scholarly journals Using Sentinel-2 for Simplifying Soil Sampling and Mapping: Two Case Studies in Umbria, Italy

2021 ◽  
Vol 13 (17) ◽  
pp. 3379
Author(s):  
Francesco Saverio Santaga ◽  
Alberto Agnelli ◽  
Angelo Leccese ◽  
Marco Vizzari
Keyword(s):  
Precision Agriculture ◽  
Principal Component ◽  
Unsupervised Classification ◽  
Soil Samples ◽  
Soil Sampling ◽  
Field Variation ◽  
Sample Collection ◽  
Sampling Schemes ◽  
And Cluster Analysis ◽  
Sentinel 2

Soil-sample collection and strategy are costly and time-consuming endeavors, mainly when the goal is in-field variation mapping that usually requires dense sampling. This study developed and tested a streamlined soil mapping methodology, applicable at the field scale, based on an unsupervised classification of Sentinel-2 (S2) data supporting the definition of reduced soil-sampling schemes. The study occurred in two agricultural fields of 20 hectares each near Deruta, Umbria, Italy. S2 images were acquired for the two bare fields. After a band selection based on bibliography, PCA (Principal Component Analysis) and cluster analysis were used to identify points of two reduced-sample schemes. The data obtained by these samplings were used in linear regressions with principal components of the selected S2 bands to produce maps for clay and organic matter (OM). Resultant maps were assessed by analyzing residuals with a conventional soil sampling of 30 soil samples for each field to quantify their accuracy level. Although of limited extent and with a specific focus, the low average errors (Clay ± 2.71%, OM ± 0.16%) we obtained using only three soil samples suggest a wider potential for this methodology. The proposed approach, integrating S2 data and traditional soil-sampling methods could considerably reduce soil-sampling time and costs in ordinary and precision agriculture applications.

scholarly journals Uncertainties associated with the delineation of management zones in precision agriculture

2020 ◽  
Author(s):  
Tomás R. Tenreiro ◽  
Margarita García-Vila ◽  
José A. Gómez ◽  
Elías Fereres
Keyword(s):  
Spatial Data ◽  
Precision Agriculture ◽  
Initial Conditions ◽  
Principal Component ◽  
Ground Cover ◽  
Temporal Variations ◽  
Yield Response ◽  
Temporal Asymmetry ◽  
Management Zones ◽  
Sampling Schemes

<p>The characterization of spatial variations in soil properties and crop performance within precision agriculture, and particularly the delineation of management zones (MZ) and sampling schemes, are complex assignments currently far from being resolved. Considerable advances have been achieved regarding the analysis of spatial data, but less attention has been devoted to assess the temporal asymmetry associated with variable <em>crop×year</em> interactions. In this case-study of a 9 ha field located in Spain, we captured interactions between both spatial and temporal variations for two contrasting seasons of remotely sensed crop data (NDVI) combined with several geomorphological properties (i.e., elevation, slope orientation, soil apparent electrical conductivity - ECa, %Clay, %Sand, pH). We developed an algorithm combining Principal Component Analysis (PCA) and clustering k-means and succeeded to delineate four MZ’s with a satisfactory fragmentation degree, each one associated with a different <em>Elevation×ECa×NDVI</em> combination. Simulated yield maps were generated using NDVI maps correlated to ground cover to establish initial conditions in simulation settings with a crop model. Yield maps were spatially correlated but fitted into variograms with irregular spatial structure. Both CV and spatial patterns did not show consistency from year to year. The results indicate that MZ’s temporal instability is an important issue for site-specific management as agronomic implications varied greatly with <em>crop×year</em> setting. We observed differences, not only regarding NDVI patterns but also in yield response to the combination of <em>Elevation×ECa</em> (and <em>Texture</em>) depending on the seasonal rainfall. A reduction of 14% of the ’Goodness of Variance Fit’ was observed for simulated yield from the first to the second <em>crop×year</em>, highlighting the difficulties in the delineation of MZ’s with persistent confidence. The interpretation of <em>MZ×Yield</em> associations was not straight forward from the metrics selected here as it also depended on agronomic knowledge. We believe that precision agriculture will benefit greatly from improved protocols for MZ delineation and sampling schemes. However, the uncertainty associated with temporal asymmetry of yield clustering and MZ’s interpretation reveals that ‘automated digital agricultural systems’ are still far from reality.</p>

scholarly journals Influence of Soil Properties on Maize and Wheat Nitrogen Status Assessment from Sentinel-2 Data

