Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow

Friedel, Michael J.; Buscema, Massimo; Vicente, L. E.; Iwashita, Fabio; Koga-Vicente, Andrea

doi:10.1080/17538947.2017.1349841

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…Other challenges for satellite applications are the relatively low image resolution, restricted availability of high-quality temporal and spatial images, primarily as a result of adverse atmospheric conditions and sensor requirements [71]. For example, in Brazil, Friedel et al [72] utilized spectroscopy techniques and spaceborne (Hyperion satellite) imagery to quantify soil obtained from the tropics. They indicated that because of the presence of shadow within the study area, satellite image efficiency was hampered.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery

et al. 2021

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Soil organic carbon (SOC) is a variable of vital environmental significance in terms of soil quality and function, global food security, and climate change mitigation. Estimation of its content and prediction accuracy on a broader scale remain crucial. Although, spectroscopy under proximal sensing remains one of the best approaches to accurately predict SOC, however, spectroscopy limitation to estimate SOC on a larger spatial scale remains a concern. Therefore, for an efficient quantification of SOC content, faster and less costly techniques are needed, recent studies have suggested the use of remote sensing approaches. The primary aim of this research was to evaluate and compare the capabilities of small Unmanned Aircraft Systems (UAS) for monitoring and estimation of SOC with those obtained from spaceborne (Sentinel-2) and proximal soil sensing (field spectroscopy measurements) on an agricultural field low in SOC content. Nine calculated spectral indices were added to the remote sensing approaches (UAS and Sentinel-2) to enhance their predictive accuracy. Modeling was carried out using various bands/wavelength (UAS (6), Sentinel-2 (9)) and the calculated spectral indices were used as independent variables to generate soil prediction models using five-fold cross-validation built using random forest (RF) and support vector machine regression (SVMR). The correlation regarding SOC and the selected indices and bands/wavelengths was determined prior to the prediction. Our results revealed that the selected spectral indices slightly influenced the output of UAS compared to Sentinel-2 dataset as the latter had only one index correlated with SOC. For prediction, the models built on UAS data had a better accuracy with RF than the two other data used. However, using SVMR, the field spectral prediction models achieved a better overall result for the entire study (log(1/R), RPD = 1.40; R2CV = 0.48; RPIQ = 1.65; RMSEPCV = 0.24), followed by UAS and then Sentinel-2, respectively. This study has shown that UAS imagery can be exploited efficiently using spectral indices.

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Section: Discussionmentioning

confidence: 99%

Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery

et al. 2021

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“…The authors in [12,13] stated that Sentinel-2 data indicate a high potential for applied forestry and vegetation analysis. The authors in [14] used unsupervised machine learning to map landscape…”

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confidence: 99%

Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns

et al. 2019

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The most commonly used model for analyzing satellite imagery is the Support Vector Machine (SVM). Since there are a large number of possible variables for use in SVM, this paper will provide a combination of parameters that fit best for extracting green urban areas from Copernicus mission satellite images. This paper aims to provide a combination of parameters to extract green urban areas with the highest degree of accuracy, in order to speed up urban planning and ultimately improve town environments. Two different towns in Croatia were investigated, and the results provide an optimal combination of parameters for green urban areas extraction with an overall kappa index of 0.87 and 0.89, which demonstrates a very high classification accuracy.

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“…However, as a rich and diverse data source, combining and preprocessing RS data is often expensive for ML, requiring standardizing data across sources; thus, an automated data fusion procedure (Bean et al, 2018) and pattern extraction scheme (Wang et al, 2019) were needed to expand ML applicability. Friedel et al (2018) proposed a workflow for soil mapping to efficiently use RS spectral data for ensem-ble clustering before training unsupervised self-organizing map network. Based on RS information, global soil texture mapping accuracies have also been evaluated using CNN (Beucher et al, 2022), ANN (Ghanbarian & Yokeley, 2021), and other MLs, such as SVM (Ließ et al, 2021;Okujeni et al, 2018), BRT (Gebauer et al, 2022), and Cubist tree (Silvero et al, 2021).…”

Section: Soil Texturesmentioning

confidence: 99%

Machine learning applications in vadose zone hydrology: A review

Li,

Nieber,

Kumar

2024

Vadose Zone Journal

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Machine learning (ML) has been broadly applied for vadose zone applications in recent years. This article provides a comprehensive review of such developments. ML applications for variables corresponding to different complex vadose zone processes are summarized mostly in a prediction context. By analyzing and assessing these applications, we discovered extensive usages of classic ML models with relatively limited applications of deep learning (DL) approaches in general. We also recognized a lack of benchmark datasets for soil property research as well as limited integration of physics‐based vadose zone principles into the ML approaches. To facilitate this interdisciplinary research of ML in vadose zone characterization and processes, a paradigm of knowledge‐guided machine learning is suggested along with other data‐driven and ML model‐based research suggestions to advance future research.

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Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow

Cited by 7 publications

References 34 publications

Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery

Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery

Support Vector Machine Accuracy Assessment for Extracting Green Urban Areas in Towns

Machine learning applications in vadose zone hydrology: A review

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