Name
Title | ||
|---|---|---|
Scatterometer-Derived Soil Moisture Calibrated for Soil Texture With a One-Dimensional Water-Flow Model |
Abstract | ||
|---|---|---|
Current global satellite scatterometer-based soil moisture retrieval algorithms do not take soil characteristics into account. In this paper, the characteristic time length of the soil water index has been calibrated for ten sampling frequencies and for different soil conductivity associated with 12 soil texture classes. The calibration experiment was independently performed from satellite observations. The reference soil moisture data set was created with a 1-D water-flow model and by making use of precipitation measurements. The soil water index was simulated by applying the algorithm to the modeled soil moisture of the upper few centimeters. The resulting optimized characteristic time lengths T increase with longer sampling periods. For instance, a T of 7 days was found for sandy soil when a sampling period of 1 day was applied, whereas an optimized T-value of 18 days was found for a sampling period of 10 days. A maximum rmse improvement of 0.5% vol. can be expected when using the calibrated T-values instead of T = 20. The soil water index and the differentiated T-values were applied to European Remote Sensing (ERS) satellite scatterometer data and were validated against in situ soil moisture measurements. The results obtained using calibrated T -values and T = 20 did not differ ( r = 0.39, rmse = 5.4% vol.) and can be explained by the averaged sampling period of 4-5 days. The soil water index obtained with current operational microwave sensors [Advanced Wind Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer-Earth Observation System] and future sensors (Soil Moisture and Ocean Salinity and Soil Moisture Active Passive) should benefit from soil texture differentiation, as they can record on a daily basis either individually or synergistically using several sensors. The proposed differentiated characteristic time length enables the continuation of the soil water index of sen- - sors with varying sampling periods (e.g., ERS-ASCAT). |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TGRS.2008.2000796 | Geoscience and Remote Sensing, IEEE Transactions |
Keywords | Field | DocType |
microwave propagation,moisture,remote sensing by radar,soil,texture,3D water-flow model,ASCAT,Advanced Microwave Scanning Radiometer,Advanced Wind Scatterometer,ERS,Earth Observation System,European Remote Sensing,Soil Moisture Active Passive,Soil Moisture and Ocean Salinity,global satellite scatterometer measurement,microwave sensors,precipitation measurement,soil moisture calibration,soil moisture retrieval algorithm,soil texture,soil water index,Hydrology,microwave,scatterometer,soil moisture,soil texture | Moisture,Water flow,Remote sensing,Scatterometer,Sampling (statistics),Water content,Soil texture,Mathematics,Precipitation,Soil water | Journal |
Volume | Issue | ISSN |
46 | 12 | 0196-2892 |
Citations | PageRank | References |
5 | 0.89 | 3 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Remko de Lange | 1 | 14 | 1.69 |
| Rob Beck | 2 | 5 | 0.89 |
| Nick van de Giesen | 3 | 55 | 9.38 |
| Jan Friesen | 4 | 23 | 2.94 |
| Allard de Wit | 5 | 11 | 2.72 |
| Wolfgang Wagner | 6 | 94 | 17.96 |
| de Lange, R. | 7 | 5 | 0.89 |
| van de Giesen, N. | 8 | 5 | 0.89 |