2020 ◽  
Vol 12 (14) ◽  
pp. 2175
Author(s):  
Alberto Crema ◽  
Mirco Boschetti ◽  
Francesco Nutini ◽  
Donato Cillis ◽  
Raffaele Casa
Keyword(s):  
Soil Properties ◽  
Precision Agriculture ◽  
N Fertilization ◽  
Soil Samples ◽  
Site Specific ◽  
Plant Nitrogen ◽  
Plant Nitrogen Uptake ◽  
Status Assessment ◽  
N Status ◽  
Sentinel 2

Soil properties variability is a factor that greatly influences cereals crops production and interacts with a proper assessment of crop nutritional status, which is fundamental to support site-specific management able to guarantee a sustainable crop production. Several management strategies of precision agriculture are now available to adjust the nitrogen (N) input to the actual crop needs. Many of the methods have been developed for proximal sensors, but increasing attention is being given to satellite-based N management systems, many of which rely on the assessment of the N status of crops. In this study, the reliability of the crop nutritional status assessment through the estimation of the nitrogen nutrition index (NNI) from Sentinel-2 (S2) satellite images was examined, focusing of the impact of soil properties variability for crop nitrogen deficiency monitoring. Vegetation indices (VIs) and biophysical variables (BVs), such as the green area index (GAI_S2), leaf chlorophyll content (Cab_S2), and canopy chlorophyll content (CCC_S2), derived from S2 imagery, were used to investigate plant N status and NNI retrieval, in the perspective of its use for guiding site-specific N fertilization. Field experiments were conducted on maize and on durum wheat, manipulating 4 groups of plots, according to soil characteristics identified by a soil map and quantified by soil samples analysis, with different N treatments. Field data collection highlighted different responses of the crops to N rate and soil type in terms of NNI, biomass (W), and nitrogen concentration (Na%). For both crops, plots in one soil class (FOR1) evidenced considerably lower values of BVs and stress conditions with respect to others soil classes even for high N rates. Soil samples analyses showed for FOR1 soil class statistically significant differences for pH, compared to the other soil classes, indicating that this property could be a limiting factor for nutrient absorption, hence crop growth, regardless of the amount of N distributed to the crop. The correlation analysis between measured crop related BVs and satellite-based products (VIs and S2_BVs) shows that it is possible to: (i) directly derive NNI from CCC_S2 (R2 = 0.76) and either normalized difference red edge index (NDRE) for maize (R2 = 0.79) or transformed chlorophyll absorption ratio index (TCARI) for durum wheat (R2 = 0.61); (ii) indirectly estimate NNI as the ratio of plant nitrogen uptake (PNUa) and critical plant nitrogen uptake (PNUc) derived using CCC_S2 (R2 = 0.77) and GAI_S2 (R2 = 0.68), respectively. Results of this study confirm that NNI is a good indicator to monitor plants N status, but also highlights the importance of linking this information to soil properties to support N site-specific fertilization in the precision agriculture framework. These findings contribute to rational agro-practices devoted to avoid N fertilization excesses and consequent environmental losses, bringing out the real limiting factors for optimal crop growth.

scholarly journals Optimum size in grid soil sampling for variable rate application in site-specific management

