A model for downscaling SMOS soil moisture using Sentinel-1 SAR data

Li, Junhua; Wang, Shusen; Gunn, Grant E.; Joosse, Pamela; Russell, H A J

doi:10.1016/j.jag.2018.07.012

Cited by 27 publications

(27 citation statements)

References 46 publications

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“…Nevertheless, it is necessary to outline the limitations of the RF models in this study. First, the depths of the soil layer contributing to the L-band radiometer signal (~5 cm) and the C-band SAR backscatter signal (∼2 cm) are different [14,20,36]. The penetration depths of microwaves mainly depend on microwave frequency, soil texture, and SM content [83].…”

Section: Rf-based Downscaling Methodsmentioning

confidence: 99%

“…The complex surface scattering conditions caused by vegetation and surface roughness can bring more uncertainty for the L2_SM_SP products, especially at a 1 km spatial resolution. Rainfall events, cultivation activities, and the inconsistent observation time between SMAP and Sentinel-1 satellites can further introduce additional outliers for the L2_SM_SP data [20,86]. The RF5 model is less affected by noise related to SAR data through a combination with other land surface features (e.g., VIs and LST), and is capable of generating 3 km and 1 km SM data with satisfactory performance.…”

Section: Analysis Of the Differences Between Rf Models And L2_sm_sp Pmentioning

confidence: 99%

“…2 of 20 salinity (SMOS) satellites [10] based on L-band passive microwaves can provide high-accuracy global daily SM products [16]. However, coarse-resolution passive microwave SM data cannot reflect the detailed distribution of surface SM; therefore, many researchers have downscaled coarse-resolution passive microwave SM data based on fine-resolution auxiliary data [17][18][19][20][21][22][23].Some downscaling methods are based on active or passive microwave data including downscaling coarse-resolution microwave brightness temperature (TB) data or coarse-resolution SM data based on microwave backscatter data [22,[24][25][26][27][28][29][30], and downscaling low-frequency passive microwave data based on high-frequency passive microwave data [31][32][33]. Active microwave techniques offer higher spatial resolutions than passive microwave techniques.…”

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confidence: 99%

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An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

et al. 2019

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A method is proposed for the production of downscaled soil moisture active passive (SMAP) soil moisture (SM) data by combining optical/infrared data with synthetic aperture radar (SAR) data based on the random forest (RF) model. The method leverages the sensitivity of active microwaves to surface SM and the triangle/trapezium feature space among vegetation indexes (VIs), land surface temperature (LST), and SM. First, five RF architectures (RF1-RF5) were trained and tested at 9 km. Second, a comparison was performed for RF1-RF5, and were evaluated against in situ SM measurements. Third, two SMAP-Sentinel active-passive SM products were compared at 3 km and 1 km using in situ SM measurements. Fourth, the RF5 model simulations were compared with the SMAP L2_SM_SP product based on the optional algorithm at 3 km and 1 km resolutions. The results showed that the downscaled SM based on the synergistic use of optical/infrared data and the backscatter at vertical-vertical (VV) polarization was feasible in semi-arid areas with relatively low vegetation cover. The RF5 model with backscatter and more parameters from optical/infrared data performed best among the five RF models and was satisfactory at both 3 km and 1 km. Compared with L2_SM_SP, RF5 was more superior at 1 km. The input variables in decreasing order of importance were backscatter, LST, VIs, and topographic factors over the entire study area. The low vegetation cover conditions probably amplified the importance of the backscatter and LST. A sufficient number of VIs can enhance the adaptability of RF models to different vegetation conditions. 2 of 20 salinity (SMOS) satellites [10] based on L-band passive microwaves can provide high-accuracy global daily SM products [16]. However, coarse-resolution passive microwave SM data cannot reflect the detailed distribution of surface SM; therefore, many researchers have downscaled coarse-resolution passive microwave SM data based on fine-resolution auxiliary data [17][18][19][20][21][22][23].Some downscaling methods are based on active or passive microwave data including downscaling coarse-resolution microwave brightness temperature (TB) data or coarse-resolution SM data based on microwave backscatter data [22,[24][25][26][27][28][29][30], and downscaling low-frequency passive microwave data based on high-frequency passive microwave data [31][32][33]. Active microwave techniques offer higher spatial resolutions than passive microwave techniques. Downscaling methods based on active microwaves usually leverage the linear or near-linear relationship between microwave TB or SM data and microwave backscatter data in a time series. Vegetation and surface roughness are generally considered time-invariant during long-time series [26]. However, active microwaves are greatly affected by vegetation and surface roughness [34]; therefore, their applications in most areas are limited [35]. Another assumption is that invariance of vegetation and surface roughness occurs in adjacent observations (several days); thus, these techniques req...

