Analysis of the Informativity of Big Satellite Precision-Farming Data Processing for Correcting Large-Scale Soil Maps

Kulyanitsa, A. L.; Рухович, Д. И.; Королева, П. В.; Vilchevskaya, Ye. V.; Калинина, Н. В.

doi:10.1134/s1064229320110083

Cited by 4 publications

(9 citation statements)

References 33 publications

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“…[21][22][23][24]. The main disadvantage of modeling is the allocation of potential rather than actual erosion, which do not always coincide [8].…”

Section: Sources and Methods For Detecting Soil Degradationmentioning

confidence: 99%

“…Often, instead of soil cover mapping, various methods of indicative botany were used in the form of analyses of vegetation indices [2][3][4][5][6][7]. An alternative to vegetation indices is the method of constructing soil maps based on the analysis of the bare soil surface [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…To calculate the relief characteristics, digital elevation models or digital terrain models are required [25,26]. Soil degradation maps obtained by modeling require groundwork, because even with the same relief characteristics (obtained from the DEM), degradation may or may not occur [8].…”

Section: Introductionmentioning

confidence: 99%

“…Working with big data is, first of all, Data mining and MapReduce [8]. When working with big RSD, it is necessary to select RSD suitable for calculations and analysis and reject unsuitable ones.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data

et al. 2022

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The detection of degraded soil distribution areas is an urgent task. It is difficult and very time consuming to solve this problem using ground methods. The modeling of degradation processes based on digital elevation models makes it possible to construct maps of potential degradation, which may differ from the actual spatial distribution of degradation. The use of remote sensing data (RSD) for soil degradation detection is very widespread. Most often, vegetation indices (indicative botany) have been used for this purpose. In this paper, we propose a method for constructing soil maps based on a multi-temporal analysis of the bare soil surface (BSS). It is an alternative method to the use of vegetation indices. The detection of the bare soil surface was carried out using the spectral neighborhood of the soil line (SNSL) technology. For the automatic recognition of BSS on each RSD image, computer vision based on deep machine learning (neural networks) was used. A dataset of 244 BSS distribution masks on 244 Landsat 4, 5, 7, and 8 scenes over 37 years was developed. Half of the dataset was used as a training sample (Landsat path/row 173/028). The other half was used as a test sample (Landsat path/row 174/027). Binary masks were sufficient for recognition. For each RSD pixel, value “1” was set when determining the BSS. In the absence of BSS, value “0” was set. The accuracy of the machine prediction of the presence of BSS was 75%. The detection of degradation was based on the average long-term spectral characteristics of the RED and NIR bands. The coefficient Cmean, which is the distance of the point with the average long-term values of RED and NIR from the origin of the spectral plane RED/NIR, was calculated as an integral characteristic of the mean long-term values. Higher long-term average values of spectral brightness served as indicators of the spread of soil degradation. To test the method of constructing soil degradation maps based on deep machine learning, an acceptance sample of 133 Landsat scenes of path/row 173/026 was used. On the territory of the acceptance sample, ground verifications of the maps of the coefficient Cmean were carried out. Ground verification showed that the values of this coefficient make it possible to estimate the content of organic matter in the plow horizon (R2 = 0.841) and the thickness of the humus horizon (R2 = 0.8599). In total, 80 soil pits were analyzed on an area of 649 ha on eight agricultural fields. Type I error (false positive) of degradation detection was 17.5%, and type II error (false negative) was 2.5%. During the determination of the presence of degradation by ground methods, 90% of the ground data coincided with the detection of degradation from RSD. Thus, the quality of machine learning for BSS recognition is sufficient for the construction of soil degradation maps. The SNSL technology allows us to create maps of soil degradation based on the long-term average spectral characteristics of the BSS.

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“…[21][22][23][24]. The main disadvantage of modeling is the allocation of potential rather than actual erosion, which do not always coincide [8].…”

Section: Sources and Methods For Detecting Soil Degradationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data

et al. 2022

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“…Monitoring of the soil cover in Russia on a large scale was carried out by two organizations-the agrochemical service [10] and GIPROZEM (State Design Institute of Land Management), which was disbanded in the 1990s during the collapse of the USSR [11]. Large-scale soil maps were compiled by GIPROZEM according to a single instruction for all agricultural lands of the USSR [4].…”

Section: Introductionmentioning

confidence: 99%

Updating of the Archival Large-Scale Soil Map Based on the Multitemporal Spectral Characteristics of the Bare Soil Surface Landsat Scenes

