Detecting Degraded Arable Land on the Basis of Remote Sensing Big Data Analysis

Рухович, Д. И.; Королева, П. В.; Калинина, Н. В.; Vilchevskaya, Ye. V.; Сулейман, Г. А.; Черноусенко, Г. И.

doi:10.1134/s1064229321020137

Cited by 5 publications

(12 citation statements)

References 30 publications

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“…Vegetation indices make it possible to identify zones of soil degradation [36,37,41]. The main disadvantage of mapping intra-field heterogeneity by vegetation indices is the lack of information about the reasons for the formation of heterogeneity [9]. Even the analysis of multi-temporal RSD series [37] will reveal only the presence of vegetation suppression.…”

Section: Sources and Methods For Detecting Soil Degradationmentioning

confidence: 99%

“…Remote sensing data of different spatial resolution were analyzed: high spatial resolution (IKONOS, GeoEye-1, WorldView, etc.) [72], medium spatial resolution (Landsat, Sentinel) [43], and archival data of 1968 and 1975 (CORONA) [73]. The quality and accuracy of interpretation are checked using topographic maps at a scale of 1:25,000 (Figure 2c).…”

Section: Creating a Map Of Arable Lands Boundariesmentioning

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%

“…Both methods are, in one way or another, methods of indicator botany. The work of 2021 [9] outlines the method of averaging vegetation indices adjusted for the methods of analysis of the spectral neighborhood of the soil line (SNSL). The SNSL theory itself is presented in a series of works of 2016-2018 [10][11][12][13][14].…”

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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Section: Sources and Methods For Detecting Soil Degradationmentioning

confidence: 99%

Section: Creating a Map Of Arable Lands Boundariesmentioning

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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“…More specifically, EO data and services are restricted not only to supporting the informed implementation of numerous soil-related policies but also to protecting soil by encouraging farmers to take extra steps to improve soil management practices. Subsequently, EO data and services can further contribute to proposing and designing management practices for improving the status of agricultural soils and stopping land degradation through the application of variable rate fertilization [14,15].…”

Section: Understanding the Governance Framework To Implement And Monitor Soil-related Policiesmentioning

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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