Maps of Stable Intra-Field Heterogeneity Based on Big Satellite Data in the Precision Farming System

Шаповалов, Д. А.

doi:10.5593/sgem2019/2.2/s11.111

Cited by 3 publications

(5 citation statements)

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“…The threshold value of one-third of the field area for low NDVI values was derived empirically from the experience of creating of task maps for precision farming based on the analysis of big data [30,52]. During the study, the fields were divided into 15, 9, 6, 3 and 2 zones of soil fertility equal in area.…”

Section: Calculation Of Zones Of Low Ndvi Values (Zones Of Reduced Fertility)mentioning

confidence: 99%

“…During the study, the fields were divided into 15, 9, 6, 3 and 2 zones of soil fertility equal in area. These were zones of the average long-term state of agricultural vegetation or the average long-term values of the vegetation indices [52]. To calculate task maps for precision farming, for most agricultural fields (80-90%), it turned out to be necessary and sufficient to divide them into three zones of fertility equal As a loss function we use binary cross entropy, which in our case is equal to the equation above.…”

Section: Calculation Of Zones Of Low Ndvi Values (Zones Of Reduced Fertility)mentioning

confidence: 99%

“…To calculate task maps for precision farming, for most agricultural fields (80-90%), it turned out to be necessary and sufficient to divide them into three zones of fertility equal in area. The work was conducted on the territory of Rostov [52], Lipetsk [80], Tambov [14] and Krasnodar regions [30].…”

Section: Calculation Of Zones Of Low Ndvi Values (Zones Of Reduced Fertility)mentioning

confidence: 99%

“…2021, 13, x FOR PEER REVIEW 14 of 29 in area. The work was conducted on the territory of Rostov [52], Lipetsk [80], Tambov [14] and Krasnodar regions [30].…”

Section: Calculation Of the Zones Of Low Values Of Ndvi (Zones Of Reduced Fertility) Based On Data Selected By Machine Learningmentioning

confidence: 99%

“…As a rule, these are areas of low productivity [47,50,51]. Areas of low productivity (low values of vegetation indices) can be fixed in the same places for a long period of time [51,52]. In this case, these sites can be indicators of the occurrence of soil degradation.…”

Section: Introductionmentioning

confidence: 99%

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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

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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.

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Section: Calculation Of Zones Of Low Ndvi Values (Zones Of Reduced Fertility)mentioning

confidence: 99%

Section: Calculation Of Zones Of Low Ndvi Values (Zones Of Reduced Fertility)mentioning

confidence: 99%

Section: Calculation Of Zones Of Low Ndvi Values (Zones Of Reduced Fertility)mentioning

confidence: 99%

“…2021, 13, x FOR PEER REVIEW 14 of 29 in area. The work was conducted on the territory of Rostov [52], Lipetsk [80], Tambov [14] and Krasnodar regions [30].…”

Section: Calculation Of the Zones Of Low Values Of Ndvi (Zones Of Reduced Fertility) Based On Data Selected By Machine Learningmentioning

confidence: 99%