Potential of multispectral and hyperspectral data to detect saline-exposed soils in Brazil

Moreira, Luís Clênio Jário; Teixeira, Adunias dos Santos; Galvão, Lênio Soares

doi:10.1080/15481603.2015.1040227

Cited by 38 publications

(27 citation statements)

References 39 publications

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“…Unfortunately, these features in pixel spectra are coincident with strong water vapor absorptions that cannot be used in the data analysis even after atmospheric correction. Overall, our results are in agreement with those obtained by Moreira et al [22], who observed a positive correlation between soil brightness and soil salinization, as expressed by increasing values of EC. Such correlation is not observed for all the salts and is typical of the predominance of NaCl.…”

Section: Discussionsupporting

confidence: 94%

“…Brightness corresponds approximately to the mean reflectance calculated between 400 and 2500 nm. Our results confirmed PC1 as a proxy of soil brightness because its eigenvector loadings were approximately similar for the 357 simulated VNIR-SWIR bands of the ProSpecTIR-VS. As a result, PC1 was highly correlated with the average reflectance calculated between 400 and 2500 nm (r = +0.87), as also observed by Moreira et al [22]. That was the reason that PC1 and the reflectance of three bands placed in this interval were selected as the best attributes for the computational models.…”

Section: Discussionsupporting

confidence: 71%

“…In the literature, soil brightness is generally related to the first principal component [22]. Brightness corresponds approximately to the mean reflectance calculated between 400 and 2500 nm.…”

Section: Discussionmentioning

confidence: 99%

“…At this stage, laboratory reflectance data obtained with the FieldSpec Pro FR-3 were resampled to the 357 bands of the ProSpecTIR-VS airborne hyperspectral sensor. Earlier studies have shown that PCA can be useful in studies of soil salinity, since the first component is usually associated with the brightness (average reflectance) of the samples, which increases with salt concentration [20][21][22]. It was therefore decided that PCA should be applied to the laboratory data resampled to the bands of the ProSpecTIR-VS. As the first derivative can enhance spectral features associated with salts, derivative analysis was applied to the resampled reflectance data, preceded by the use of a moving average filter with a three-band window.…”

Section: Ec and Spectral Reflectance Measurementsmentioning

confidence: 99%

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Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation

et al. 2017

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Soil salinization due to irrigation affects agricultural productivity in the semi-arid region of Brazil. In this study, the performance of four computational models to estimate electrical conductivity (EC) (soil salinization) was evaluated using laboratory reflectance spectroscopy. To investigate the influence of bandwidth and band positioning on the EC estimates, we simulated the spectral resolution of two hyperspectral sensors (airborne ProSpecTIR-VS and orbital Hyperspectral Infrared Imager (HyspIRI)) and three multispectral instruments (RapidEye/REIS, High Resolution Geometric (HRG)/SPOT-5, and Operational Land Imager (OLI)/Landsat-8)). Principal component analysis (PCA) and the first-order derivative analysis were applied to the data to generate metrics associated with soil brightness and spectral features, respectively. The three sets of data (reflectance, PCA, and derivative) were tested as input variable for Extreme Learning Machine (ELM), Ordinary Least Square regression (OLS), Partial Least Squares Regression (PLSR), and Multilayer Perceptron (MLP). Finally, the laboratory models were inverted to a ProSpecTIR-VS image (400-2500 nm) acquired with 1-m spatial resolution in the northeast of Brazil. The objective was to estimate EC over exposed soils detected using the Normalized Difference Vegetation Index (NDVI). The results showed that the predictive ability of the linear models and ELM was better than that of the MLP, as indicated by higher values of the coefficient of determination (R 2 ) and ratio of the performance to deviation (RPD), and lower values of the root mean square error (RMSE). Metrics associated with soil brightness (reflectance and PCA scores) were more efficient in detecting changes in the EC produced by soil salinization than metrics related to spectral features (derivative). When applied to the image, the PLSR model with reflectance had an RMSE of 1.22 dS·m −1 and an RPD of 2.21, and was more suitable for detecting salinization (10-20 dS·m −1 ) in exposed soils (NDVI < 0.30) than the other models. For all computational models, lower values of RMSE and higher values of RPD were observed for the narrowband-simulated sensors compared to the broadband-simulated instruments. The soil EC estimates improved from the RapidEye to the HRG and OLI spectral resolutions, showing the importance of shortwave intervals (SWIR-1 and SWIR-2) in detecting soil salinization when the reflectance of selected bands is used in data modelling.

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Section: Discussionsupporting

confidence: 94%

Section: Discussionsupporting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Section: Ec and Spectral Reflectance Measurementsmentioning

confidence: 99%

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Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation

et al. 2017

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“…SVR, as an extension of SVM, uses the same principles as SVM for classification, while it can produce real numbers as output given a set of independent input variables by defining a margin of tolerance (ε). Both SVM and SVR have been proven robust for classification and the estimation of continuous properties in remote sensing [58][59][60][61][62][63][64]. In this study, LibSVM (version 3.20) tools developed by Chang and Lin [65] were adopted to implement SVR, for which the radius basis function (RBF) kernel was used and the optimal combination of parameters such as cost, gamma and ε were selected using a grid search approach.…”

Section: Machine Learning-based Downscaling Modelsmentioning

confidence: 99%

Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches

Park

et al. 2016

Remote Sensing

128

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This study presented a MODIS 8-day 1 km evapotranspiration (ET) downscaling method based on Landsat 8 data (30 m) and machine learning approaches. Eleven indicators including albedo, land surface temperature (LST), and vegetation indices (VIs) derived from Landsat 8 data were first upscaled to 1 km resolution. Machine learning algorithms including Support Vector Regression (SVR), Cubist, and Random Forest (RF) were used to model the relationship between the Landsat indicators and MODIS 8-day 1 km ET. The models were then used to predict 30 m ET based on Landsat 8 indicators. A total of thirty-two pairs of Landsat 8 images/MODIS ET data were evaluated at four study sites including two in United States and two in South Korea. Among the three models, RF produced the lowest error, with relative Root Mean Square Error (rRMSE) less than 20%. Vegetation greenness related indicators such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and vegetation moisture related indicators such as Normalized Difference Infrared Index-Landsat 8 OLI band 7 (NDIIb7) and Normalized Difference Water Index (NDWI) were the five most important features used in RF model. Temperature-based indicators were less important than vegetation greenness and moisture-related indicators because LST could have considerable variation during each 8-day period. The predicted Landsat downscaled ET had good overall agreement with MODIS ET (average rRMSE = 22%) and showed a similar temporal trend as MODIS ET. Compared to the MODIS ET product, the downscaled product demonstrated more spatial details, and had better agreement with in situ ET observations (R 2 = 0.56). However, we found that the accuracy of MODIS ET was the main control factor of the accuracy of the downscaled product. Improved coarse-resolution ET estimation would result in better finer-resolution estimation. This study proved the potential of using machine learning approaches for ET downscaling considering their effectiveness and ease of implementation. Future research includes development of the spatial-temporal fusion models of Landsat data and MODIS ET in order to increase temporal resolution of downscaled ET.

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Agricultural Land Degradation in Brazil

Oliveira

Godoi

Colman

et al. 2022

The Handbook of Environmental Chemistry

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Potential of multispectral and hyperspectral data to detect saline-exposed soils in Brazil

Cited by 38 publications

References 39 publications

Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation

Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation

Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches

Agricultural Land Degradation in Brazil

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