Hydrological Evaluation of Satellite Soil Moisture Data in Two Basins of Different Climate and Vegetation Density Conditions

Abstract

Accurate soil moisture information is very important for real-time flood forecasting. Although satellite soil moisture observations are useful information, their validations are generally hindered by the large spatial difference with the point-based measurements, and hence they cannot be directly applied in hydrological modelling. This study adopts a widely applied operational hydrological model Xinanjiang (XAJ) as a hydrological validation tool. Two widely used microwave sensors (SMOS and AMSR-E) are evaluated, over two basins (French Broad and Pontiac) with different climate types and vegetation covers. The results demonstrate SMOS outperforms AMSR-E in the Pontiac basin (cropland), while both products perform poorly in the French Broad basin (forest). The MODIS NDVI thresholds of 0.81 and 0.64 (for cropland and forest basins, resp.) are very effective in dividing soil moisture datasets into “denser” and “thinner” vegetation periods. As a result, in the cropland, the statistical performance is further improved for both satellites (i.e., improved to NSE = 0.74, RMSE = 0.0059 m and NSE = 0.58, RMSE = 0.0066 m for SMOS and AMER-E, resp.). The overall assessment suggests that SMOS is of reasonable quality in estimating basin-scale soil moisture at moderate-vegetated areas, and NDVI is a useful indicator for further improving the performance.

Soil moisture index estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

“…This condition can get even worse due to the soil conditions of the affected area [5][6][7]. It is important to identify the soil moisture of the area before considering rainfall as the factor that causing the landslide event, especially on deep-seated landslides and terrains with complex hydrological [8,9]. Commonly, soil moisture measurement can be estimated by using three methods: in situ measurements, hydrological model, and remote sensing.…”

Evaluation of Remotely Sensed Soil Moisture for Landslide Hazard Assessment

“…Unfortunately, in many remote areas, soil moisture stations are not available or only are sparsely distributed in those non-hazardous areas, due to high installation and maintenance cost (e.g., in our study area, although there is a total of 19 in-situ soil moisture sensors installed, nearly all of them are installed in the plain areas where landslides never occurred). Another technique to obtain continuous soil moisture variations relies on land surface /hydrological modelling [7,[24][25][26][27]]. However, model-based methods tend to suffer from time drifts problem (e.g., error accumulation over times), require a large number of accurate data inputs and are normally computationally intensive particularly for large monitoring areas.…”

Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA

“…In the past, quite a few active and passive missions retrieved soil moisture, which mainly includes Soil Moisture and Ocean Salinity (SMOS) [8], Soil Moisture Active Passive (SMAP) [9], Advanced Scatterometer (ASCAT) [10], European Remote Sensing (ERS) [11], Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) [12,13]. Global Change Observation Mission First-Water (GCOM-W1) launched on 17 May 2012 carries AMSR2 and is an improved design of AMSR-E NASA's Aqua Satellite with enhancement in the reflector, C-band frequency and improved calibration system.…”