scholarly journals The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes

2021 ◽  
Vol 10 (4) ◽  
pp. 243
Author(s):  
Azamat Suleymanov ◽  
Evgeny Abakumov ◽  
Ruslan Suleymanov ◽  
Ilyusya Gabbasova ◽  
Mikhail Komissarov
Keyword(s):  
Spatial Distribution ◽  
Soil Properties ◽  
Soil Nutrients ◽  
Precision Agriculture ◽  
Large Scale ◽  
Soil Nutrient ◽  
Steppe Zone ◽  
Shuttle Radar Topography Mission ◽  
Coefficient Of Determination ◽  
Support Vector

Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.

Prediction of soil properties by using geographically weighted regression at a regional scale

Soil Research ◽  
2017 ◽  
Vol 55 (4) ◽  
pp. 318 ◽  
Author(s):  
Xing Tan ◽  
Peng-Tao Guo ◽  
Wei Wu ◽  
Mao-Fen Li ◽  
Hong-Bin Liu
Keyword(s):  
Spatial Distribution ◽  
Soil Properties ◽  
Precision Agriculture ◽  
Geographically Weighted Regression ◽  
Regional Scale ◽  
Ordinary Least Squares ◽  
Ecological Modelling ◽  
Weighted Regression ◽  
Coefficient Of Determination ◽  
Ols Regression

Detailed information about spatial distribution of soil properties is important in ecological modelling, environmental prediction, precision agriculture, and natural resources management, as well as land-use planning. In the present study, a recently developed method called geographically weighted regression (GWR) is applied to predict spatial distribution of soil properties (pH, soil organic matter, available nitrogen, available potassium) based on topographical indicators, climate factors, and geological stratum at a regional scale. In total, 1914 soil samples collected from a depth of 0–20cm were used to calibrate and validate the models. Performances of the GWR models were compared with the traditional, ordinary least-squares (OLS) regression. The results indicated that the GWR models made significant improvements to model performances over OLS regression, based on F-test, coefficient of determination, and corrected Akaike information criterion. GWR models also improved the reliability of the soil–environment relationships by reducing the spatial autocorrelations in model residuals. Meanwhile, the use of GWR models disclosed that the relationships between soil properties and environmental variables were not invariant over space but exhibited significant spatial non-stationarity. Accordingly, the GWR models remarkably improved the prediction accuracies over the corresponding OLS models. The results demonstrated that GWR could serve as a useful tool for digital soil mapping in areas with complex terrain.

The Nutrients Spatial Variability of Black Soil Based on GIS

2014 ◽  
Vol 694 ◽  
pp. 580-583
Author(s):  
Yue Ling Zhao ◽  
Hai Yan Han ◽  
Li Ying Cao ◽  
Gui Fen Chen
Keyword(s):  
Information Technology ◽  
Spatial Distribution ◽  
Soil Nutrients ◽  
Production Technology ◽  
Precision Agriculture ◽  
Agricultural Production ◽  
Soil Nutrient ◽  
Black Soil ◽  
Critical Determinant ◽  
Speed Up

The precision agriculture (PA) is the end product of the modern high tech's information technology and the agricultural production technology union. The soil nutrients are not only an important component of soil research, but also a critical determinant of its productivity. Some soil nutrients spatial distribution pictures were established based on important factors that affect crops production. The soil nutrient situation was understood by the paper in Jilin province black soil. The results can realized some information opening and sharing and helped some farmer and manager to understand some soil nutrient spatial distribution. They can speed up the development of Jilin province’s precision agriculture.

scholarly journals Estimation of Soil Nutrient Content Using Hyperspectral Data

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1129
Author(s):  
Yiping Peng ◽  
Lu Wang ◽  
Li Zhao ◽  
Zhenhua Liu ◽  
Chenjie Lin ◽  
...  
Keyword(s):  
Least Squares ◽  
Soil Nutrients ◽  
Cross Validation ◽  
Regional Scale ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
New Method ◽  
Accurate Estimation ◽  
Support Vector ◽  
Soil Nutrient Content

