Relationships between the NDVI, Yield of Spring Wheat, and Properties of the Plow Horizon of Eluviated Clay-Illuvial Chernozems and Dark Gray Soils

Gopp, N. V.; Savenkov, O. A.

doi:10.1134/s1064229319030050

Cited by 10 publications

(2 citation statements)

References 9 publications

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“…There is a discussion about the need for a physical interpretation of statistical dependences obtained using artificial intelligence [97]. Physical interpretation is understood as the presence of regression models linking one or another calculated characteristic of remote sensing data and soil property measured during ground work [98,99]. Indeed, in this way it is possible to establish the parameters of the regression between the NDVI and the properties of the arable horizon (the content of humus, phosphorus, potassium, zinc, etc.).…”

Section: Physical Interpretation Of Work Technologymentioning

confidence: 99%

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: Physical Interpretation Of Work Technologymentioning

confidence: 99%

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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“…Remote sensing, either through satellite imagery or low-altitude drones (i.e., unoccupied aerial vehicles (UAVs)), has already been widely employed in different agricultural applications [11]. Low-cost commercial drones are usually used to collect spectral data to estimate different crop parameters, such as biomass [12,13] and nitrogen content [14,15], in order to predict crop yields [16][17][18] and identify weeds [19,20] or water stress in crops [21]. The popularity of drones is due to their flexibility and cost-efficiency in farm-scale surveys in comparison to satellite or terrestrial approaches [22].…”

Section: Introductionmentioning

confidence: 99%

Can Basic Soil Quality Indicators and Topography Explain the Spatial Variability in Agricultural Fields Observed from Drone Orthomosaics?

et al. 2023

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Crop growth is often uneven within an agricultural parcel, even if it has been managed evenly. Aerial images are often used to determine the presence of vegetation and its spatial variability in field parcels. However, the reasons for this uneven growth have been less studied, and they might be connected to variations in topography, as well as soil properties and quality. In this study, we evaluated the relationship between drone image data and field and soil quality indicators. In total, 27 multispectral and RGB drone image datasets were collected from four real farm fields in 2016–2020. We analyzed 13 basic soil quality indicators, including penetrometer resistance in top- and subsoil, soil texture (clay, silt, fine sand, and sand content), soil organic carbon (SOC) content, clay/SOC ratio, and soil quality assessment parameters (topsoil biological indicators, subsoil macroporosity, compacted layers in the soil profile, topsoil structure, and subsoil structure). Furthermore, a topography variable describing water flow was used as an indicator. Firstly, we evaluated single pixel-wise linear correlations between the drone datasets and soil/field-related parameters. Correlations varied between datasets and, in the best case, were 0.8. Next, we trained and tested multiparameter non-linear models (random forest algorithm) using all 14 soil-related parameters as features to explain the multispectral (NIR band) and RGB (green band) reflectance values of each drone dataset. The results showed that the soil/field indicators could effectively explain the spatial variability in the drone images in most cases (R2 > 0.5), especially for annual crops, and in the best case, the R2 value was 0.95. The most important field/soil features for explaining the variability in drone images varied between fields and imaging times. However, it was found that basic soil quality indicators and topography variables could explain the variability observed in the drone orthomosaics in certain conditions. This knowledge about soil quality indicators causing within-field variation could be utilized when planning cultivation operations or evaluating the value of a field parcel.

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Spatial prediction of soil micronutrients using machine learning algorithms integrated with multiple digital covariates

Keshavarzi,

Kaya,

Başayiğit

et al. 2023

Nutr Cycl Agroecosyst

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The design and application of multiple tools to spatially assess soil properties are key to e cient land management plans. While soil micronutrients are paramount soil properties for multiple purposes, collecting a representative number of samples is time-consuming and expensive. The main aim of this research was to evaluate different scenarios involving 41 environmental variables with two machine learning (ML) algorithms for predicting the spatial distribution of soil micronutrients within the piedmont plain in north-eastern Iran. Sixty-eight locations with different land uses were soil sampled to determine the contents of the micronutrients of iron (Fe), manganese (Mn), zinc (Zn) and copper (Cu). The environmental variables were derived from a digital elevation model, open-source Landsat 8 OLI, Sentinel 2A MSI images, WorldClim climate variables and raster maps of key soil properties. Normalised Root Mean Square Error (NRMSE) and Taylor diagrams were used to evaluate the ML models. Based on the validation results and soil scientists evaluation of the produced maps, the Random Forest (RF) algorithm emerged as the most effective method for predicting the spatial distribution of the soil micronutrients. For the validation set, 91%, 94%, 91% and 108% NRMSE values for Fe, Mn, Zn and Cu, respectively, were given by the RF algorithm. However, one parsimonious scenario involving only the climate covariates also showed promising results. These digital maps produced at 30 m spatial resolution could be used as valuable reconnaissance base information to effectively identify micronutrient de ciencies and excess hotspots for large areas.

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Relationships between the NDVI, Yield of Spring Wheat, and Properties of the Plow Horizon of Eluviated Clay-Illuvial Chernozems and Dark Gray Soils

Cited by 10 publications

References 9 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

Can Basic Soil Quality Indicators and Topography Explain the Spatial Variability in Agricultural Fields Observed from Drone Orthomosaics?

Spatial prediction of soil micronutrients using machine learning algorithms integrated with multiple digital covariates

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