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Section: Rf-based Downscaling Methodsmentioning

confidence: 99%

Section: Analysis Of the Differences Between Rf Models And L2_sm_sp Pmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

et al. 2019

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“…Radarsat Constellation Mission (RCM) with 4-days revisit interval will further expand current C-band SAR satellites' revisit capabilities (6-days for Sentinel-1A/B and 24-days for Radarsat-2). These capacities are promoting new scientific and operational perspectives, e.g., downscaling passive microwave soil moisture [34,35], in the retrieval of soil moisture continuously over large areas using C-band SAR imagery.…”

Section: Of 17mentioning

confidence: 99%

Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval

Wang

2018

Remote Sensing

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The water cloud model (WCM) is a widely used radar backscatter model applied to SAR images to retrieve soil moisture over vegetated areas. The WCM needs vegetation descriptors to account for the impact of vegetation on SAR backscatter. The commonly used vegetation descriptors in WCM, such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI), are sometimes difficult to obtain due to the constraints in data availability in in-situ measurements or weather dependency in optical remote sensing. To improve soil moisture retrieval, this study investigates the feasibility of using all-weather SAR derived vegetation descriptors in WCM. The in-situ data observed at an agricultural crop region south of Winnipeg in Canada, RapidEye optical images and dual-polarized Radarsat-2 SAR images acquired in growing season were used for WCM model calibration and test. Vegetation descriptors studied include HV polarization backscattering coefficient ( σ H V ° ) and Radar Vegetation Index (RVI) derived from SAR imagery, and NDVI derived from optical imagery. The results show that σ H V ° achieved similar results as NDVI but slightly better than RVI, with a root mean square error of 0.069 m3/m3 and a correlation coefficient of 0.59 between the retrieved and observed soil moisture. The use of σ H V ° can overcome the constraints of the commonly used vegetation descriptors and reduce additional data requirements (e.g., NDVI from optical sensors) in WCM, thus improving soil moisture retrieval and making WCM feasible for operational use.

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The use of remote sensing indices to understand flood‐recharged soil moisture impacts on trees in semi‐arid floodplains: A review

et al. 2022

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Remote sensing indices (RSIs) are essential for detection, mapping and monitoring of floods, soil moisture and tree characteristics. An understanding of the nexus between flood-recharged soil moisture (FRSM) and endangered biodiversity tree hotspots in semi-arid floodplains is critical. Specifically, the influence of FRSM on biophysical characteristics of keystone tree species with social, economic, ecological and environmental functions is crucial. However, the use of RSIs to understand the link between episodic floods, FRSM and trees, as well as their respective spatiotemporal dynamics within the semi-arid floodplains, remains largely unexplored. Hence, this study reviewed literature on the adoption of RSIs in understanding short-term phenological and long-term tree structural changes arising from flood-related infiltration. The reviewed literature shows the predominance of the normalised difference water index, the land surface water index and normalised difference vegetation index for flood, FRSM and tree mapping. The review also shows the emergence of SAR-based normalised difference flood index for understanding the impacts of FRSM on riparian trees. Although the review identified an increase in data availability, however, there are high costs of active commercial remote sensing data of superior quality and computing resources. Furthermore, available free data are incapable of penetrating canopies and have coarse spatial resolution constraining the use of RSIs to understand the impacts of FRSM on trees. In view of increasing free SAR data and cloudbased computing resources, application of emerging deep learning algorithms offers prospects for the use of RSIs to understand the impacts of FRSM on riparian trees.

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A model for downscaling SMOS soil moisture using Sentinel-1 SAR data

Cited by 27 publications

References 46 publications

An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval

The use of remote sensing indices to understand flood‐recharged soil moisture impacts on trees in semi‐arid floodplains: A review

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