Soil nutrients play a vital role in plant growth and thus the rapid acquisition of soil nutrient content is of great significance for agricultural sustainable development. Hyperspectral remote-sensing techniques allow for the quick monitoring of soil nutrients. However, at present, obtaining accurate estimates proves to be difficult due to the weak spectral features of soil nutrients and the low accuracy of soil nutrient estimation models. This study proposed a new method to improve soil nutrient estimation. Firstly, for obtaining characteristic variables, we employed partial least squares regression (PLSR) fit degree to select an optimal screening algorithm from three algorithms (Pearson correlation coefficient, PCC; least absolute shrinkage and selection operator, LASSO; and gradient boosting decision tree, GBDT). Secondly, linear (multi-linear regression, MLR; ridge regression, RR) and nonlinear (support vector machine, SVM; and back propagation neural network with genetic algorithm optimization, GABP) algorithms with 10-fold cross-validation were implemented to determine the most accurate model for estimating soil total nitrogen (TN), total phosphorus (TP), and total potassium (TK) contents. Finally, the new method was used to map the soil TK content at a regional scale using the soil component spectral variables retrieved by the fully constrained least squares (FCLS) method based on an image from the HuanJing-1A Hyperspectral Imager (HJ-1A HSI) of the Conghua District of Guangzhou, China. The results identified the GBDT-GABP was observed as the most accurate estimation method of soil TN ( of 0.69, the root mean square error of cross-validation (RMSECV) of 0.35 g kg−1 and ratio of performance to interquartile range (RPIQ) of 2.03) and TP ( of 0.73, RMSECV of 0.30 g kg−1 and RPIQ = 2.10), and the LASSO-GABP proved to be optimal for soil TK estimations ( of 0.82, RMSECV of 3.39 g kg−1 and RPIQ = 3.57). Additionally, the highly accurate LASSO-GABP-estimated soil TK (R2 = 0.79) reveals the feasibility of the LASSO-GABP method to retrieve soil TK content at the regional scale.

scholarly journals Anthropogenic Disturbance Impacts Mycorrhizal Communities and Abiotic Soil Properties: Implications for an Endemic Forest Disease

2021 ◽  
Vol 3 ◽  
Author(s):  
Sarah J. Sapsford ◽  
Trudy Paap ◽  
Giles E. St. J. Hardy ◽  
Treena I. Burgess
Keyword(s):  
Soil Properties ◽  
Soil Nutrients ◽  
Large Scale ◽  
Stand Structure ◽  
Disease Incidence ◽  
Arbuscular Mycorrhizal ◽  
Am Fungi ◽  
Illumina Miseq ◽  
Fungal Communities ◽  
Canker Disease

In forest ecosystems, habitat fragmentation negatively impacts stand structure and biodiversity; the resulting fragmented patches of forest have distinct, disturbed edge habitats that experience different environmental conditions than the interiors of the fragments. In southwest Western Australia, there is a large-scale decline of the keystone tree species Corymbia calophylla following fragmentation and land use change. These changes have altered stand structure and increased their susceptibility to an endemic fungal pathogen, Quambalaria coyrecup, which causes chronic canker disease especially along disturbed forest habitats. However, the impacts of fragmentation on belowground processes in this system are not well-understood. We examined the effects of fragmentation on abiotic soil properties and ectomycorrhizal (ECM) and arbuscular mycorrhizal (AM) fungal communities, and whether these belowground changes were drivers of disease incidence. We collected soil from 17 sites across the distribution range of C. calophylla. Soils were collected across a gradient from disturbed, diseased areas to undisturbed, disease-free areas. We analysed soil nutrients and grew C. calophylla plants as a bioassay host. Plants were harvested and roots collected after 6 months of growth. DNA was extracted from the roots, amplified using fungal specific primers and sequenced using Illumina MiSeq. Concentrations of key soil nutrients such as nitrogen, phosphorus and potassium were much higher along the disturbed, diseased edges in comparison to undisturbed areas. Disturbance altered the community composition of ECM and AM fungi; however, only ECM fungal communities had lower rarefied richness and diversity along the disturbed, diseased areas compared to undisturbed areas. Accounting for effects of disturbance, ECM fungal diversity and leaf litter depth were highly correlated with increased disease incidence in C. calophylla. In the face of global change, increased virulence of an endemic pathogen has emerged in this Mediterranean-type forest.

scholarly journals Retrieval and Mapping of Soil Organic Carbon Using Sentinel-2A Spectral Images from Bare Cropland in Autumn

2021 ◽  
Vol 13 (6) ◽  
pp. 1072
Author(s):  
Ke Wang ◽  
Yanbing Qi ◽  
Wenjing Guo ◽  
Jielin Zhang ◽  
Qingrui Chang
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Properties ◽  
Spectral Reflectance ◽  
Prediction Models ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Fertility Level ◽  
Sentinel 2A

