A study of the responsiveness of crops to fertilizers by zones of stable intra-field heterogeneity based on big satellite data analysis

Хитров, Н. Б.; Рухович, Д. И.; Королева, П. В.; Калинина, Н. В.; Trubnikov, A.V.; Petukhov, D.A.; Kulyanitsa, A. L.

doi:10.1080/03650340.2019.1703957

Cited by 10 publications

(18 citation statements)

References 23 publications

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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%

“…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%

“…Some works use big time series [27]. It can be assumed that, based on the analysis of vegetation indices in the processing mode of big satellite data [28][29][30], zones of soil degradation can be distinguished.…”

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%

“…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%

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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“…Available multi-layered studies associate vegetation indexes from remote sensing to nitrogen fertilization [8], to the content [9] or to the supply of N, P, and K [10], to soil texture [11], or soil type [12]. Less frequent (or absent) is the association between remote data and specific soil chemical and physical features.…”

Section: Introductionmentioning

confidence: 99%

Spatial Variations of Vegetation Index from Remote Sensing Linked to Soil Colloidal Status

Bascietto

Santangelo

Beni

2021

Land

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Recent decades have seen a progressive degradation of soils owing to an intensification of farming practices (weeding and high trafficking), increasing use of pesticides and fertilizers, mainly nitrogen, resulting in a steady decline in soil organic matter, a key component to maintain soil fertility. The work has coupled the normalized difference vegetation index (NDVI) of wheat cultivation in Central Italy to soil properties where the wheat was grown to identify the properties linked to within-field variability in productivity. NDVI was assessed through Copernicus Sentinel-2 (S-2) data during the wheat anthesis phase. The main outcome showed a significant correlation of NDVI variability to soil colloidal status and to the relative quantity in the exchange complex of the Ca2+ ions. No relationship emerged between NDVI and soil macronutrients (nitrogen, phosphorus, and potassium) concentration. The work suggested that such elements (nitrogen, especially) should not be provided solely considering the vegetation index spatial variations. Rational and sustainable management of soil fertility requires the integration of the NDVI data with the whole complex of soil physical/chemical status. In this way, the identification of the real key factors of fertility will avoid the negative impact of overfertilization. As an example, a fertilization plan was simulated for the sunflower–wheat sequence. The results showed that in the study area additional supplies of N and K would be unnecessary.

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Implications of intra-plot heterogeneity for yield estimation accuracy: Evidence from smallholder maize systems in Ethiopia

Sida

Chamberlin

Ayalew

et al. 2021

Field Crops Research

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Highlights We examine inter- and intra-plot heterogeneity of productivity in maize plots. We find high inter- and intra-plot variabilities in plant population and yield. Intra-plot heterogeneity overrides inter-plot heterogeneity in smallholder maize. The magnitude and distribution of yield estimation errors are method-dependent. More intensively managed plots show lower intra-plot heterogeneity and lower yield.

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A study of the responsiveness of crops to fertilizers by zones of stable intra-field heterogeneity based on big satellite data analysis

Cited by 10 publications

References 23 publications

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

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

Spatial Variations of Vegetation Index from Remote Sensing Linked to Soil Colloidal Status

Implications of intra-plot heterogeneity for yield estimation accuracy: Evidence from smallholder maize systems in Ethiopia

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