Soil-Moisture Estimation From X-Band Data Using Tikhonov Regularization and Neural Net

Kseneman, Matej; Gleich, Dušan

doi:10.1109/tgrs.2012.2228486

Cited by 12 publications

(5 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where σ • pp (in power) is the observed canopy backscattering coefficient, which is represented as the sum of vegetation volume scattering σ • veg and the bare soil scattering σ • soil , τ 2 is the two-way transmissivity of the vegetation, A and B are the model coefficients, and V 1 and V 2 are the vegetation descriptors. According to Bai and He [42], the WCM can be further simplified by expanding τ 2 through the Maclaurin series [64]:…”

Section: A Simplified Water-cloud Model (Wcm)mentioning

confidence: 99%

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

Wang

2018

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

Section: A Simplified Water-cloud Model (Wcm)mentioning

confidence: 99%

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

Wang

2018

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Estimating soil moisture using remote sensing is mainly limited to surface soil moisture ( Akbar et al, 2017 ; Kseneman & Gleich, 2013 ; Peng & Loew, 2017 ; Moradizadeh & Saradjian, 2018 ; Chew, Small & Larson, 2016 ). Prior studies of soil moisture estimation using the daily diagnostic equation were based on moisture loss using the day of the year as an approximation ( Pan & Nieswiadomy, 2016 ; Pan, Nieswiadomy & Qian, 2015 ; Pan, 2012 ).…”

Section: Discussionmentioning

confidence: 99%

Using solar radiation data in soil moisture diagnostic equation for estimating root-zone soil moisture

Omotere

Pan

Wang

2022

PeerJ

View full text Add to dashboard Cite

The soil moisture daily diagnostic equation (SMDE) evaluates the relationship between the loss function coefficient and the summation of the weighted average of precipitation. The loss function coefficient uses the day of the year (DOY) to approximate the seasonal changes in soil moisture loss for a given location. Solar radiation is the source of the energy that drives the complex and intricates of the earth-atmospheric processes and biogeochemical cycles in the environment. Previous research assumed DOY is the approximation of other environmental factors (e.g., temperature, wind speed, solar radiation). In this article, two solar radiation parameters were introduced, i.e., the actual solar radiation and the clear sky solar radiation and were incorporated into the loss function coefficient to improve its estimation. This was applied to 2 years of continuous rainfall, soil moisture data from USDA soil climate network (SCAN) sites AL2053, GA2027 MS2025, and TN2076. It was observed that the correlation coefficient between the observed soil moisture and B values (which is the cumulated average of rainfall to soil moisture loss) increased on average by 2.3% and the root mean square errors (RMSEs) for estimating volumetric soil moisture at columns 0–5, 0–10, 0–20, 0–50, 0–100 cm reduced on average by 8.6% for all the study sites. The study has confirmed that using actual solar radiation data in the soil moisture daily diagnostic equation can improve its accuracy.

show abstract

“…The expression for τ 2 (and eventually the WCM) is simplified by truncating the series and limiting it to it's first two terms. Truncation of the transmittivity term to simplify the WCM is a well accepted practice adopted by several investigations [49], [31], [42], [50], [51]. Successive inclusion of the higher order terms in the Maclaurin series essentially increases the number of model constants, which makes the process of estimations increasingly challenging and require more scenes in the time-series to participate in estimation and optimization of the model constants.…”

Section: Methodsmentioning

confidence: 99%

Soil Moisture Retrieval During Crop Growth Cycle Using Satellite SAR Time Series

Muhuri,

Goïta,

Magagi

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Satellite SAR-based soil moisture retrieval over agricultural fields, under crop overlain conditions, is a challenging exercise. This is so since the overlying crop volume interacts with both the incoming and the backscattered radar signal. Therefore, the soil moisture linked solely to the top layer (0 − 5 cm) of the soil cannot be reliably retrieved under such conditions without avoiding the obscuring effect of growing crop volume. In this investigation, we demonstrated a proof of concept for a timeseries approach to retrieve soil moisture during crop growth cycle. Contrary to the use of the single-scene approach, the novelty of the proposed approach lies in exploiting the satellite SAR time-series acquired during a cropping cycle. The proposed timeseries approach is effective for capturing the nuances in the crop phenological stages while calibrating the Dubois-WCM soil moisture retrieval model. By employing this approach, we achieved the 0.04 m 3 m −3 soil moisture retrieval RMSE benchmark at a high spatial resolution and addressed the issue of solving for the Dubois-WCM model constants under data constrained conditions. Furthermore, we observed that combination of temporally nonoverlapping vegetation descriptors (optical and SAR) resulted in degradation in the performance of the retrievals and under such circumstances single polarimetric descriptors performed better.

show abstract

Soil-Moisture Estimation From X-Band Data Using Tikhonov Regularization and Neural Net

Cited by 12 publications

References 33 publications

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

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

Using solar radiation data in soil moisture diagnostic equation for estimating root-zone soil moisture

Soil Moisture Retrieval During Crop Growth Cycle Using Satellite SAR Time Series

Contact Info

Product

Resources

About