Soil is the largest carbon reservoir on the terrestrial surface. Soil organic carbon (SOC) not only regulates global climate change, but also indicates soil fertility level in croplands. SOC prediction based on remote sensing images has generated great interest in the research field of digital soil mapping. The short revisiting time and wide spectral bands available from Sentinel-2A (S2A) remote sensing data can provide a useful data resource for soil property prediction. However, dense soil surface coverage reduces the direct relationship between soil properties and S2A spectral reflectance such that it is difficult to achieve a successful SOC prediction model. Observations of bare cropland in autumn provide the possibility to establish accurate SOC retrieval models using the S2A super-spectral reflectance. Therefore, in this study, we collected 225 topsoil samples from bare cropland in autumn and measured the SOC content. We also obtained S2A spectral images of the western Guanzhong Plain, China. We established four SOC prediction models, including random forest (RF), support vector machine (SVM), partial least-squares regression (PLSR), and artificial neural network (ANN) based on 15 variables retrieved from the S2A images, and compared the prediction accuracy using RMSE (root mean square error), R2 (coefficient of determination), and RPD (ratio of performance to deviation). Based on the optimal model, the spatial distribution of SOC was mapped and analyzed. The results indicated that the inversion model with the RF algorithm achieved the highest accuracy, with an R2 of 0.8581, RPD of 2.1313, and RMSE of 1.07. The variables retrieved from the shortwave infrared (SWIR) bands (B11 and B12) usually had higher variable importance, except for the ANN model. SOC content mapped with the RF model gradually decreased with increasing distance from the Wei river, and values were higher in the west than in the east. These results matched the SOC distribution based on measurements at the sample sites. This research provides evidence that soil properties such as SOC can be retrieved and spatially mapped based on S2A images that are obtained from bare cropland in autumn.

scholarly journals Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1430
Author(s):  
Yingying Li ◽  
Zhengyong Zhao ◽  
Sunwei Wei ◽  
Dongxiao Sun ◽  
Qi Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Forest Soil ◽  
Soil Nutrients ◽  
Predictive Power ◽  
Remote Sensing Data ◽  
Soil Nutrient ◽  
Soil Maps ◽  
Soil Layers ◽  
Coarse Resolution

The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of forest ecosystems. The rapid development of remote sensing satellites provides an excellent opportunity to improve the accuracy of forest soil prediction models. This study aimed to explore the utility of the Gaofen-1 (GF-1) satellite in the forest soil mapping model in Luoding City, Yunfu City, Guangdong Province, Southeast China. We used 1000 m resolution coarse-resolution soil map to represent the overall regional soil nutrient status, 12.5 m resolution terrain-hydrology variables to reflect the detailed spatial distribution of soil nutrients, and 8 m resolution remote sensing variables to reflect the surface vegetation status to build terrain-hydrology artificial neural network (ANN) models and full variable ANNs, respectively. The prediction objects were alkali-hydro-nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). The results showed that the full-variable ANN accuracy at five soil depths was better than the terrain-hydrology ANNs, indicating that remote sensing variables reflecting vegetation status can improve the prediction of forest soil nutrients. The remote sensing variables had different effectiveness for different soil nutrients and different depths. In upper soil layers (0–20 and 20–40 cm), remote sensing variables were more useful for AN, AP, and OM, and were between 10%–14% (R2), and less effective for AK at only 8% and 6% (R2). In deep soil layers (40–60, 60–80, and 80–100 cm), the improvement of all soil nutrient models was not significant, between 3 and 6% (R2). RMSE and ROA ± 5% also decreased with the depth of soil. Remote sensing ANNs (coarse resolution soil maps + remote sensing variables) further demonstrated that the predictive power of remote sensing data decreases with soil depth. Compared to terrain-hydrological variables, remote sensing variables perform better at 0–20 cm, but the predictive power decreased rapidly with depth. In conclusion, the results of the study showed that the integration of remote sensing with coarse-resolution soil maps and terrain-hydrology variables could strongly improve upper forest soil (0–40 cm) nutrients prediction and NDVI, green band, and forest types were the best remote sensing predictors. In addition, the study area is rich in AN and OM, while AP and AK are scarce. Therefore, to improve forest health, attention should be paid to monitoring and managing AN, AP, AK, and OM levels.

Research on the Spatial Variability of Soil Nutrients Prediction Based on GIS

2013 ◽  
Vol 791-793 ◽  
pp. 1681-1685
Author(s):  
Zi Han Qin
Keyword(s):  
Spatial Variability ◽  
Soil Nutrients ◽  
Spatial Data ◽  
Precision Agriculture ◽  
Interpolation Method ◽  
Soil Nutrient ◽  
Growth Period ◽  
Crop Growth ◽  
Accurate Analysis ◽  
Point Line

Soil is a necessary nutrition library of crop growth, its nutrient spatial variability exists the whole crop growth period with the characteristics of long and complex, so the spatial variability of soil nutrient prediction is one of the hot research problems to be urgently solved in precision agriculture. This paper first introduces GIS and statistical analysis organic combination of research method and model, on the basis of this, based on GIS spatial data level of analysis and point-line-side buffer, we will accurate analysis of point-line-sides mutation interval through the variation Euclidean distance and distance weighting interpolation method. Finally through the comparison of mean value and T test, we can predict the approximate interval of soil nutrients spatial variability, to a certain extent, it can provide theory and technology support for the scientific prediction of soil nutrients spatial variability.

scholarly journals Spatial distribution and correlation of soil properties in a field: a case study

