Downscaling SMAP Radiometer Soil Moisture Over the CONUS Using an Ensemble Learning Method

Abbaszadeh, Peyman; Moradkhani, Hamid; Zhan, Xiwu

doi:10.1029/2018wr023354

Cited by 152 publications

(113 citation statements)

References 103 publications

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“…GTOPO30 has a 1 km spatial resolution and it has been used in many studies for estimation of multiple topographical indices (Marlier et al 2015, Folk et al 2018, Abbaszadeh et al 2019b. In this study, we used GTOPO30 to derive several topographic factors including altitude, slope, flow accumulation, and TRI at different spatial resolutions (i.e.…”

Section: Gtopo30 Topography Datamentioning

confidence: 99%

Leveraging machine learning for predicting flash flood damage in the Southeast US

Alipour

Ahmadalipour

Abbaszadeh

et al. 2020

Environ. Res. Lett.

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Flash flood is a recurrent natural hazard with substantial impacts in the Southeast US (SEUS) due to the frequent torrential rainfalls that occur in the region, which are triggered by tropical storms, thunderstorms, and hurricanes. Flash floods are costly natural hazards, primarily due to their rapid onset. Therefore, predicting property damage of flash floods is imperative for proactive disaster management. Here, we present a systematic framework that considers a variety of features explaining different components of risk (i.e. hazard, vulnerability, and exposure), and examine multiple machine learning methods to predict flash flood damage. A large database of flash flood events consisting of more than 14 000 events are assessed for training and testing the methodology, while a multitude of data sources are utilized to acquire reliable information related to each event. A variable selection approach was employed to alleviate the complexity of the dataset and facilitate the model development process. The random forest (RF) method was then used to map the identified input covariates to a target variable (i.e. property damage). The RF model was implemented in two modes: first, as a binary classifier to estimate if a region of interest was damaged in any particular flood event, and then as a regression model to predict the amount of property damage associated with each event. The results indicate that the proposed approach is successful not only for classifying damaging events (with an accuracy of 81%), but also for predicting flash flood damage with a good agreement with the observed property damage. This study is among the few efforts for predicting flash flood damage across a large domain using mesoscale input variables, and the findings demonstrate the effectiveness of the proposed methodology.

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Section: Gtopo30 Topography Datamentioning

confidence: 99%

Leveraging machine learning for predicting flash flood damage in the Southeast US

Alipour

Ahmadalipour

Abbaszadeh

et al. 2020

Environ. Res. Lett.

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“…The correlation between precipitation and soil moisture spatial and temporal patterns has been observed by many studies [2,8]. Since precipitation datasets are of higher spatial resolution, it has been used in the process of downscaling coarse resolution soil moisture [32,39].…”

Section: Nws Precipitation Datamentioning

confidence: 99%

“…The potential solution might be to use this information as a way to split up the area of interest into segments and to build independent models for each segment separately. A similar approach has been done by soil types, and it proved to be useful [32]. In addition, the additional predictors should be checked and included in order to reduce RMSE in tropical/megathermal climates, but with the preservation of high R 2 value.…”

Section: Spatial Patterns Of the High-resolution Soil Moisture Mapsmentioning

confidence: 99%

New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

et al. 2020

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ESA CCI SM products have provided remotely-sensed surface soil moisture (SSM) content with the best spatial and temporal coverage thus far, although its output spatial resolution of 25 km is too coarse for many regional and local applications. The downscaling methodology presented in this paper improves ESA CCI SM spatial resolution to 1 km using two-step approach. The first step is used as a data engineering tool and its output is used as an input for the Random forest model in the second step. In addition to improvements in terms of spatial resolution, the approach also considers the problem of data gaps. The filling of these gaps is the initial step of the procedure, which in the end produces a continuous product in both temporal and spatial domains. The methodology uses combined active and passive ESA CCI SM products in addition to in situ soil moisture observations and the set of auxiliary downscaling predictors. The research tested several variants of Random forest models to determine the best combination of ESA CCI SM products. The conclusion is that synergic use of all ESA CCI SM products together with the auxiliary datasets in the downscaling procedure provides better results than using just one type of ESA CCI SM product alone. The methodology was applied for obtaining SSM maps for the area of California, USA during 2016. The accuracy of tested models was validated using five-fold cross-validation against in situ data and the best variation of model achieved RMSE, R 2 and MAE of 0.0518 m 3 /m 3 , 0.7312 and 0.0374 m 3 /m 3 , respectively. The methodology proved to be useful for generating high-resolution SSM products, although additional improvements are necessary.

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“…The Random Forest model is specifically chosen here for its ability to handle complex nonlinear relationships with reduced overfitting. It has also shown promising results in many soil moisture downscaling studies (Abbaszadeh et al, ; Im et al, ; Park et al, ; Zhao et al, ).…”

Section: Introductionmentioning

confidence: 99%

Gap Filling of High‐Resolution Soil Moisture for SMAP/Sentinel‐1: A Two‐Layer Machine Learning‐Based Framework

Mao

Kathuria

Duffield

et al. 2019

Water Resources Research

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As the most recent 3‐km soil moisture product from the Soil Moisture Active Passive (SMAP) mission, the SMAP/Sentinel‐1 L2_SM_SP product has a unique capability to provide global‐scale 3‐km soil moisture estimates through the fusion of radar and radiometer microwave observations. The spatial and temporal availability of this high‐resolution soil moisture product depends on concurrent radar and radiometer observations which is significantly restricted by the narrow swath and low revisit schedule of the Sentinel‐1 radars. To address this issue, this paper presents a novel two‐layer machine learning‐based framework which predicts the brightness temperature and subsequently the soil moisture at gap areas. The proposed method is able to gap‐fill soil moisture satisfactorily at areas where the radiometer observations are available while the radar observations are missing. We find that incorporating historical radar backscatter measurements (30‐day average) into the machine learning framework boosts its predictive performance. The effectiveness of the two‐layer framework is validated against regional holdout SMAP/Sentinel‐1 3‐km soil moisture estimates at four study areas with distinct climate regimes. Results indicate that our proposed method is able to reconstruct 3‐km soil moisture at gap areas with high Pearson correlation coefficient (47%/35%/20%/80% improvement of mean R, at Arizona/Oklahoma/Iowa/Arkansas) and low unbiased Root Mean Square Error (20%/10%/7%/26% improvement of mean unbiased root mean square error) when compared to the SMAP 33‐km soil moisture product. Additional validations against airborne data and in situ data from soil moisture networks are also satisfactory.

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Downscaling SMAP Radiometer Soil Moisture Over the CONUS Using an Ensemble Learning Method

Cited by 152 publications

References 103 publications

Leveraging machine learning for predicting flash flood damage in the Southeast US

Leveraging machine learning for predicting flash flood damage in the Southeast US

New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

Gap Filling of High‐Resolution Soil Moisture for SMAP/Sentinel‐1: A Two‐Layer Machine Learning‐Based Framework

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