Soil total carbon mapping, in Djerid Arid area, using ASTER multispectral remote sensing data combined with laboratory spectral proximal sensing data

Aïchi, Hamouda; Fouad, Youssef; Lili-Chabaane, Zohra; Sanaa, Mustapha; Walter, Christian

doi:10.1007/s12517-021-06698-z

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…The primary constraint for applying mixed models is the presence of a spatial structure of the model residuals. Indeed, Aïchi et al [142], in their attempt to spatialize the soil total carbon (STC) content of their arid study site, showed that the PLS model residuals had a spatial structure and that the approach relying on the RK hybrid method led to more accurate STC content mapping using ASTER satellite images. In contrast, Castaldi et al [139] did not run the LMEM for SOC estimation due to a lack of a spatial structure of the ordinary least square residuals using Hyperion hyperspectral data.…”

Section: Mixed Models Incorporating Spectral Information Into Spatial...mentioning

confidence: 99%

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

et al. 2022

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There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km2: dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of ~15 g·kg−1 and a range of 30 g·kg−1 in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information.

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Section: Mixed Models Incorporating Spectral Information Into Spatial...mentioning

confidence: 99%

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

et al. 2022

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“…In the case of a manuscript that included more than one author from the same organization, each institution contributed only once to the final map (Figure 8). A first glance, the map indicates that out of the 20 contributing countries, the major contributions came from Asian countries (39.5%), while only a single study originated from North Africa [77], whereas no contributions originated from countries in Oceania. The aforementioned result is attributed mainly to the considerable contribution of Chinese (22.4%) as well as Indonesian institutions (3.9%).…”

Section: The Impact Of Main Initiatives and Projectsmentioning

confidence: 99%

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

et al. 2021

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We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.

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“…To increase the accuracy of models developed using remote sensing data, in particular multispectral satellite and/or airborne imagery, three main approaches have been developed: (1) Inclusion of spectral indices to consider effects of interference factors [30,33,[36][37][38], especially for several dates in a time series [30,38]; (2) Combination of remotely sensed data with laboratory or field spectra to fuse spectral and spatial soil information [18,35,[39][40][41][42];…”

Section: Introductionmentioning

confidence: 99%

Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets

Zayani,

Fouad,

Michot

et al. 2023

Remote Sensing

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Understanding spatial and temporal variability in soil organic carbon (SOC) content helps simultaneously assess soil fertility and several parameters that are strongly associated with it, such as structural stability, nutrient cycling, biological activity, and soil aeration. Therefore, it appears necessary to monitor SOC regularly and investigate rapid, non-destructive, and cost-effective approaches for doing so, such as proximal and remote sensing. To increase the accuracy of predictions of SOC content, this study evaluated combining remote sensing time series with laboratory spectral measurements using machine and deep-learning algorithms. Partial least squares (PLS) regression, random forest (RF), and deep neural network (DNN) models were developed using Sentinel-2 (S2) time series of 58 sampling points of bare soil and according to three approaches. In the first approach, only S2 bands were used to calibrate and compare the performance of the models. In the second, S2 indices, Sentinel-1 (S1) indices, and S1 soil moisture were added separately during model calibration to evaluate their effects individually and then together. In the third, we added the laboratory indices incrementally and tested their influence on model accuracy. Using only S2 bands, the DNN model outperformed the PLS and RF models (ratio of performance to the interquartile distance RPIQ = 0.79, 1.36 and 1.67, respectively). Additional information improved performances only for model calibration, with S1 soil moisture yielding the most stable improvement among three iterations. Including equivalent indices of the S2 indices calculated using soil spectra obtained under laboratory conditions improved prediction of SOC, and the use of only two indices achieved good validation performances for the RF and DNN models (mean RPIQ = 2.01 and 1.77, respectively).

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Soil total carbon mapping, in Djerid Arid area, using ASTER multispectral remote sensing data combined with laboratory spectral proximal sensing data

Cited by 5 publications

References 26 publications

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets

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