2011 ◽  
Vol 68 (3) ◽  
pp. 386-392 ◽  
Author(s):  
Marcos Rafael Nanni ◽  
Fabrício Pinheiro Povh ◽  
José Alexandre Melo Demattê ◽  
Roney Berti de Oliveira ◽  
Marcelo Luiz Chicati ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Precision Agriculture ◽  
Production Efficiency ◽  
Sampling Interval ◽  
Soil Samples ◽  
Soil Sampling ◽  
Kappa Statistics ◽  
Increase Production Efficiency ◽  
Site Specific ◽  
Specific Management

The importance of understanding spatial variability of soils is connected to crop management planning. This understanding makes it possible to treat soil not as a uniform, but a variable entity, and it enables site-specific management to increase production efficiency, which is the target of precision agriculture. Questions remain as the optimum soil sampling interval needed to make site-specific fertilizer recommendations in Brazil. The objectives of this study were: i) to evaluate the spatial variability of the main attributes that influence fertilization recommendations, using georeferenced soil samples arranged in grid patterns of different resolutions; ii) to compare the spatial maps generated with those obtained with the standard sampling of 1 sample ha-1, in order to verify the appropriateness of the spatial resolution. The attributes evaluated were phosphorus (P), potassium (K), organic matter (OM), base saturation (V%) and clay. Soil samples were collected in a 100 × 100 m georeferenced grid. Thinning was performed in order to create a grid with one sample every 2.07, 2.88, 3.75 and 7.20 ha. Geostatistical techniques, such as semivariogram and interpolation using kriging, were used to analyze the attributes at the different grid resolutions. This analysis was performed with the Vesper software package. The maps created by this method were compared using the kappa statistics. Additionally, correlation graphs were drawn by plotting the observed values against the estimated values using cross-validation. P, K and V%, a finer sampling resolution than the one using 1 sample ha-1 is required, while for OM and clay coarser resolutions of one sample every two and three hectares, respectively, may be acceptable.

scholarly journals Elemental composition of surface soils in Nature Park Shumen Plateau and Shumen City, Bulgaria

2018 ◽  
Vol 5 (2) ◽  
pp. 68-76
Author(s):  
Vanya Koleva ◽  
Teodora Koynova ◽  
Asya Dragoeva ◽  
Nikolay Natchev
Keyword(s):  
Heavy Metals ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Elemental Composition ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Samples ◽  
Surface Soils ◽  
Nature Park ◽  
And Cluster Analysis

Abstract Anthropogenic activities cause environmental pollution and alter biogeochemical cycles. Soils in cities and their vicinity are exposed to different pollutants. Nature Park Shumen Plateau is a protected area situated in the proximity of Shumen (Bulgaria). The aim of this research was to compare elemental composition of surface soil samples from Nature Park with two areas in Shumen city. Soil samples from seven sites on the territory of Nature Park and from two urban sites were collected. The elemental composition of the samples was determined using Energy Dispersive X-Ray Fluorescence technique. Principal component analysis and cluster analysis were performed to interpret the complex data. The content of 24 elements was determined: Br, Y, Zr, Mo, Ag, Cd, Sn, Sb, I, Cs, Ba, La, Ce Si, K, Ca, Ti, Mn, Fe, Cu, Zn, Rb, Sr, and Pb. Results presented here and previously showed that concentrations of heavy metals Cu, Zn, Cd and Pb are below the upper limit according to Bulgarian legislation. Concentrations of Mn and Fe in samples from Nature Park were comparable to the literature data reported for unpolluted areas. Principal component analysis and cluster analysis show similarity of the content of 24 elements between samples from Nature Park and from Shumen city. These findings are in accordance with our previous positive results from Allium-test: cytogenetic endpoints showed a presence of harmful compounds in Nature Park soils. The content of heavy metals in the surface soils studied show a lack of environmental risk for Nature Park. However, a similar distribution pattern of the investigated elements in the park and two anthropologically influenced areas in Shumen city indicated a potential hazard in Nature Park.

