likelihood where the soil parameters and sensor parameters are set as realistic as possible to the circumstances on the ground and in the validity range of the Oh model. In this way, a complete and coherent Bayesian probabilistic framework is established. Experimental results show that SMSynth is capable of generating realistic SAR images that suit the needs of a large amount of training samples of empirical models. |
url |
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/127/2018/isprs-archives-XLII-3-127-2018.pdf |
work_keys_str_mv |
AT ycao smsynthanimagerysynthesissystemforsoilmoistureretrieval AT lxu smsynthanimagerysynthesissystemforsoilmoistureretrieval AT jpeng smsynthanimagerysynthesissystemforsoilmoistureretrieval |
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