Retrieval and Multi-scale Validation of Soil Moisture from Multi-temporal SAR Data in a Semi-Arid Tropical Region

Utilization of optical satellite data in tropical region was limited to free cloud cover. Therefore, Synthetic Aperture Radar (SAR) becomes an alternative solution for forest mapping in Indonesia due to its capability to penetrate cloud. The objective of this research was to develop a forestmapping model based on multi temporal SAR data. Multi temporal ALOS PALSAR data for 2007 and 2008 were used for forest mapping, and one year mosaic LANDSAT data in 2008 was used as references data to obtain training sample and to verify the final forest classification. PALSAR processing was done using gamma naught conversion and Lee filtering. Samples were made in forest and water area, and the statistical values of the each object were calculated. Some thresholds were determined based on the average and standard deviation, and the best threshold was selected to classify forest and water in 2008. It was assumed that forest could not change in 1-2 years period. The classification of forest, water, and the change were combined to produce final forest in 2008, and then it was visually verified with mosaic LANDSAT in 2008. The result showed that forest, water, and the change could be well classified using threshold method. The forest derived from PALSAR was visually consistent with forest appearance in LANDSAT and forest produced from INCAS. It has better performance than forest derived from INCAS for separating oil palm plantation from the forest.

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Multi-Scale Validation of SMAP Soil Moisture Products over Cold and Arid Regions in Northwestern China Using Distributed Ground Observation Data

Remote Sensing ◽

10.3390/rs9040327 ◽

2017 ◽

Vol 9 (4) ◽

pp. 327 ◽

Cited By ~ 18

Author(s):

Chunfeng Ma ◽

Xin Li ◽

Long Wei ◽

Weizhen Wang

Keyword(s):

Soil Moisture ◽

Arid Regions ◽

Scale Validation ◽

Northwestern China ◽

Observation Data ◽

Multi Scale ◽

Ground Observation

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Soil moisture retrieval through a merging of multi-temporal L-band SAR data and hydrologic modelling

Hydrology and Earth System Sciences ◽

10.5194/hess-13-343-2009 ◽

2009 ◽

Vol 13 (3) ◽

pp. 343-356 ◽

Cited By ~ 39

Author(s):

F. Mattia ◽

G. Satalino ◽

V. R. N. Pauwels ◽

A. Loew

Keyword(s):

Soil Moisture ◽

Soil Moisture Content ◽

A Priori ◽

Retrieval Algorithm ◽

A Priori Information ◽

Hydrologic Modelling ◽

Multi Temporal ◽

L Band ◽

Sar Data ◽

Priori Information

Abstract. The objective of the study is to investigate the potential of retrieving superficial soil moisture content (mv) from multi-temporal L-band synthetic aperture radar (SAR) data and hydrologic modelling. The study focuses on assessing the performances of an L-band SAR retrieval algorithm intended for agricultural areas and for watershed spatial scales (e.g. from 100 to 10 000 km2). The algorithm transforms temporal series of L-band SAR data into soil moisture contents by using a constrained minimization technique integrating a priori information on soil parameters. The rationale of the approach consists of exploiting soil moisture predictions, obtained at coarse spatial resolution (e.g. 15–30 km2) by point scale hydrologic models (or by simplified estimators), as a priori information for the SAR retrieval algorithm that provides soil moisture maps at high spatial resolution (e.g. 0.01 km2). In the present form, the retrieval algorithm applies to cereal fields and has been assessed on simulated and experimental data. The latter were acquired by the airborne E-SAR system during the AgriSAR campaign carried out over the Demmin site (Northern Germany) in 2006. Results indicate that the retrieval algorithm always improves the a priori information on soil moisture content though the improvement may be marginal when the accuracy of prior mv estimates is better than 5%.

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