Mapping of Agate-like Soil Cover Structures Based on a Multitemporal Soil Line Using Neural Network Filtering of Remote Sensing Data
The present study focuses on analysis of the soil cover structure (SCS, SCSs), which is the most detailed level of soil organization in space. The detail in which complex SCS can be studied is often insufficient, since until now it has not been possible to map it over large areas at scales larger th...
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2025-01-01
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| author | Dmitry I. Rukhovich Polina V. Koroleva Alexey D. Rukhovich Mikhail A. Komissarov |
| author_facet | Dmitry I. Rukhovich Polina V. Koroleva Alexey D. Rukhovich Mikhail A. Komissarov |
| author_sort | Dmitry I. Rukhovich |
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| description | The present study focuses on analysis of the soil cover structure (SCS, SCSs), which is the most detailed level of soil organization in space. The detail in which complex SCS can be studied is often insufficient, since until now it has not been possible to map it over large areas at scales larger than 1:10,000. To increase the detail in which SCS can be studied, the methods of identifying the bare soil surface (BSS) and averaging its multitemporal spectral characteristics were used, which opens up new possibilities for mapping complex SCS over large areas. New SCSs of leached chernozems (Luvic Chernic Phaeozem) were discovered, which can produce patterns on satellite images similar to sections of Timan agate—agate-like soil cover structures (ASCS, ASCSs). ASCSs are formed on Quaternary sediments of varying thickness from 0.3 to 6 m, underlain by carbonate and red sediments of the Permian period. The ASCS pattern is formed by ring-shaped stripes (rings) of different colors and brightness, which are determined by the carbonate and red-colored inclusions involved in the arable horizon. Eight soil varieties were identified to describe ASCSs during the study. According to the WRB, there are six main soil types, and according to the classification of Russian soils in 1977, there are four types. ASCSs were identified over large areas and soil maps of ASCSs were constructed using multitemporal spectral characteristics of the BSS in the form of multitemporal soil line coefficients. Neural networks were used to identify BSS on big remote sensing data. ASCSs have contrasting soil properties and contrasting fertility (productivity of agricultural crops). ASCS maps can serve as the basis for task maps of precision farming systems. Perhaps ASCSs are unique objects for the area of chernozem distribution, where in one soil profile there are rocks with an age from the first thousand years (Quaternary) to 250 million years (Permian). Chernozems are fertile, studied, mercilessly exploited, but sometimes they are simply beautiful—agate-like. |
| format | Article |
| id | doaj-art-bd9dc5ba2c6145c88e44f4154952626b |
| institution | Kabale University |
| issn | 2076-3263 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geosciences |
| spelling | doaj-art-bd9dc5ba2c6145c88e44f4154952626b2025-01-24T13:34:15ZengMDPI AGGeosciences2076-32632025-01-011513210.3390/geosciences15010032Mapping of Agate-like Soil Cover Structures Based on a Multitemporal Soil Line Using Neural Network Filtering of Remote Sensing DataDmitry I. Rukhovich0Polina V. Koroleva1Alexey D. Rukhovich2Mikhail A. Komissarov3V.V. Dokuchaev Soil Science Institute, Pyzhevskiy Pereulok 7, 119017 Moscow, RussiaV.V. Dokuchaev Soil Science Institute, Pyzhevskiy Pereulok 7, 119017 Moscow, RussiaV.V. Dokuchaev Soil Science Institute, Pyzhevskiy Pereulok 7, 119017 Moscow, RussiaUfa Institute of Biology UFRC RAS, Pr. Oktyabrya 69, 450054 Ufa, RussiaThe present study focuses on analysis of the soil cover structure (SCS, SCSs), which is the most detailed level of soil organization in space. The detail in which complex SCS can be studied is often insufficient, since until now it has not been possible to map it over large areas at scales larger than 1:10,000. To increase the detail in which SCS can be studied, the methods of identifying the bare soil surface (BSS) and averaging its multitemporal spectral characteristics were used, which opens up new possibilities for mapping complex SCS over large areas. New SCSs of leached chernozems (Luvic Chernic Phaeozem) were discovered, which can produce patterns on satellite images similar to sections of Timan agate—agate-like soil cover structures (ASCS, ASCSs). ASCSs are formed on Quaternary sediments of varying thickness from 0.3 to 6 m, underlain by carbonate and red sediments of the Permian period. The ASCS pattern is formed by ring-shaped stripes (rings) of different colors and brightness, which are determined by the carbonate and red-colored inclusions involved in the arable horizon. Eight soil varieties were identified to describe ASCSs during the study. According to the WRB, there are six main soil types, and according to the classification of Russian soils in 1977, there are four types. ASCSs were identified over large areas and soil maps of ASCSs were constructed using multitemporal spectral characteristics of the BSS in the form of multitemporal soil line coefficients. Neural networks were used to identify BSS on big remote sensing data. ASCSs have contrasting soil properties and contrasting fertility (productivity of agricultural crops). ASCS maps can serve as the basis for task maps of precision farming systems. Perhaps ASCSs are unique objects for the area of chernozem distribution, where in one soil profile there are rocks with an age from the first thousand years (Quaternary) to 250 million years (Permian). Chernozems are fertile, studied, mercilessly exploited, but sometimes they are simply beautiful—agate-like.https://www.mdpi.com/2076-3263/15/1/32chernozem diversity and productivitymachine learningmultitemporal soil lineremote sensing datasoil cover structure |
| spellingShingle | Dmitry I. Rukhovich Polina V. Koroleva Alexey D. Rukhovich Mikhail A. Komissarov Mapping of Agate-like Soil Cover Structures Based on a Multitemporal Soil Line Using Neural Network Filtering of Remote Sensing Data Geosciences chernozem diversity and productivity machine learning multitemporal soil line remote sensing data soil cover structure |
| title | Mapping of Agate-like Soil Cover Structures Based on a Multitemporal Soil Line Using Neural Network Filtering of Remote Sensing Data |
| title_full | Mapping of Agate-like Soil Cover Structures Based on a Multitemporal Soil Line Using Neural Network Filtering of Remote Sensing Data |
| title_fullStr | Mapping of Agate-like Soil Cover Structures Based on a Multitemporal Soil Line Using Neural Network Filtering of Remote Sensing Data |
| title_full_unstemmed | Mapping of Agate-like Soil Cover Structures Based on a Multitemporal Soil Line Using Neural Network Filtering of Remote Sensing Data |
| title_short | Mapping of Agate-like Soil Cover Structures Based on a Multitemporal Soil Line Using Neural Network Filtering of Remote Sensing Data |
| title_sort | mapping of agate like soil cover structures based on a multitemporal soil line using neural network filtering of remote sensing data |
| topic | chernozem diversity and productivity machine learning multitemporal soil line remote sensing data soil cover structure |
| url | https://www.mdpi.com/2076-3263/15/1/32 |
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