Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon

Castaldi, Fabio; Palombo, Angelo; Santini, Federico; Pascucci, Simone; Pignatti, Stefano; Casa, Raffaele

doi:10.1016/j.rse.2016.03.025

Cited by 222 publications

(130 citation statements)

References 37 publications

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“…Our results from sensor simulation were in agreement with a recent study by Castaldi et al [28]. When evaluating the potential of the current and forthcoming multispectral and hyperspectral sensors to estimate physico-chemical attributes of soils from Europe using PLSR, Castaldi et al [29] found large RPD values for simulated hyperspectral instruments than for multispectral sensors.…”

Section: Discussionsupporting

confidence: 91%

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: 91%

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

et al. 2017

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“…These hyperspectral sensors, which have spatial resolutions between~8-30 m, are expected to provide higher SNRs than current sensors, especially in the SWIR region, and could allow for an operational quantitative SOC mapping at low cost with global coverage. SOC could be estimated using the forthcoming sensors PRISMA, EnMAP, and HyspIRI with an intermediate accuracy (RMSE about 0.2% and R 2 between~0.5-0.7), according to the tests performed by Castaldi et al [96] using simulated data.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

et al. 2017

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Abstract:The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and oxidizable organic C (OC) content were calibrated using two datasets: a ground observation dataset with 39 topsoil samples collected in the field (for models built using AHS data), and a dataset with 200 TOC/OC observations predicted by AHS (for models built using Hyperion data). For both datasets, the prediction was performed by stepwise multiple linear regression (SMLR) using reflectances and spectral indices (SI) obtained from the images, and by the widely-used partial least squares regression (PLSR) method. SMLR provided a performance comparable to or even better than PLSR, while using a lower number of channels. SMLR models for the AHS were based on a maximum of eight indices, and showed a coefficient of determination in the leave-one-out cross-validation R 2 = 0.60-0.62, while models for the Hyperion sensor showed R 2 = 0.49-0.61, using a maximum of 20 indices. Although slightly worse models were obtained for the Hyperion sensor, which was attributed to its lower signal-to-noise ratio (SNR), the prediction of TOC/OC was consistent across both sensors. The relevant wavelengths for TOC/OC predictions were the red region of the spectrum (600-700 nm), and the short wave infrared region betweeñ 2000-2250 nm. The use of SMLR and spectral indices based on reference channels at~1000 nm was suitable to quantify topsoil C, and provided an alternative to the more complex PLSR method.

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“…PLSR allows summarizing of the spectral information and, therefore, in some cases a very narrow band width is not strictly necessary. Castaldi et al [9] showed that reducing the spectral resolution from 1 nm to 40 nm did not cause a significant decrease in the estimation accuracy for the SOC, clay, sand and silt content. Spectroscopy, applied in the laboratory, allows estimating soil variables only at the sampling points, while both airborne and satellite data provide quantitative soil information over large areas.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike airborne data, satellite acquisition ensures a cheap way to obtain spectral data over very large areas. However, the quantitative prediction of soil properties using the first generation of hyperspectral satellite sensors is hampered by the very low signal-to-noise ratio (SNR) in the SWIR region for Hyperion imagers on board of the NASA EO-1 platform [9,18,19], or by the restricted spectral range (415-1050 nm) for the Compact High Resolution Imaging Spectrometer (CHRIS) on the European Space Agency's PROBA platform [9,[20][21][22]. Because of the low SNR, Zhang et al [23] obtained unreliable PLSR predictions for soil moisture and clay content using Hyperion image reflectance spectra.…”

Section: Introductionmentioning

confidence: 99%

Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

et al. 2018

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Abstract:The most commonly used approach to estimate soil variables from remote-sensing data entails time-consuming and expensive data collection including chemical and physical laboratory analysis. Large spectral libraries could be exploited to decrease the effort of soil variable estimation and obtain more widely applicable models. We investigated the feasibility of a new approach, referred to as bottom-up, to provide soil organic carbon (SOC) maps of bare cropland fields over a large area without recourse to chemical analyses, employing both the pan-European topsoil database from the Land Use/Cover Area frame statistical Survey (LUCAS) and Airborne Prism Experiment (APEX) hyperspectral airborne data. This approach was tested in two areas having different soil characteristics: the loam belt in Belgium, and the Gutland-Oesling region in Luxembourg. Partial least square regression (PLSR) models were used in each study area to estimate SOC content, using both bottom-up and traditional approaches. The PLSR model's accuracy was tested on an independent validation dataset. Both approaches provide SOC maps having a satisfactory level of accuracy (RMSE = 1.5-4.9 g·kg −1 ; ratio of performance to deviation (RPD) = 1.4-1.7) and the inter-comparison did not show differences in terms of RMSE and RPD either in the loam belt or in Luxembourg. Thus, the bottom-up approach based on APEX data provided high-resolution SOC maps over two large areas showing the within-and between-field SOC variability.

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Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon

Cited by 222 publications

References 37 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

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database

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