SOIL REFLECTANCE SENSING FOR DETERMINING SOIL PROPERTIES IN PRECISION AGRICULTURE

2001 ◽  
Vol 44 (6) ◽  
Author(s):  
J. A. Thomasson ◽  
R. Sui ◽  
M. S. Cox ◽  
A. Al–Rajehy
Keyword(s):  
Soil Properties ◽  
Precision Agriculture ◽  
Soil Reflectance

scholarly journals Relationship between Spatial Variability Pattern of Wheat Yield and Soil Properties

2018 ◽  
pp. 1-14
Author(s):  
Alidad Karami ◽  
Sadegh Afzalinia
Keyword(s):  
Spatial Structure ◽  
Soil Properties ◽  
Soil Ph ◽  
Precision Agriculture ◽  
Interpolation Method ◽  
Irrigation System ◽  
Wheat Yield ◽  
Gaussian Model ◽  
Variogram Model ◽  
Clay Percentage

Aims: Determining effects of spatial variation of some soil properties on wheat quantity and quality variation in order that proper soil and inputs management can be applied for sustainable wheat production. Study Design: Analyzing data of a field with center pivot irrigation system and uniform management using the geostatistical method. Place and Duration of Study: Soil and Water Research Department, Fars Agricultural and Natural Resources Research and Education Center, Darab, Iran, from September 2013 to February 2014. Methodology: Wheat yield data harvested by class lexion 510 combine from 25 m2 plots (11340 locations) with the corresponding geographical location were used. Besides, soil properties and wheat yield were measured at 36 randomly selected points on the field. Interpolation of parameters was predicted with the best semi-variogram model using kriging, inverse distance weighted (IDW), and cokriging methods. Results: Results showed that wheat yield varied from 2 to 10.08 tons per hectare. Cokriging with cofactor of kernel weight interpolator had more accuracy compared to the combine default interpolator (kriging). A logical, linear correlation was found between different parameters. The best variogram model for pH, OC, and ρb was exponential, for EC, TNV, SP, soil silt and clay percentage was spherical, and for soil, percentage sand was Gaussian model. Data of soil sand, silt, and clay percentage, EC, TNV, and SP had strong spatial structure, and soil pH, OC, and ρb had moderate spatial structure. The best interpolation method for soil pH, EC, sand and silt percentage was kriging method; while, for TNV, SP, OC, ρb, and clay percentage was IDW. Conclusion: There was a close relationship between wheat yield variation and changes in the soil properties. Soil properties and wheat yield distribution maps provided valuable information which could be used for wheat yield improvement in precision agriculture.

scholarly journals Separation of soil and canopy reflectance signatures of Mid German agricultural soils

2011 ◽  
Vol 51 (No, 7) ◽  
pp. 296-303 ◽  
Author(s):  
T. Behrens ◽  
K. Gregor ◽  
W. Diepenbrock
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Vegetation Index ◽  
Near Infrared ◽  
Field Experiments ◽  
Agricultural Soils ◽  
Soil Reflectance

Remote sensing can provide visual indications of crop growth during production season. In past, spectral optical estimations were well performed in the ability to be correlated with crop and soil properties but were not consistent within the whole production season. To better quantify vegetation properties gathered via remote sensing, models of soil reflectance under changing moisture conditions are needed. Signatures of reflected radiation were acquired for several Mid German agricultural soils in laboratory and field experiments. Results were evaluated at near-infrared spectral region at the wavelength of 850 nm. The selected soils represented different soil colors and brightness values reflecting a broad range of soil properties. At the wavelength of 850 nm soil reflectance ranged between 10% (black peat) and 74% (white quartz sand). The reflectance of topsoils varied from 21% to 32%. An interrelation was found between soil brightness rating values and spectral optical reflectance values in form of a linear regression. Increases of soil water content from 0% to 25% decreased signatures of soil reflectance at 850 nm of two different soil types about 40%. The interrelation of soil reflectance and soil moisture revealed a non-linear exponential function. Using knowledge of the individual signature of soil reflectance as well as the soil water content at the measurement, soil reflectance could be predicted. As a result, a clear separation is established between soil reflectance and reflectance of the vegetation cover if the vegetation index is known.

Covariates selection assessment for field scale digital soil mapping in the context of precision fertilization management

2020 ◽  
Author(s):  
Nada Mzid ◽  
Stefano Pignatti ◽  
Irina Veretelnikova ◽  
Raffaele Casa
Keyword(s):  
Soil Properties ◽  
Precision Agriculture ◽  
Prediction Models ◽  
Central Italy ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Maps ◽  
Field Scale ◽  
Umbria Region ◽  
Digital Soil Maps

<p>The application of digital soil mapping in precision agriculture is extremely important, since an assessment of the spatial variability of soil properties within cultivated fields is essential in order to optimize agronomic practices such as fertilization, sowing, irrigation and tillage. In this context, it is necessary to develop methods which rely on information that can be obtained rapidly and at low cost. In the present work, an assessment is carried out of what are the most useful covariates to include in the digital soil mapping of field-scale properties of agronomic interest such as texture (clay, sand, silt), soil organic matter and pH in different farms of the Umbria Region in Central Italy. In each farm a proximal sensing-based mapping of the apparent soil electrical resistivity was carried out using the EMAS (Electro-Magnetic Agro Scanner) sensor. Soil sampling and subsequent analysis in the laboratory were carried out in each field. Different covariates were then used in the development of digital soil maps: apparent resistivity, high resolution Digital Elevation Model (DEM) from Lidar data, and bare soil and/or vegetation indices derived from Sentinel-2 images of the experimental fields. The approach followed two steps: (i) estimation of the variables using a Multiple Linear Regression (MLR) model, (ii) spatial interpolation via prediction models (including regression kriging and block kriging). The validity of the digital soil maps results was assessed both in terms of the accuracy in the estimation of soil properties and in terms of their impact on the fertilization prescription maps for nitrogen (N), phosphorus (P) and potassium (K).</p>

