Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

Zeng, Linglin; Hu, Shun; Xiang, Daxiang; Zhang, Xiang; Li, Deren; Lin, Li; Zhang, Tingqiang

doi:10.3390/rs11030284

Cited by 27 publications

(11 citation statements)

References 80 publications

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“…In addition, the corresponding accuracy differences between the global and continental models tend to be more significant in the continents with sparse observation stations, e.g., Africa, and not obvious in the continents with more observation stations, e.g., North America because on the global scale, the training data from the NOAA dataset is strongly biased towards America and Europe in terms of the station density, data quantity and even data quality and not favored to Africa and South America. Therefore, the global model tended to be biased in In addition, generally higher accuracy was observed in 8-day Ta estimation than that in daily estimation, which was in accordance with other studies (e.g., [56]) as daily Ta and LST affected by the ever-changing factors show higher variability than the 8-day composites [17], which are more difficult to be accurately captured. This is because the surface Ta is determined by two physical processes, the heating effect produced by the longwave radiation from the land surface and the advective effect resulting from turbulent flow exchange.…”

Section: Model Performance When Trained and Cross-validated On Global Continental And Climate Zone Scalessupporting

confidence: 87%

“…Considering the large data volume and the time cost, the number of regression trees (ntree) is set as 100 and the number of input variables per node (mtry) was tested from 1/3 to all the total number of variables by one iteration of random sampling for each kind of estimation (e.g., daily Tmax). To find the optimal mtry values, ntree were optimized based on the root mean square error (RMSE) of the calibration using the training dataset [56].…”

Section: Model Performance When Trained and Validated On Global Continental And Climate Zone Scalesmentioning

confidence: 99%

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8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

et al. 2021

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Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

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Section: Model Performance When Trained and Cross-validated On Global Continental And Climate Zone Scalessupporting

confidence: 87%

Section: Model Performance When Trained and Validated On Global Continental And Climate Zone Scalesmentioning

confidence: 99%

8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

et al. 2021

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“…Estimates of the regional spatial-temporal variability of surface soil moisture (SSM) are in crucial need for better understanding the energy, water, and carbon exchanges at the land-atmosphere interface [1]. Indeed, surface soil moisture is a key state variable in various processes occurring on this interface, such as the partitioning of precipitation into infiltration and runoff [2] or of incoming solar radiation into sensible and latent heat [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data

et al. 2019

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The main objective of this work was to retrieve surface soil moisture (SSM) by using scattering models and a support vector machine (SVM) technique driven by backscattering coefficients obtained from Sentinel-1 satellite images acquired over bare agricultural soil in the Tensfit basin of Morocco. Two backscattering models were selected in this study due to their wide use in inversion procedures: the theoretical integral equation model (IEM) and the semi-empirical model (Oh). To this end, the sensitivity of the SAR backscattering coefficients at V V ( σ v v ∘ ) and V H ( σ v h ∘ ) polarizations to in situ soil moisture data were analyzed first. As expected, the results showed that over bare soil the σ v v ∘ was well correlated with SSM compared to the σ v h ∘ , which showed more dispersion with correlation coefficients values (r) of about 0.84 and 0.61 for the V V and V H polarizations, respectively. Afterwards, these values of σ v v ∘ were compared to those simulated by the backscatter models. It was found that IEM driven by the measured length correlation L slightly underestimated SAR backscatter coefficients compared to the Oh model with a bias of about − 0.7 dB and − 1.2 dB and a root mean square (RMSE) of about 1.1 dB and 1.5 dB for Oh and IEM models, respectively. However, the use of an optimal value of L significantly improved the bias of IEM, which became near to zero, and the RMSE decreased to 0.9 dB. Then, a classical inversion approach of σ v v ∘ observations based on backscattering model is compared to a data driven retrieval technic (SVM). By comparing the retrieved soil moisture against ground truth measurements, it was found that results of SVM were very encouraging and were close to those obtained by IEM model. The bias and RMSE were about 0.28 vol.% and 2.77 vol.% and − 0.13 vol.% and 2.71 vol.% for SVM and IEM, respectively. However, by taking into account the difficultly of obtaining roughness parameter at large scale, it was concluded that SVM is still a useful tool to retrieve soil moisture, and therefore, can be fairly used to generate maps at such scales.

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“…These results show that soil properties, leaching, and transport estimation should not be generalized solely based on soil type. According to these results, when mapping the soil moisture, estimation could be very challenging because it can vary highly in spatial and temporal distribution [81] and using soil texture for pedotransfer functions might not be sufficient for accurate mapping of heterogenous soil hydraulic properties [11].…”

Section: Variability Of Soil Hydraulic Parameters and Layering Impactmentioning

confidence: 99%

Determination of Soil Hydraulic Parameters and Evaluation of Water Dynamics and Nitrate Leaching in the Unsaturated Layered Zone: A Modeling Case Study in Central Croatia

et al. 2021

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Nitrate leaching through soil layers to groundwater may cause significant degradation of natural resources. The aims of this study were: (i) to estimate soil hydraulic properties (SHPs) of the similar soil type with same management on various locations; (ii) to determine annual water dynamics; and (iii) to estimate the impact of subsoil horizon properties on nitrate leaching. The final goal was to compare the influence of different SHPs and layering on water dynamics and nitrate leaching. The study was conducted in central Croatia (Zagreb), at four locations on Calcaric Phaeozem, Calcaric Regosol, and Calcaric Fluvic Phaeozem soil types. Soil hydraulic parameters were estimated using the HYPROP system and HYPROP-FIT software. Water dynamics and nitrate leaching were evaluated using HYDRUS 2D/3D during a period of 365 days. The amount of water in the soil under saturated conditions varied from 0.422 to 0.535 cm3 cm–3 while the hydraulic conductivity varied from 3 cm day−1 to 990.9 cm day−1. Even though all locations have the same land use and climatic conditions with similar physical properties, hydraulic parameters varied substantially. The amount and velocity of transported nitrate (HYDRUS 2D/3D) were affected by reduced hydraulic conductivity of the subsoil as nitrates are primarily transported via advective flux. Despite the large differences in SHPs of the topsoil layers, the deeper soil layers, having similar SHPs, imposed a buffering effect preventing faster nitrate downward transport. This contributed to a very similar distribution of nitrates through the soil profile at the end of simulation period. This case study indicated the importance of carefully selecting relevant parameters in multilayered soil systems when evaluating groundwater pollution risk.

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Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

Cited by 27 publications

References 80 publications

8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data

Determination of Soil Hydraulic Parameters and Evaluation of Water Dynamics and Nitrate Leaching in the Unsaturated Layered Zone: A Modeling Case Study in Central Croatia

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