Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Adab, Hamed; Morbidelli, Renato; Saltalippi, Carla; Moradian, Mahmoud; Ghalhari, Gholam Abbas Fallah

doi:10.3390/w12113223

Cited by 98 publications

(51 citation statements)

References 114 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A support vector machine (SVM) or its regression version, support vector regression (SVR), is a supervised machine learning algorithm, which is powerful and flexible for classification and regression [35]. Using an optimal kernel, SVM maps the input data into different classes in a hyperplane in multidimensional space in an iterative manner until it finds a maximum marginal hyperplane, in which the differences among classes are maximized so as to minimize the error of classification [34].…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

et al. 2021

View full text Add to dashboard Cite

In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R2), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R2 = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening.

show abstract

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In this method, a penalty parameter is used as a regularization parameter, which represents the bias added to the regression coefficient in the equation [44]. The linear regression model that was used in the L1 regularization method is called the least absolute shrinkage and selection operator (LASSO), while the model that used L2 is called a ridge operator [45].…”

Section: Selection Of Groundwater Conditioning Factorsmentioning

confidence: 99%

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Park

2021

Sustainability

View full text Add to dashboard Cite

Understanding the potential groundwater resource distribution is critical for sustainable groundwater development, conservation, and management strategies. This study analyzes and maps the groundwater potential in Busan Metropolitan City, South Korea, using random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGB) methods. Fourteen groundwater conditioning factors were evaluated for their contribution to groundwater potential assessment using an elastic net. Curvature, the stream power index, the distance from drainage, lineament density, and fault density were excluded from the subsequent analysis, while nine other factors were used to create groundwater potential maps (GMPs) using the RF, GBM, and XGB models. The accuracy of the resultant GPMs was tested using receiver operating characteristic curves and the seed cell area index, and the results were compared. The analysis showed that the three models used in this study satisfactorily predicted the spatial distribution of groundwater in the study area. In particular, the XGB model showed the highest prediction accuracy (0.818), followed by the GBM (0.802) and the RF models (0.794). The XGB model, which is the most recently developed technique, was found to best contribute to improving the accuracy of the GPMs. These results contribute to the establishment of a sustainable management plan for groundwater resources in the study area.

show abstract

“…Advances in machine learning (ML) techniques have been mostly utilized in hydrology (Lange and Sippel 2020) and climate research (Huntingford et al 2019) for the prediction and forecasting of environmental variables (Li et al 2011), as well as the optimization of model parameters. Over the last few years, ML approaches have become more common in soil hydrology research to estimate model-derived RZSM using ANNs or satellite-derived SSM using SVMs (Yu et al 2012;Adab et al 2020;Carranza et al 2021). Yu et al (2012) used SVMs and the ensemble particle filter (EnPF) to develop a multi-layer soil moisture prediction model for the Meilin watershed in China, which showed that SVMs are statistically significant and resilient for soil moisture prediction in both the surface and root zone layers.…”

Section: Introductionmentioning

confidence: 99%

Spatial–temporal modeling of root zone soil moisture dynamics in a vineyard using machine learning and remote sensing

Kisekka

Peddinti

Kustas³

et al. 2022

Irrig Sci

View full text Add to dashboard Cite

High-resolution spatial–temporal root zone soil moisture (RZSM) information collected at different scales is useful for a variety of agricultural, hydrologic, and climate applications. RZSM can be estimated using remote sensing, empirical equations, or process-based simulation models. Machine learning (ML) approaches for evaluating RZSM across numerous spatial–temporal scales are less generalizable than process-based models. However, data-driven ML approaches offer a unique opportunity to develop complex models of soil moisture without making assumptions about the processes governing soil water dynamics in a given study region. In this study, comparisons were made between two models, pySEBAL and EFSOIL, which were based on evaporation fraction (EF) and soil properties, and a data-driven model based on the Random Forest (RF) ensemble algorithm. These approaches were evaluated to demonstrate their capabilities for RZSM estimation. The EF obtained from Landsat images was used after validation with eddy covariance measurements as the major input to all three models, along with other meteorological and soil physical properties. The RF model was trained using in situ soil moisture data from Time Domain Reflectometry (TDR) sensors installed in a vineyard from 2018 to 2020. The predictor variables comprised of meteorological, soil properties, EF, and a vegetation index. The results reveal that there was a strong correlation between the in situ measured soil moisture and the RF predicted soil moisture at all sensor locations. Due to the complexity of the physical processes involved in soil water flow, the empirical models pySEBAL and EFSOIL were unable to reliably predict RZSM values at all monitored locations. The high RZSM predicted by pySEBAL demonstrated the presence of possible bias in the model’s algorithm used to estimate soil moisture. We also demonstrated that ML based on the RF algorithm may be used to predict spatially distributed RZSM when a few soil moisture ground measurements are combined with remote sensing to produce soil moisture maps.

show abstract

Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Cited by 98 publications

References 114 publications

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

The Predictive Capability of a Novel Ensemble Tree-Based Algorithm for Assessing Groundwater Potential

Spatial–temporal modeling of root zone soil moisture dynamics in a vineyard using machine learning and remote sensing

Contact Info

Product

Resources

About