Using Imaging Spectroscopy to study soil properties

Ben‐Dor, Eyal; Chabrillat, Sabine; Demattê, José Alexandre Melo; Taylor, G. R.; Hill, Joachim; Whiting, Michael L.; Sommer, Stefan

doi:10.1016/j.rse.2008.09.019

Cited by 453 publications

(268 citation statements)

References 72 publications

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“…Several research groups are now working on integrating airborne/ satellite sensing with ground-based soil sensing into a single framework (see e.g. work of Stevens et al (2008) and Ben-Dor et al (2009)). The newly launched SHALOM Hyperspectral Space Mission (Feingersh and Dor 2016) could be another source of possibly crucial remote sensing data for nutrient mapping and monitoring.…”

Section: Missing Covariatesmentioning

confidence: 99%

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Hengl

Leenaars

Shepherd

et al. 2017

Nutr Cycl Agroecosyst

222

153

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Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0-30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable-phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms- random forest and gradient boosting, as implemented in R packages ranger and xgboost-and then used to generate predictions in a fully-optimized computing system. Cross-validation revealed that apart from S, P and B, significant models can be produced for most targeted nutrients (R-square between 40-85%). Further comparison with OFRA field trial database shows that soil nutrients are indeed critical for agricultural development, with Mn, Zn, Al, B and Na, appearing as the most important nutrients for predicting crop yield. A limiting factor for mapping nutrients using the existing point data in Africa appears to be (1) the high spatial clustering of sampling locations, and (2) missing more detailed parent material/geological maps. Logical steps towards improving prediction accuracies include: further collection of input (training) point samples, further harmonization of measurement methods, addition of more detailed covariates specific to Africa, and implementation of a full spatiotemporal statistical modeling framework.

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Section: Missing Covariatesmentioning

confidence: 99%

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Hengl

Leenaars

Shepherd

et al. 2017

Nutr Cycl Agroecosyst

222

153

View full text Add to dashboard Cite

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“…Although the technique is mainly used under controlled laboratory conditions, with the rise in available portable spectrometers, its application in situ [5], as well as from the air-and space-borne sensors, is growing [6,7]. Under in-situ measurement conditions, additional challenges associated with the variation of soil-to-sensor distance affect the accuracy of the measurement [8,9].…”

Section: Introductionmentioning

confidence: 99%

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

et al. 2017

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Abstract:Soil spectroscopy has shown to be a fast, cost-effective, environmentally friendly, non-destructive, reproducible and repeatable analytical technique. Soil components, as well as types of instruments, protocols, sampling methods, sample preparation, spectral acquisition techniques and analytical algorithms have a combined influence on the final performance. Therefore, it is important to characterize these differences and to introduce an effective approach in order to minimize the technical factors that alter reflectance spectra and consequent prediction. To quantify this alteration, a joint project between Czech University of Life Sciences Prague (CULS) and Tel-Aviv University (TAU) was conducted to estimate Cox, pH-H 2 O, pH-KCl and selected forms of Fe and Mn. Two different soil spectral measurement protocols and two data mining techniques were used to examine seventy-eight soil samples from five agricultural areas in different parts of the Czech Republic. Spectral measurements at both laboratories were made using different ASD spectroradiometers. The CULS protocol was based on employing a contact probe (CP) spectral measurement scheme, while the TAU protocol was carried out using a CP measurement method, accompanied with the internal soil standard (ISS) procedure. Two spectral datasets, acquired from different protocols, were both analyzed using partial least square regression (PLSR) technique as well as the PARACUDA II ® , a new data mining engine for optimizing PLSR models. The results showed that spectra based on the CULS setup (non-ISS) demonstrated significantly higher albedo intensity and reflectance values relative to the TAU setup with ISS. However, the majority of statistics using the TAU protocol was not noticeably better than the CULS spectra. The paper also highlighted that under both measurement protocols, the PARACUDA II ® engine proved to be a powerful tool for providing better results than PLSR. Such initiative is not only a way to unlock current limitations of soil spectroscopy, but also offers considerable efficiency and cost-and time-saving possibilities, which lead to further improvements in prediction performance of spectral models.

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“…Users can follow two different procedures: (1) to choose certain bands (e.g. BandA = 560 nm and BandB = 970 nm) that result in a certain single model (on 'analysis' WS); (2) or the other possibility is to involve all bands, and to generate a matrix-like table with all the possible pairs of bands (on 'table' WS), where the bands change column by column (BandA) and row by row (BandB). In this second case an n-element formula results in an n-dimension To avoid the loss of the data gained, a user-defined number (up to 100) of the best values is saved to the fifth ('best') WS from each table together with all the parameters which are needed to reconstruct the models later.…”

Section: Working With Parametric Formulasmentioning

confidence: 99%

“…Beside aerial sensors, there are field equipment and laboratory devices capable of recording the spectral characteristics of given surfaces or substances [1]. Consequently, wider research communities started to use spectrum analysis in different fields, from remote sensing to analytical chemistry or physics [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An interactive tool for semi-automatic feature extraction of hyperspectral data

Kovács

Szabó

2016

Open Geosciences

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Abstract:The spectral reflectance of the surface provides valuable information about the environment, which can be used to identify objects (e.g. land cover classification) or to estimate quantities of substances (e.g. biomass). We aimed to develop an MS Excel add-in -Hyperspectral Data Analyst (HypDA) -for a multipurpose quantitative analysis of spectral data in VBA programming language. HypDA was designed to calculate spectral indices from spectral data with user defined formulas (in all possible combinations involving a maximum of 4 bands) and to find the best correlations between the quantitative attribute data of the same object. Different types of regression models reveal the relationships, and the best results are saved in a worksheet. Qualitative variables can also be involved in the analysis carried out with separability and hypothesis testing; i.e. to find the wavelengths responsible for separating data into predefined groups. HypDA can be used both with hyperspectral imagery and spectrometer measurements. This bivariate approach requires significantly fewer observations than popular multivariate methods; it can therefore be applied to a wide range of research areas.

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Using Imaging Spectroscopy to study soil properties

Cited by 453 publications

References 72 publications

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

An interactive tool for semi-automatic feature extraction of hyperspectral data

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