The error structure of the SMAP single and dual channel soil moisture retrievals
Knowledge of the temporal error structure for remotely sensed surface soil moisture retrievals can improve our ability to exploit them for hydrologic and climate studies. This study employs a triple collocation analysis to investigate both the total variance and temporal auto-correlation of errors i...
| Veröffentlicht in: | Geophysical research letters. - 1984. - 45(2018), 2 vom: 28. Jan., Seite 758-765 |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2018
|
| Zugriff auf das übergeordnete Werk: | Geophysical research letters |
| Schlagworte: | Journal Article |
| Zusammenfassung: | Knowledge of the temporal error structure for remotely sensed surface soil moisture retrievals can improve our ability to exploit them for hydrologic and climate studies. This study employs a triple collocation analysis to investigate both the total variance and temporal auto-correlation of errors in Soil Moisture Active and Passive (SMAP) products generated from two separate soil moisture retrieval algorithms, the vertically-polarized brightness temperature based Single Channel Algorithm (SCA-V, the current baseline SMAP algorithm) and the Dual Channel Algorithm (DCA). A key assumption made in SCA-V is that real-time vegetation opacity can be accurately captured using only a climatology for vegetation opacity. Results demonstrate that, while SCA-V generally outperforms DCA, SCA-V can produce larger total errors when this assumption is significantly violated by inter-annual variability in vegetation health and biomass. Furthermore, larger auto-correlated errors in SCA-V retrievals are found in areas with relatively large vegetation opacity deviations from climatological expectations. This implies that a significant portion of the auto-correlated error in SCA-V is attributable to the violation of its vegetation opacity climatology assumption and suggests that utilizing a real (as opposed to climatological) vegetation opacity time series in the SCA-V algorithm would reduce the magnitude of auto-correlated soil moisture retrieval errors |
|---|---|
| Beschreibung: | Date Revised 29.03.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0094-8276 |
| DOI: | 10.1002/2017GL075656 |