The Utilization of Ftir (Fourier Transform Infra-Red) Method Combined with Chemometrics For Authentication of Indonesian Coffee Powder

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
Firmansyah A. ◽  
Winingsih W. ◽  
Soebara Y S
Keyword(s):  
Fourier Transform ◽  
Natural Product ◽  
High Performance ◽  
Principal Component ◽  
Raw Material ◽  
Attenuated Total Reflectance ◽  
Coffee Bean ◽  
Product Analysis ◽  
Total Reflectance ◽  
And Cluster Analysis

Analysis of natural product remain challenging issues for analytical chemist, since natural products are complicated system of mixture. The most popular methods of choice used for quality control of raw material and finished product are high performance liquid chromatography (HPLC), gas chromatography (GC) and mass spectrometry (MS). The utilization of FTIR-ATR (Fourier Transform Infrared-Attenuated Total Reflectance) method in natural product analysis is still limited. This study attempts to expand the use of FTIR spectroscopy in authenticating Indonesian coffee powder.The coffee samples studied were taken from nine regions in Indonesia, namely Aceh Gayo, Flores, Kintamani, Mandheling, Papua, Sidikalang, Toraja, Kerinci and Lampung.The samples in the form of coffee bean from various regions were powdered . The next step conducted was to determine the spectrum using the FTIR-ATR (Attenuated Total Reflectance) using ZnSe crystal of 8000 resolution. Spectrum samples, then, were analyzed using chemometrics. The utilized chemometric model was the principal component analysis (PCA) and cluster analysis (CA). Based on the chemometric analysis, there are similarities between Aceh Gayo coffee with Toraja coffee, Mandailing coffee, Kintamani coffee and Flores coffee. Sidikalang coffee has a similarity to Flores coffee; Papua coffee has a similarity to Sidikalang coffee; Lampung coffee has a similarity to Sidikalang coffee, while Kerinci coffee has a similarity to Papua coffee.

scholarly journals Comparison of Capability of SAR and Optical Data in Mapping Forest above Ground Biomass Based on Machine Learning

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Negar Tavasoli ◽  
Hossein Arefi
Keyword(s):  
Machine Learning ◽  
Carbon Stock ◽  
Vegetation Indices ◽  
Forest Biomass ◽  
Principal Component ◽  
Optical Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Texture Characteristics ◽  
Sentinel 2

Assessment of forest above ground biomass (AGB) is critical for managing forest and understanding the role of forest as source of carbon fluxes. Recently, satellite remote sensing products offer the chance to map forest biomass and carbon stock. The present study focuses on comparing the potential use of combination of ALOSPALSAR and Sentinel-1 SAR data, with Sentinel-2 optical data to estimate above ground biomass and carbon stock using Genetic-Random forest machine learning (GA-RF) algorithm. Polarimetric decompositions, texture characteristics and backscatter coefficients of ALOSPALSAR and Sentinel-1, and vegetation indices, tasseled cap, texture parameters and principal component analysis (PCA) of Sentinel-2 based on measured AGB samples were used to estimate biomass. The overall coefficient (R2) of AGB modelling using combination of ALOSPALSAR and Sentinel-1 data, and Sentinel-2 data were respectively 0.70 and 0.62. The result showed that Combining ALOSPALSAR and Sentinel-1 data to predict AGB by using GA-RF model performed better than Sentinel-2 data.

scholarly journals Four-Year Study on the Bio-Agronomic Response of Biotypes of Capparis spinosa L. on the Island of Linosa (Italy)

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 327
Author(s):  
Salvatore La Bella ◽  
Francesco Rossini ◽  
Mario Licata ◽  
Giuseppe Virga ◽  
Roberto Ruggeri ◽  
...  
Keyword(s):  
Block Design ◽  
Principal Component ◽  
Flower Bud ◽  
Natural Habitats ◽  
Phenological Stages ◽  
Average Value ◽  
Randomized Complete Block Design ◽  
Capparis Spinosa ◽  
Production Behavior ◽  
And Cluster Analysis

