Location of Bare Soil Surface and Soil Line on the RED–NIR Spectral Plane

Королева, П. В.; Рухович, Д. И.; Rukhovich, Alexey; Rukhovich, Danila; Kulyanitsa, A. L.; Trubnikov, A.V.; Калинина, Н. В.; Симакова, М. С.

doi:10.1134/s1064229317100040

Cited by 16 publications

(25 citation statements)

References 9 publications

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“…The resulting cleaned data provided an improved dataset for subsequent cover analysis and monitoring. For example, using the RED-NIR line concept, higher values could represent straw and post-harvest residues [55]. Detected anomalies provided a valuable dataset of reference spectra of various soil covers.…”

Section: Discussionmentioning

confidence: 99%

“…Supporting NDVI, green vegetation indices (GVI) (Equation ( 3)) were used, where values exceeding 0 have been attributed to bare soil pixels [11,12]. The soil-line (Red-NIR line) concept using near infrared and the red bands [14,55] was also used to identify major anomalies in the data, including various types of artificial covers. The Red-NIR line allowed a visual screening of the data, knowing that bare soil pixels were expected to follow a linear trend-line and were of relatively low reflectances [55].…”

Section: Anomaly Detectionmentioning

confidence: 99%

“…The soil-line (Red-NIR line) concept using near infrared and the red bands [14,55] was also used to identify major anomalies in the data, including various types of artificial covers. The Red-NIR line allowed a visual screening of the data, knowing that bare soil pixels were expected to follow a linear trend-line and were of relatively low reflectances [55]. High differences in inter-band reflectance in the visible part of the spectrum could also indicate the presence of anomalies.…”

Section: Anomaly Detectionmentioning

confidence: 99%

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Sentinel-2 Recognition of Uncovered and Plastic Covered Agricultural Soil

Ibrahim

Gobin

2021

Remote Sensing

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Medium resolution satellite data, such as Sentinel-2 of the Copernicus programme, offer great new opportunities for the agricultural sector, and provide insights on soil surface characteristics and their management. Soil monitoring requires a high-quality dataset of uncovered and plastic covered agricultural soil. We developed a methodology to identify uncovered soil pixels in agricultural parcels during seedbed preparation and considered the impacts of clouds and shadows, vegetation cover, and artificial covers, such as those of greenhouses and plastic mulch films. We preserved the spatial and temporal integrity of parcels in the process and analysed spectral anomalies and their sources. The approach is based on freely available tools, namely Google Earth Engine and R Programming packages. We tested the methodology on the northern region of Belgium, which is characterised by small, fragmented parcels. We selected a period between mid-April to end-May, when active agricultural management practices leave the soil bare in preparation for the main cropping season. The spectral angle mapper was used to identify soil covered by non-plastic greenhouses or temporary soil covers, such as plastic mulch films. The effect of underlying soil on temporary covers was considered. The retrogressive plastic greenhouse index was used for detecting plastic greenhouses. The result was a high quality dataset of potential bare uncovered agricultural soil that allows further soil surface characterisation. This offered an improved understanding of the use of artificial covers, their spatial distribution, and their corresponding crops during the considered period. Artificial covers occurred most frequently in maize parcels. The approach resulted in precision values exceeding 0.9 for the detection of temporary covers and non-plastic greenhouses and a sensitivity value exceeding 0.95 for non-plastic and plastic greenhouses.

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Section: Discussionmentioning

confidence: 99%

Section: Anomaly Detectionmentioning

confidence: 99%

Section: Anomaly Detectionmentioning

confidence: 99%

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Sentinel-2 Recognition of Uncovered and Plastic Covered Agricultural Soil

Ibrahim

Gobin

2021

Remote Sensing

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“…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%

“…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]. The SNSL theory assumes the possibility of revealing the spatial heterogeneity of the soil cover on the basis of big satellite data, but without the use of indicator botany.…”

Section: Introductionmentioning

confidence: 99%

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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