Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data

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

doi:10.3390/rs71115561

Cited by 63 publications

(45 citation statements)

References 37 publications

(62 reference statements)

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“…The strength of this relationship is not the same for all soil variables, and also the shape of the spectral features can be very different. For example, the metal-OH bending and O-H stretching related to a clay lattice cause a narrow and shallow absorption feature at 2207 nm [1,2]. Overtones of the O-H and H-O-H stretch vibrations in the free water produce two deep absorption features at 1455 nm and 1915 nm, while organic matter has a strong relationship with electromagnetic radiation in the visible region, due to a wide spectral feature centred around 664 nm related to the chlorophyll pigment [3,4].…”

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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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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“…Surely, these studies have resulted in valuable information on the effect of soil moisture and soil surface roughness on spectroscopy data. For soil moisture it is known that the reflectance decreases with higher soil moisture, the water absorption features at around 1400 and 1900 nm are more affected than the rest of the spectra [20,36,37,40]. For soil surface roughness it is known that for increasing roughness, the reflectance is generally lower, due to self-shadows [19,32,33].…”

Section: Introductionmentioning

confidence: 99%

“…Much research has been done on quantifying and removing these effects from the soil spectra [1,19,20,[29][30][31][32][33][34][35][36][37][38][39][40]. However, most research is focused on the effect on specific soil properties (e.g., [34,36]); focus only on specific spectroscopy techniques (e.g., [35]); or are laboratory based (e.g., [33]). Surely, these studies have resulted in valuable information on the effect of soil moisture and soil surface roughness on spectroscopy data.…”

Section: Introductionmentioning

confidence: 99%

Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping

2016

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An increasing demand for full spatio-temporal coverage of soil information drives the growing use of soil spectroscopy. Soil spectroscopy application performed under laboratory conditions or in-field studies in semi-arid areas have shown promising results. However, when acquiring data in temperate zones, limitations by vegetation-free coverage, variation in soil moisture and management are driving coherent spatio-temporal data collection. This study explores the use of multi-temporal imaging spectroscopy data to increase the total mapping area of bare soils in a heterogeneous agricultural landscape. Spectrally and spatially high-resolution data from the Airborne Prism Experiment (APEX) were collected in September 2013, April 2014 and April 2015. Bare soils in all acquisitions were identified. To eliminate short-term differences in soil moisture and soil surface roughness, the empirical line method was used to calibrate the reflectance values of the singular images (2013 and 2015) towards the singular image with most bare soil pixels (2014). Difference indicators show that the calibration was successful (decrease in root mean square difference and angle difference, increase in R 2 and gain and offset close to one and zero). Finally, the multi-temporal composite image contained more than double the amount of bare soil pixels as compared to a singular acquisition. Summary statistics show that reflectance values of the multi-temporal composite approximate the single image data of 2014 (mean and standard deviation of 2014: 24.2 ± 8.9 vs. 24.0 ± 9.5 for the multi-temporal composite of 2013, 2014 and 2015). This indicates that global differences in soil moisture and land management have been corrected for. As a result, an improved spatial representation of soil parameters can be retrieved from the composite data. Spatial distribution of the correction factors and analysis of the spatial variability of all images, however, indicate that non-linear, short-term differences like variation in soil moisture and land management largely influence the result of the multi-temporal composite. Quantification and attribution of those factors will be required in the future to allow correcting for them.

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“…This is similar for SOM because it is known that soils with more SOM hold more water [70]. The effect of soil moisture can be spectrally corrected but existing methods (e.g., [67,[71][72][73][74][75]) were developed for imaging spectroscopy data and are not easily applicable to multispectral data. Soil surface roughness also affects the reflectance of a bare soil.…”

Section: Differences In Soil Moisturementioning

confidence: 99%

“…The main effect is the increasing reflectance with decreasing soil moisture. Some spectral features are more affected [6], especially the water absorption features around 1400 and 1900 nm and the SWIR range [66][67][68]. As a result, soil moisture tends to mask the effect of other soil properties (e.g., organic matter and iron oxides) [69].…”

Section: Differences In Soil Moisturementioning

confidence: 99%

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

et al. 2017

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Many soil remote sensing applications rely on narrow-band observations to exploit molecular absorption features. However, broadband sensors are invaluable for soil surveying, agriculture, land management and mineral exploration, amongst others. These sensors provide denser time series compared to high-resolution airborne imaging spectrometers and hold the potential of increasing the observable bare-soil area at the cost of spectral detail. The wealth of data coming along with these applications can be handled using cloud-based processing platforms such as Earth Engine. We present a method for identifying the least-vegetated observation, or so called barest pixel, in a dense time series between January 1985 and March 2017, based on Landsat 5, 7 and 8 observations. We derived a Barest Pixel Composite and Bare Soil Composite for the agricultural area of the Swiss Plateau. We analysed the available data over time and concluded that about five years of Landsat data were needed for a full-coverage composite (90% of the maximum bare soil area). Using the Swiss harmonised soil data, we derived soil properties (sand, silt, clay, and soil organic matter percentages) and discuss the contribution of these soil property maps to existing conventional and digital soil maps. Both products demonstrate the substantial potential of Landsat time series for digital soil mapping, as well as for land management applications and policy making.

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Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data

Cited by 63 publications

References 37 publications

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

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

Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping

Barest Pixel Composite for Agricultural Areas Using Landsat Time Series

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