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https://hdl.handle.net/11681/29520Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lewis, Michael G. | - |
| dc.contributor.author | Fisher, Andmorgan R. | - |
| dc.contributor.author | Smith, Clint B. | - |
| dc.date.accessioned | 2018-10-01T15:46:08Z | - |
| dc.date.available | 2018-10-01T15:46:08Z | - |
| dc.date.issued | 2018-09 | - |
| dc.identifier.govdoc | ERDC/GRL TR-18-1 | - |
| dc.identifier.uri | http://hdl.handle.net/11681/29520 | - |
| dc.identifier.uri | http://dx.doi.org/10.21079/11681/29520 | - |
| dc.description | Technical Report | - |
| dc.description.abstract | This paper reviews an approach to increase soil moisture resolution over a sample region over Australia using the Soil Moisture Active Passive (SMAP) sensor and Landsat 8 only. This approach uses an inductive localized approach, eschewing the need to obtain a resilient and definitive model in favor of a temporary model that utilizes current conditional inputs only. For the purposes of this analysis, the SMAP 36 km soil moisture product is considered fully valid and accurate. Landsat bands coinciding in collection date with a SMAP capture are down sampled to match the resolution of the SMAP product. A series of indices describing the Soil-Vegetation-Atmosphere Triangle (SVAT) relationship are then produced using the down sampled Landsat bands. These indices are then related to the local coincident SMAP values to identify a series of rules or trees to identify the local rules defining the relationship between soil moisture and the aforementioned indices. This paper uses a random forest due to its highly accurate learning against local ground truth data. The defined rules are then applied to the Landsat image in the native Landsat resolution to determine local soil moisture. Ground truth comparison is done via a series of grids using time dielectric impedance and airborne L-band Multi-beam Radiometer (PLMR) observations done under the SMAPEx-5 campaign (Panciera 2013). The predictive power of the inferred learning soil moisture algorithm (ILSMA) did well with a mean absolute error of 0.054 over an airborne L-band retrieved surface over the same region. | en_US |
| dc.description.sponsorship | Army Terrestrial Environmental Modeling and Intelligence System Research Program (U.S.) | en_US |
| dc.description.tableofcontents | Abstract .................................................................................................................................... ii Figures and Tables .................................................................................................................. iv Preface ..................................................................................................................................... vi Unit Conversion Factors ........................................................................................................ vii 1 Introduction ...................................................................................................................... 1 1.1 Background ........................................................................................................ 1 1.2 History ................................................................................................................ 1 1.3 Purpose .............................................................................................................. 2 2 Data ................................................................................................................................... 3 2.1 Optical ................................................................................................................ 3 2.2 Microwave .......................................................................................................... 5 2.3 Integration between microwave and VIS/NIR .................................................. 6 3 Methods ............................................................................................................................ 8 4 Results ............................................................................................................................. 18 5 Discussion ....................................................................................................................... 28 References ............................................................................................................................. 30 Report Documentation Page | - |
| dc.format.extent | 42 pages / 5.37 Mb | - |
| dc.format.medium | PDF/A | - |
| dc.language.iso | en_US | en_US |
| dc.publisher | Geospatial Research Laboratory (U.S.) | en_US |
| dc.publisher | Engineer Research and Development Center (U.S.) | en_US |
| dc.relation.ispartofseries | Technical Report (Engineer Research and Development Center (U.S.)) ; no. ERDC/GRL TR-18-1 | - |
| dc.rights | Approved for Public Release; Distribution is Unlimited | - |
| dc.source | This Digital Resource was created in Microsoft Word and Adobe Acrobat | - |
| dc.subject | Soil moisture | en_US |
| dc.subject | Environmental monitoring--Remote sensing | en_US |
| dc.subject | Microwave remote sensing | en_US |
| dc.title | Determining soil moisture at moderately high resolution via Soil Moisture Active Passive (SMAP) and Landsat using inferred learning | en_US |
| dc.type | Report | en_US |
| Appears in Collections: | Technical Report | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ERDC-GRL TR-18-1.pdf | 5.5 MB | Adobe PDF |  View/Open |