The caper plant is widespread in Sicily (Italy) both wild in natural habitats and as specialized crops, showing considerable morphological variation. However, although contributing to a thriving market, innovation in caper cropping is low. The aim of the study was to evaluate agronomic and production behavior of some biotypes of Capparis spinosa L. subsp. rupestris, identified on the Island of Linosa (Italy) for growing purposes. Two years and seven biotypes of the species were tested in a randomized complete block design. The main morphological and production parameters were determined. Phenological stages were also observed. Analysis of variance showed high variability between the biotypes. Principal component analysis and cluster analysis highlighted a clear distinction between biotypes based on biometric and production characteristics. Production data collected in the two-year period 2007–2008 showed the greatest production levels in the third year following planting in 2005. In particular, biotype SCP1 had the highest average value (975.47 g) of flower bud consistency. Our results permitted the identification of biotypes of interest for the introduction into new caper fields. Further research is needed in order to characterize caper biotypes in terms of the chemical composition of the flower buds and fruits.

scholarly journals The Utility of the Upcoming HyspIRI’s Simulated Spectral Settings in Detecting Maize Gray Leafy Spot in Relation to Sentinel-2 MSI, VENµS, and Landsat 8 OLI Sensors

Agronomy ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 846
Author(s):  
Mbulisi Sibanda ◽  
Onisimo Mutanga ◽  
Timothy Dube ◽  
John Odindi ◽  
Paramu L. Mafongoya
Keyword(s):  
Food Security ◽  
Precision Agriculture ◽  
Leaf Spot ◽  
Gray Leaf Spot ◽  
Economic Losses ◽  
Leaf Spot Disease ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Maize Crop ◽  
Sentinel 2

Considering the high maize yield loses caused by incidences of disease, as well as incomprehensive monitoring initiatives in crop farming, there is a need for spatially explicit, cost-effective, and consistent approaches for monitoring, as well as for forecasting, food-crop diseases, such as maize Gray Leaf Spot. Such approaches are valuable in reducing the associated economic losses while fostering food security. In this study, we sought to investigate the utility of the forthcoming HyspIRI sensor in detecting disease progression of Maize Gray Leaf Spot infestation in relation to the Sentinel-2 MSI and Landsat 8 OLI spectral configurations simulated using proximally sensed data. Healthy, intermediate, and severe categories of maize crop infections by the Gray Leaf Spot disease were discriminated based on partial least squares–discriminant analysis (PLS-DA) algorithm. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93, and 0.89, which were exhibited by Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor sensors, respectively. Furthermore, the results showed that the visible section, red-edge, and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray Leaf Spot infections. These findings underscore the potential value of the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop-disease epidemics, which are necessary to ensure food security.

Deep Learning Associated with Laser-Induced Breakdown Spectroscopy (LIBS) for the Prediction of Lead in Soil

2019 ◽  
Vol 73 (5) ◽  
pp. 565-573 ◽  
Author(s):  
Yun Zhao ◽  
Mahamed Lamine Guindo ◽  
Xing Xu ◽  
Miao Sun ◽  
Jiyu Peng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Confusion Matrix ◽  
Principal Component ◽  
Classification Performance ◽  
Soil Samples ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Different Levels

In this study, a method based on laser-induced breakdown spectroscopy (LIBS) was developed to detect soil contaminated with Pb. Different levels of Pb were added to soil samples in which tobacco was planted over a period of two to four weeks. Principal component analysis and deep learning with a deep belief network (DBN) were implemented to classify the LIBS data. The robustness of the method was verified through a comparison with the results of a support vector machine and partial least squares discriminant analysis. A confusion matrix of the different algorithms shows that the DBN achieved satisfactory classification performance on all samples of contaminated soil. In terms of classification, the proposed method performed better on samples contaminated for four weeks than on those contaminated for two weeks. The results show that LIBS can be used with deep learning for the detection of heavy metals in soil.

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