2011 ◽  
Vol 48 (No. 10) ◽  
pp. 425-432
Author(s):  
L. Borůvka ◽  
H. Donátová ◽  
K. Němeček
Keyword(s):  
Spatial Distribution ◽  
Soil Properties ◽  
Precision Agriculture ◽  
Clay Content ◽  
Total Porosity ◽  
Organic Carbon Content ◽  
Agricultural Field ◽  
Spatial Correlations ◽  
Fine Silt ◽  
Silt Content

Analysis of spatial distribution and correlation of soil properties represents an important outset for precision agriculture. This paper presents an analysis of spatial distribution and mutual correlations, both classical and spatial, of soil properties in an agricultural field in Klučov. Clay and fine silt content, pH, organic carbon content (C<sub>org</sub>), moisture (Q), total porosity (Pt), capillary porosity (P<sub>c</sub>), and coefficients of aggregate vulnerability to fast wetting (K<sub>v1</sub>), to slow wetting and drying (K<sub>v2</sub>), and to mechanical impacts (K<sub>v3</sub>) were determined. Semivariogram ranges from 206 m (clay content) to 1120 m (K<sub>v3</sub>) were detected. Many relationships between soil properties were spatially based. Fine silt content and Corg&nbsp;proved to be the most important soil properties controlling all the three aggregate vulnerability coefficients, which was not clear for K<sub>v2</sub>&nbsp;from classical correlation only. Determined spatial correlations and similarities in spatial distribution may serve as groundwork in delineation of different zones for site-specific management.

scholarly journals Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3919
Author(s):  
Xiaoyu Yang ◽  
Nisha Bao ◽  
Wenwen Li ◽  
Shanjun Liu ◽  
Yanhua Fu ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Signal To Noise Ratio ◽  
Feature Selection Method ◽  
Soil Nutrient ◽  
Pso Algorithm ◽  
Support Vector ◽  
Least Squares Regression ◽  
Successive Projections Algorithm ◽  
Imaging Spectrometry ◽  
Rapid Acquisition

Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained from an unmanned aerial vehicle (UAV) platform for estimating the soil organic matter (SOM) and soil total nitrogen (STN) in farmland. The results showed that: (1) Multiplicative Scattering Correction (MSC) performed better in reducing image scattering noise than Standard Normal Variate (SNV) transformation or spectral derivatives, and it yielded a result with higher correlation and lower signal-to-noise ratio; (2) The proposed feature selection method combining Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling algorithm (CARS), could provide selective preference for hyperspectral bands. Exploiting this method, 24 and 22 feature bands were selected for SOM and STN estimation, respectively; (3) The particle swarm optimization (PSO) algorithm was employed to obtain optimized input weights and bias values of the extreme learning machine (ELM) model for more accurate prediction of SOM and STN. The improved PSO-ELM model based on the selected preference bands achieved higher prediction accuracy (R2 of 0.73 and RPD of 1.91 for SOM, R2 of 0.63, and RPD of 1.53 for STN) than support vector machine (SVM), partial least squares regression (PLSR), and the ELM model. This study provides an important guideline for monitoring soil nutrient for precision agriculture with imaging spectrometry.

scholarly journals Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning

2020 ◽  
Vol 12 (12) ◽  
pp. 2028 ◽  
Author(s):  
Luwei Feng ◽  
Zhou Zhang ◽  
Yuchi Ma ◽  
Qingyun Du ◽  
Parker Williams ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Vegetation Indices ◽  
Model Performance ◽  
Original Data ◽  
The United States ◽  
Hyperspectral Data ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Yield Prediction ◽  
Ensemble Model

Alfalfa is a valuable and intensively produced forage crop in the United States, and the timely estimation of its yield can inform precision management decisions. However, traditional yield assessment approaches are laborious and time-consuming, and thus hinder the acquisition of timely information at the field scale. Recently, unmanned aerial vehicles (UAVs) have gained significant attention in precision agriculture due to their efficiency in data acquisition. In addition, compared with other imaging modalities, hyperspectral data can offer higher spectral fidelity for constructing narrow-band vegetation indices which are of great importance in yield modeling. In this study, we performed an in-season alfalfa yield prediction using UAV-based hyperspectral images. Specifically, we firstly extracted a large number of hyperspectral indices from the original data and performed a feature selection to reduce the data dimensionality. Then, an ensemble machine learning model was developed by combining three widely used base learners including random forest (RF), support vector regression (SVR) and K-nearest neighbors (KNN). The model performance was evaluated on experimental fields in Wisconsin. Our results showed that the ensemble model outperformed all the base learners and a coefficient of determination (R2) of 0.874 was achieved when using the selected features. In addition, we also evaluated the model adaptability on different machinery compaction treatments, and the results further demonstrate the efficacy of the proposed ensemble model.

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