Rukhovich,

Koroleva,

Rukhovich

et al. 2023

Remote Sensing

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For most of the arable land in Russia (132–137 million ha), the dominant and accurate soil information is stored in the form of map archives on paper without coordinate reference. The last traditional soil map(s) (TSM, TSMs) were created over 30 years ago. Traditional and/or archival soil map(s) (ASM, ASMs) are outdated in terms of storage formats, dates, and methods of production. The technology of constructing a multitemporal soil line (MSL) makes it possible to update ASMs and TSMs based on the processing of big remote-sensing data (RSD). To construct an MSL, the spectral characteristics of the bare soil surface (BSS) are used. The BSS on RSD is distinguished within the framework of the conceptual apparatus of the spectral neighborhood of the soil line. The filtering of big RSD is based on deep machine learning. In the course of the work, a vector georeferenced version of the ASM and an updated soil map were created based on the coefficient “C” of the MSL. The maps were verified based on field surveys (76 soil pits). The updated map is called the map of soil interpretation of the coefficient “C” (SIC “C”). The SIC “C” map has a more detailed legend compared to the ASM (7 sections/chapters instead of 5), greater accuracy (smaller errors of the first and second kind), and potential suitability for calculating soil organic matter/carbon (SOM/SOC) reserves (soil types/areals in the SIC “C” map are statistically significant are divided according to the thickness of the organomineral horizon and the content of SOM in the plowed layer). When updating, a systematic underestimation of the numbers of contours and areas of soils with manifestations of negative/degradation soil processes (slitization and erosion) on the TSM was established. In the process of updating, all three shortcomings of the ASMs/TSMs (archaic storage, dates, and methods of creation) were eliminated. The SIC “C” map is digital (thematic raster), modern, and created based on big data processing methods. For the first time, the actualization of the soil map was carried out based on the MSL characteristics (coefficient “C”).

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Digital Technologies Determination Effectiveness for the Productivity of Organic Winter Wheat Production in Low Soil Performance Indicator

Astrauskas

Staugaitis

2022

Agriculture

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The most important aspect of precision farming is the prediction of crop yield and quality. Digital technologies (soil maps and combine harvester with telemetry functions) were used to determinate the yield of organically grown winter wheat (variety Skagen) in two fields of 18.8 and 4.5 ha in Lithuanian regional conditions, in an area classified as low-performance for farming. The objective of the research was to determine the effectiveness of digital technologies (soil maps and combine harvester with telemetry functions) in assessment of the dynamics of soil pH, P2O5, and K2O, humus and organic winter wheat (variety Skagen) productivity, and grain crude-protein dependence in low-performance soils. Haplic Luvisol soils predominated, while Eutric Gleysols, Haplic Arenosols, and Eutric Planosols soils intervened in smaller areas, and the granulometric composition of the soil in the arable layer and the subsoil varied from sand to sandy loam, loam, and silt loam. In the sandy areas of Haplic Arenosols and in the lower parts of the field, where Eutric Gleysols, intervened in predominant Haplic Luvisols soils, winter wheat crude protein content and grain yield were lower. The biggest grain yield of 6.95 t ha−1 was obtained in Haplic Luvisols soils. Crude protein of winter wheat grains varied from 9.70 to 13.34%. Although both technologies reflected the non-uniform yields of the fields and correlation between them well, the information on the soil cover of the field better explained the reasons for lower yields. In the case of this research, sand inclusions and lower areas in winter wheat fields, causing plants to soak during winter, were identified. The combination of two digital technologies (soil maps and combine harvester with telemetry functions) made it possible to determine yields accurately, and quickly. Moreover, there is a need, in the future, to evaluate the reasons for yield variation and address changes in yields due to the improvement of certain low-performance soil areas. The complex use of these technologies can be beneficial in terms of labour and economy. However, the accurate benefit of labour time and economic should be investigated.

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Analysis of the Informativity of Big Satellite Precision-Farming Data Processing for Correcting Large-Scale Soil Maps

Cited by 4 publications

References 33 publications

Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data

Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data

Updating of the Archival Large-Scale Soil Map Based on the Multitemporal Spectral Characteristics of the Bare Soil Surface Landsat Scenes

Digital Technologies Determination Effectiveness for the Productivity of Organic Winter Wheat Production in Low Soil Performance Indicator

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