The role of positional errors while interpolating soil organic carbon contents using satellite imagery

Самсонова, В. П.; Meshalkina, Joulia; Blagoveschensky, Yu.N.; Yaroslavtsev, Alexis; Stoorvogel, J.J.

doi:10.1007/s11119-018-9575-4

Cited by 16 publications

(8 citation statements)

References 11 publications

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“…The constructed model was tested on the experimental field of «Bashmakovskiy khleb» JSC in 2020. Practical verification has shown the possibility of using the model in agricultural production under normal climatic conditions and its high correlation with the actual results obtained [1]. Statistical analysis of the calculated data of the model and the actual yield with the achieved economic indicators in the conditions of the model field showed a level of reliability of calculations of 95%.…”

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confidence: 74%

Agroecological modeling of spring barley cultivation technology in the conditions of the Penza region

2021

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The article presents the results of modeling the cultivation of barley of the Eifel variety on leached chernozems of the Bashmakovsky district of the Penza region. In order to carry out modeling, the framework Local information and reference system for the optimization of land use was deeply adapted. The adaptation of the software modules to the climatic and soil conditions of a particular study area allowed one to reach 7% errors in modeling the cultivation of agricultural crops in the presence of a set of indicators necessary for constructing a model. Technological calculations of the model made it possible to reduce the number of minimum required technological operations, as well as to rationally distribute the application of mineral fertilizers for the planned yield. The economic calculations of the model made it possible to achieve a high production profitability of 66 ± 7%. The constructed model was tested on the experimental field of «Bashmakovskiy khleb» JSC in 2020. Practical verification has shown the possibility of using the model in agricultural production under normal climatic conditions and its high correlation with the actual results obtained [1]. Statistical analysis of the calculated data of the model and the actual yield with the achieved economic indicators in the conditions of the model field showed a level of reliability of calculations of 95%.

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confidence: 74%

Agroecological modeling of spring barley cultivation technology in the conditions of the Penza region

2021

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“…Some recent Chinese studies used the hyperspectral data of the Gaofen-5 satellite with a 30 m resolution and bandwidth of 60 km [40][41][42]. In parallel, with the emerging of precision agriculture, field-scale approaches to SOC modeling have also been developed from satellite sensors with higher spatial resolution: IKONOS with 4 m resolution [43], PlanetScope with 3 m resolution [44] and Worldview 2 with 2.5 m resolution [45,46].…”

Section: Satellites Spectral Informationmentioning

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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“…Three-dimensional modelling of data-uncertainty percentages was done using the ordinary kriging method. Although kriging is a method designed to interpolate measurements of natural phenomena, modelling has been applied successfully to datasets with non-natural parameters such as uncertainty (Silva and Costa 2016;Samsonova et al 2018). As such, the TILES subsurface model now includes not only the lithoclass probabilities (for clay, silt, fine-medium-coarse sand and gravel) and the modelling-related uncertainty (entropy), but also the series of data uncertainties (for positioning, sampling and vintage).…”

Section: Incorporating Uncertainty Percentages In 3d Geological Modelsmentioning

confidence: 99%

Uncertainty assessment applied to marine subsurface datasets

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Hademenos

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et al. 2020

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A recently released voxel model quantifying aggregate resources of the Belgian part of the North Sea includes lithological properties of all Quaternary sediments and modelling-related uncertainty. As the underlying borehole data come from various sources and cover a long time span, data-related uncertainties should be accounted for as well. Applying a tiered data-uncertainty assessment to a composite lithology dataset with uniform, standardised lithological descriptions and rigorously completed metadata fields, uncertainties were qualified and quantified for positioning, sampling and vintage. The uncertainty on horizontal positioning combines navigational errors, on-board and off-deck offsets, and underwater drift. Sampling-gear uncertainty evaluates the suitability of each instrument in terms of its efficiency of sediment yield per lithological class. Vintage uncertainty provides a likelihood of temporal change since the moment of sampling, using the mobility of fine-scale bedforms as an indicator. For each uncertainty component, quality flags from 1 (very uncertain) to 5 (very certain) were defined, and converted into corresponding uncertainty percentages meeting the input requirements of the voxel model. Obviously, an uncertainty-based data selection procedure, aimed at improving the confidence of data products, reduces data density. Whether or not this density reduction is detrimental to the spatial coverage of data products, will depend on their intended use. At the very least, demonstrable reductions in spatial coverage will help to highlight the need for future data acquisition and to optimise survey plans. By opening up our subsurface model with associated data uncertainties in a public decision support application, policy makers and other end users are better able to visualise overall confidence and identify areas with insufficient coverage meeting their needs. Having to work with a borehole dataset that is increasingly limited with depth below the seabed, engineering geologists and geospatial analysts in particular will profit from a better visualisation of data-related uncertainty.Thematic collection: This article is part of the Mapping the Geology and Topography of the European Seas (EMODnet) collection available at: https://www.lyellcollection.org/cc/EMODnet

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The role of positional errors while interpolating soil organic carbon contents using satellite imagery

Cited by 16 publications

References 11 publications

Agroecological modeling of spring barley cultivation technology in the conditions of the Penza region

Agroecological modeling of spring barley cultivation technology in the conditions of the Penza region

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

Uncertainty assessment applied to marine subsurface datasets

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