Soil Heat Flux Modeling Using Artificial Neural Networks and Multispectral Airborne Remote Sensing Imagery

Canelon, Dario; Chávez, José L.

doi:10.3390/rs3081627

Cited by 9 publications

(11 citation statements)

References 24 publications

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“…These new methods have been used to decrease costs while trying to maintain or even increase accuracy and spatial resolution so that they can better identify and characterize “physical, chemical, and biological properties” of soils [ 5 ]. Some examples of successful, multi-scaled, utilizations of multispectral and hyperspectral sensors range from mapping of salt-affected soils using Landsat [ 6 ], to using a satellite platform to model soil heat flux using airborne hyperspectral sensors over farmlands [ 7 ], measuring tropical soil characteristics using narrow band hyperspectral models [ 8 ] in a laboratory setting or country level mapping of soils using 2,350 samples from across Australia [ 9 ]. These applications highlight the diversity of possible uses and have led to the identification of different soil properties and types through nondestructive methods.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Analysis of Soil Nitrogen, Carbon, Carbonate, and Organic Matter Using Regression Trees

Gmur

Vogt

Zabowski

et al. 2012

Sensors

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The characterization of soil attributes using hyperspectral sensors has revealed patterns in soil spectra that are known to respond to mineral composition, organic matter, soil moisture and particle size distribution. Soil samples from different soil horizons of replicated soil series from sites located within Washington and Oregon were analyzed with the FieldSpec Spectroradiometer to measure their spectral signatures across the electromagnetic range of 400 to 1,000 nm. Similarity rankings of individual soil samples reveal differences between replicate series as well as samples within the same replicate series. Using classification and regression tree statistical methods, regression trees were fitted to each spectral response using concentrations of nitrogen, carbon, carbonate and organic matter as the response variables. Statistics resulting from fitted trees were: nitrogen R2 0.91 (p < 0.01) at 403, 470, 687, and 846 nm spectral band widths, carbonate R2 0.95 (p < 0.01) at 531 and 898 nm band widths, total carbon R2 0.93 (p < 0.01) at 400, 409, 441 and 907 nm band widths, and organic matter R2 0.98 (p < 0.01) at 300, 400, 441, 832 and 907 nm band widths. Use of the 400 to 1,000 nm electromagnetic range utilizing regression trees provided a powerful, rapid and inexpensive method for assessing nitrogen, carbon, carbonate and organic matter for upper soil horizons in a nondestructive method.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Analysis of Soil Nitrogen, Carbon, Carbonate, and Organic Matter Using Regression Trees

Gmur

Vogt

Zabowski

et al. 2012

Sensors

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“…ML also shown optimal results for the estimation of environmental variables [52], H and LE retrievals [e.g., 53], other SEB components such as Rn, H and LE [e.g., 54,55] or ET [56,57]. For the estimation of G, [32] modelled the heat flux with ANN and RS data, and [33] compared the ANN and two empirical equations based on NDVI, Ts and α. In accordance with our findings, in both of the works, ML outperformed the other methods for the estimation of G. The underestimation of the empirical equations and the large errors found at high Fv ranges are partly because the majority of the type I and II equations rely on a single linear relationship with the predictor variables.…”

Section: Analysis Of the Accuracy And Performance Of The Different Me...mentioning

confidence: 99%

“…In the last decades machine learning (ML) methods have been applied in the environmental science, generally obtaining accurate results. Nevertheless, up the date, only [32] modelled the G with RS data and the Artificial Neural Networks (ANN) ML algorithm. Furthermore, only [33] provided a comparison of the ANN model against two G/Rn empirical equations.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Machine Learning Outperforms Empirical Equations for the Ground Heat Flux Estimation with Remote Sensing Data

Bonsoms¹,

Boulet²

2022

Preprint

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Estimating evapotranspiration at field scale is a major component of sustainable water management. Due to the difficulty to assess some major unknowns of the water cycle at that scale, including irrigation amounts, evapotranspiration is often computed as the residual of the instantaneous surface energy budget. One of the Surface Energy Bal-ance components with the largest uncertainties in their quantification over bare soils and sparse vegetation areas is the ground heat flux (G). Over the last decades, the es-timation of G with RS data has been mainly achieved with empirical equations, on the basis of the G and net radiation (Rn) ratio, G/Rn. G/Rn empirical equations generally require vegetation data (Type I empirical equations), in combination with surface tem-perature (Ts) and albedo (Type II empirical equations). In this article we aim to evalu-ate the estimation of G with RS. For the first time, we compare eight G/Rn empirical equations against two types of machine learning (ML) methods: an ensemble ML type, the Random Forest (RF), and the Neural Networks (NN). The comparison of each method is evaluated over dense dataset, including a wide range of climate and land covers, with data of Eddy-Covariance towers extended along the mid-latitude area that encompass the European and African continent. Our results have shown evidence that the driver of G in bare soils and sparse vegetation areas (Fraction of Vegetation, Fv <= 0.25) is Ts, instead of vegetation greenness indexes. On the other hand, the estimation of G with Rn, Ts or Fv decreases at dense vegetation areas (Fv >= 0.50). There are not significant differences between the most accurate type I and II empirical equations. For bare soils and sparse vegetation areas the empirical equation that better estimates G is E8, which combines the Leaf Area Index (LAI) and Ts. In dense vegetation areas (Fv >= 0.25), an exponential empirical equation based on Fv (E4), shows the best performance. However, ML better estimates G than the empirical equations, independently of the Fv ranges. A RF model with Rn, LAI and Ts as predictor variables shows the best accuracy and performance metrics, outperforming the NN model.

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“…The performance of each network was evaluated adopting both the Correlation Coefficient, r [−1,1] and the Coefficient of Determination, R 2 [−1,1]. It is convenient to note that R 2 is equal to r in linear regression analyses, but that is not necessarily the case in ANN [ 38 ].…”

Section: Ann Simulation Modelmentioning

confidence: 99%

Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

et al. 2016

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A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration.

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Soil Heat Flux Modeling Using Artificial Neural Networks and Multispectral Airborne Remote Sensing Imagery

Cited by 9 publications

References 24 publications

Hyperspectral Analysis of Soil Nitrogen, Carbon, Carbonate, and Organic Matter Using Regression Trees

Hyperspectral Analysis of Soil Nitrogen, Carbon, Carbonate, and Organic Matter Using Regression Trees

Ensemble Machine Learning Outperforms Empirical Equations for the Ground Heat Flux Estimation with Remote Sensing Data

Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

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