Thermal Inertia-Based Method for Estimating Soil Moisture

Matsushima, Dai

doi:10.5772/intechopen.80252

Cited by 6 publications

(2 citation statements)

References 52 publications

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“…It was indicated that the GOES-8 satellite imagery derived LST increased with the decrease of observed SSM (Sun and Pinker 2004). Furthermore, the daily LST difference is negatively related to the thermal inertia of soil, while thermal inertia increases with soil moisture increase (Matsushima 2018;Paruta et al 2020) (Zhuang et al 2023).…”

Section: Land Surface Temperature and Air Temperaturementioning

confidence: 99%

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

Han

Zeng

Zhang³

et al. 2023

Preprint

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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 (ML) based systematic integration of in-situ hydrological measurements, complex environmental and climate data and satellite observation facilitate to generate the best data products to monitor and analyse the exchanges of water, energy and carbon in the Earth system at a proper space-time resolution. This study investigates the estimation of daily SSM using eight optimised ML algorithms and ten ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using the international soil moisture network (ISMN) data collected from 1722 stations distributed across the World. The result showed that K-neighbours Regressor (KNR) performs best on “test_random” set, while Random Forest Regressor (RFR) performs best on “test_temporal” and “test_independent-stations”. Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSEs were below 0.1 cm3/cm3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and Random Forest Regressor (RFR) achieved a median r score of 0.6 in twelve, eleven, nine and nine climate zones, respectively, out of fifteen climate zones. The performance of ensemble models improved significantly with the median value of RMSE below 0.075 cm3/cm3 for all climate zones . All voting regressors achieved the r scores of above 0.6 in thirteen climate zones except BSh and BWh because of the sparse distribution of training stations. The metrical 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 XB performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability for predicting SSM compared to the optimised, or base ML algorithms, and indicates their huge potential applicability in estimating water cycle budgets, managing irrigation and predicting crop yields.

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Section: Land Surface Temperature and Air Temperaturementioning

confidence: 99%

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

Han

Zeng

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…LST dominates the pattern of potential evapotranspiration and plays an essential role in SSM retrieval [35], for example, the GOES-8 satellite imager derived LST increases with the decrease of observed SSM [36]. Furthermore, the daily LST difference is related to the thermal inertia of soil with a negative relationship, while thermal inertia increases with soil moisture increase [8,37]. Thus, the daily LST difference between daytime and nighttime was also selected as a predictor variable.…”

Section: Land Surface Features and Precipitationmentioning

confidence: 99%

In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model

et al. 2021

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The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m3 m−3 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m−3 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.

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