Indicative capacity of NDVI in predictive mapping of the properties of plow horizons of soils on slopes in the south of Western Siberia

Gopp, N. V.; Nechaeva, T. V.; Savenkov, O. A.; Смирнова, Н. В.; Смирнов, В. А.

doi:10.1134/s1064229317110060

Cited by 13 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gholizadeh, A. et al (2018) showed that GNDVI and SATVI indices provided the strongest correlation with SOC on agricultural plots. Also, several studies conclude that vegetation indices are the most important variables in predicting soil properties (Gopp, N.V. et al 2017;Chen, D. et al 2019;Emadi, M. et al 2020).…”

Section: Remote Sensing Datamentioning

confidence: 99%

Mapping soil organic carbon under erosion processes using remote sensing

et al. 2021

View full text Add to dashboard Cite

This study aimed to map soil organic carbon under erosion processes on an arable field in the Republic of Bashkortostan (Russia). To estimate the spatial distribution of organic carbon in the Haplic Chernozem topsoil, we applied Sentinel-2A satellite data and the linear regression method. We used 13 satellite bands and 15 calculated spectral indices for regression modelling. A regression model with an average prediction level has been created (R2 = 0.58, RMSE = 0.56, RPD = 1.61). Based on the regression model, cartographic materials for organic carbon content have been created. Water flows and erosion processes were determined using the calculated Flow Accumulation model. The relationship between organic carbon, biological activity, and erosion conditions is shown. The 13C-NMR spectroscopy method was used to estimate the content and nature of humic substances of different soil samples. Based on the 213C-NMR analysis, a correlation was established with the spectral reflectivity of eroded and non-eroded soils. It was revealed that the effect of soil organic carbon on spectral reflectivity depends not only on the quantity but also on the quality of humic substances and soil formation conditions.

show abstract

Section: Remote Sensing Datamentioning

confidence: 99%

Mapping soil organic carbon under erosion processes using remote sensing

et al. 2021

View full text Add to dashboard Cite

show abstract

“…It should be noted that the research period and soil conditions vary depending on the natural and geographical location of study area. The authors also note that the soil should be in an air-dry state, the surface roughness should be minimal, and there should be no vegetation on the soil surface (Gopp et al, 2017;Gopp et al, 2019;David, 2013;Wang and Ge, 2012;Wang et al, 2022;Savin et al, 2021). These conditions were met in our study by using NDSI, NDWI, NDVI, NDBI, and BAEI masks and climatic data.…”

Section: Discussionmentioning

confidence: 90%

“…Combined with ground-based measurements, satellite data offers improved accuracy of SOM estimates, providing a comprehensive analysis of soil fertility indicators (Belenok et al, 2021;Huang et al, 2018;Weiss, Jacob and Duveillerc, 2020;Chen et al, 2006;Zhang et al, 2021;Yuzugullu et al, 2020). Satellite imagery, particularly multispectral and hyperspectral images, are essential tools in assessing SOM due to their ability to capture reflectance and absorption properties of different soil constituents (Luo et al, 2023;Gopp et al, 2017;Gopp et al, 2019;Khangura et al, 2023). Organic matter influences reflectance in the visible (VIS) and near-infrared (NIR) regions, making these spectral bands key in SOM evaluation (Reis et al, 2021;Bouasria et al, 2020;Yu et al, 2021;Yuzugullu et al, 2020;Zhang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Application of remote sensing methods for statistical estimation of organic matter in soils

Belenok,

Hebryn-Baidy,

Bіelousova

et al. 2023

Earth sci. res. j.

View full text Add to dashboard Cite

The availability of reliable information on the physicochemical properties of soils is a necessary tool for maintaining and improving fertility and effective optimization of agricultural land management in many countries. However, ground-based research methods require significant financial and time resources. It has been established that methods based on remote sensing data are an efficient, accurate, and less costly solution for studying different types of soil cover parameters. This work aims to determine the predicted indicator of humus content in soils in selected regions of the Kyiv region (Ukraine) with the corresponding soil types. For this, the spectral properties of chernozem soils were investigated based on Landsat 8 OLI satellite images. A mosaic of the mean spectral reflectance values for the study period (2013-2015) was created using the Google Earth Engine. The vegetation indices NDSI, NDWI, NDBI, MSAVI, and NDVI were used to identify bare soils. Using multiple linear regression, an optimal F-Comparing Nested Model was created for predicting humus content in soils, including seven parameters. The model's accuracy was estimated with such indicators R=0.95, R2= 0.90, σy = 0.16 %. The approach based on the proposed model can be used to support the adoption of the necessary management decisions to improve soil fertility and maintain balanced land use.

show abstract

“…There is a discussion about the need for a physical interpretation of statistical dependences obtained using artificial intelligence [97]. Physical interpretation is understood as the presence of regression models linking one or another calculated characteristic of remote sensing data and soil property measured during ground work [98,99]. Indeed, in this way it is possible to establish the parameters of the regression between the NDVI and the properties of the arable horizon (the content of humus, phosphorus, potassium, zinc, etc.).…”

Section: Physical Interpretation Of Work Technologymentioning

confidence: 99%

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

et al. 2021

View full text Add to dashboard Cite

Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps.

show abstract

Indicative capacity of NDVI in predictive mapping of the properties of plow horizons of soils on slopes in the south of Western Siberia

Cited by 13 publications

References 12 publications

Mapping soil organic carbon under erosion processes using remote sensing

Mapping soil organic carbon under erosion processes using remote sensing

Application of remote sensing methods for statistical estimation of organic matter in soils

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

Contact Info

Product

Resources

About