Improved soil moisture estimation: Synergistic use of satellite observations and land surface models over CONUS based on machine learning

Lee, Jaese; Park, Sumin; Im, Jungho; Yoo, Cheolhee; Seo, Eunkyo

doi:10.1016/j.jhydrol.2022.127749

Cited by 9 publications

(3 citation statements)

References 110 publications

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“…Earlier models for estimating SM generally have many model parameters (Wang et al, 2022a;Zhang et al, 2022a;Kisekka et al, 2022;Tramblay and Segui, 2022). However, these parameters have varying data accuracy, especially at large spatial and temporal scales (Lee et al, 2022). Moreover, there may be some autocorrelation between multiple model parameters.…”

Section: Introductionmentioning

confidence: 99%

Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands

Wang

2023

Front. Environ. Sci.

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Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region.

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Section: Introductionmentioning

confidence: 99%

Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands

Wang

2023

Front. Environ. Sci.

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“…Traditional employee turnover theories tend to analyze and compare only a small portion of employee data, and cannot fully analyze the collected employee information data comprehensively, which may lead to inaccurate results and cause companies to make wrong decisions in the end [ 6 , 7 ]. At the same time, there is a large amount of unbalanced data with widely varying sample sizes in real life, such as medical pathology diagnosis, credit card fraud, network intrusion information, business operations and employee turnover data [ 8 , 9 ]. The traditional Support Vector Machine (SVM) can lead to the resulting hyperplane being more biased toward minority class, making them misclassified as majority class.…”

Section: Introductionmentioning

confidence: 99%

Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data

Li,

Fox

2023

PLoS ONE

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The sudden resignation of core employees often brings losses to companies in various aspects. Traditional employee turnover theory cannot analyze the unbalanced data of employees comprehensively, which leads the company to make wrong decisions. In the face the classification of unbalanced data, the traditional Support Vector Machine (SVM) suffers from insufficient decision plane offset and unbalanced support vector distribution, for which the Synthetic Minority Oversampling Technique (SMOTE) is introduced to improve the balance of generated data. Further, the Fuzzy C-mean (FCM) clustering is improved and combined with the SMOTE (IFCM-SMOTE-SVM) to new synthesized samples with higher accuracy, solving the drawback that the separation data synthesized by SMOTE is too random and easy to generate noisy data. The kernel function is combined with IFCM-SMOTE-SVM and transformed to a high-dimensional space for clustering sampling and classification, and the kernel space-based classification algorithm (KS-IFCM-SMOTE-SVM) is proposed, which improves the effectiveness of the generated data on SVM classification results. Finally, the generalization ability of KS-IFCM-SMOTE-SVM for different types of enterprise data is experimentally demonstrated, and it is verified that the proposed algorithm has stable and accurate performance. This study introduces the SMOTE and FCM clustering, and improves the SVM by combining the data transformation in the kernel space to achieve accurate classification of unbalanced data of employees, which helps enterprises to predict whether employees have the tendency to leave in advance.

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“…This allows an ML model to make more accurate predictions of soil moisture based on remote-sensing data. Many efforts have been directed toward improving soil moisture prediction in the community using ML techniques (Abowarda et al, 2021;Karthikeyan and Mishra, 2021;Lee et al, 2022;Lei et al, 2022;Sungmin and Orth, 2021;. At the point scale, work has compared three ML algorithms in the laboratory using a radar sensor (Uthayakumar et al, 2022).…”

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confidence: 99%

Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale

Han,

Zeng,

Zhang

et al. 2023

Geosci. Model Dev.

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Abstract. Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest root-mean-square error (0.0379 cm3 cm−3) on the “test_random” set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0.0599 cm3 cm−3) on the “test_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0.0786 cm3 cm−3) on the “test_independent-stations” set (for testing the performance on the stations that were not used in training). Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0.1 cm3 cm−3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm−3 for all climate zones. All voting regressors achieved r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.

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Improved soil moisture estimation: Synergistic use of satellite observations and land surface models over CONUS based on machine learning

Cited by 9 publications

References 110 publications

Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands

Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands

Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data

Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale

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