Interpretation of electrical conductivity patterns by soil properties and geological maps for precision agriculture

2008 ◽  
Vol 10 (6) ◽  
pp. 490-507 ◽  
Author(s):  
Jürgen Kühn ◽  
Alexander Brenning ◽  
Marc Wehrhan ◽  
Sylvia Koszinski ◽  
Michael Sommer
Keyword(s):  
Electrical Conductivity ◽  
Soil Properties ◽  
Precision Agriculture ◽  
Geological Maps

Ground-Sensor Soil Reflectance as Related to Soil Properties and Crop Response in a Cotton Field

2005 ◽  
Vol 6 (4) ◽  
pp. 399-411 ◽  
Author(s):  
S. Stamatiadis ◽  
C. Christofides ◽  
C. Tsadilas ◽  
V. Samaras ◽  
J. S. Schepers ◽  
...  
Keyword(s):  
Soil Properties ◽  
Cotton Field ◽  
Crop Response ◽  
Soil Reflectance

Advances in precision agriculture in south-eastern Australia. III. Interactions between soil properties and water use help explain spatial variability of crop production in the Victorian Mallee

2009 ◽  
Vol 60 (9) ◽  
pp. 870 ◽  
Author(s):  
R. D. Armstrong ◽  
J. Fitzpatrick ◽  
M. A. Rab ◽  
M. Abuzar ◽  
P. D. Fisher ◽  
...  
Keyword(s):  
Root Growth ◽  
Grain Yield ◽  
Soil Properties ◽  
Soil Water ◽  
Water Use ◽  
Precision Agriculture ◽  
Crop Growth ◽  
Management Zones ◽  
Eastern Australia ◽  
Supplementary Irrigation

A major barrier to the adoption of precision agriculture in dryland cropping systems is our current inability to reliably predict spatial patterns of grain yield for future crops for a specific paddock. An experiment was undertaken to develop a better understanding of how edaphic and climatic factors interact to influence the spatial variation in the growth, water use, and grain yield of different crops in a single paddock so as to improve predictions of the likely spatial pattern of grain yields in future crops. Changes in a range of crop and soil properties were monitored over 3 consecutive seasons (barley in 2005 and 2007 and lentils in 2006) in the southern section of a 167-ha paddock in the Mallee region of Victoria, which had been classified into 3 different yield (low, moderate, and high) and seasonal variability (stable and variable) zones using normalised difference vegetation index (NDVI) and historic yield maps. The different management zones reflected marked differences in a range of soil properties including both texture in the topsoil and potential chemical-physical constraints in the subsoil (SSCs) to root growth and water use. Dry matter production, grain yield, and quality differed significantly between the yield zones but the relative difference between zones was reduced when supplementary irrigation was applied to barley in 2005, suggesting that some other factor, e.g. nitrogen (N), may have become limiting in that year. There was a strong relationship between crop growth and the use of soil water and nitrate across the management zones, with most water use by the crop occurring in the pre-anthesis/flowering period, but the nature of this relationship appeared to vary with year and/or crop type. In 2006, lentil yield was strongly related to crop establishment, which varied with soil texture and differences in plant-available water. In 2007 the presence of soil water following a good break to the season permitted root growth into the subsoil where there was evidence that SSCs may have adversely affected crop growth. Because of potential residual effects of one crop on another, e.g. through differential N supply and use, we conclude that the utility of the NDVI methodology for developing zone management maps could be improved by using historical records and data for a range of crop types rather than pooling data from a range of seasons.

Development and Testing of a Low-Cost Portable Apparent Soil Electrical Conductivity Sensor Using a Beaglebone Black

2020 ◽  
Vol 36 (3) ◽  
pp. 341-355
Author(s):  
Daniel M. Queiroz ◽  
Emanoel D. T. S. Sousa ◽  
Won Suk Lee ◽  
John K. Schueller
Keyword(s):  
Electrical Conductivity ◽  
Soil Properties ◽  
Precision Agriculture ◽  
Low Cost ◽  
Satellite System ◽  
Mountainous Area ◽  
Soil Electrical Conductivity ◽  
Resistivity Method ◽  
Conductivity Sensor ◽  
Global Navigation Satellite

Abstract.The adoption of apparent soil electrical conductivity (soil ECa) sensors has increased in precision agricultural systems, especially in systems pulled by vehicles. This work developed a portable soil sensor for measuring soil ECa that could be used without vehicles in mountainous areas and small farms. The developed system was based on the electrical resistivity method. The system measured the electrical conductivity by applying a square wave signal at frequencies defined by the user. The acquired data were georeferenced using a low-cost global navigation satellite system (GNSS) receiver. The sensor system was developed using a BeagleBone Black, a low-cost single-board computer. A user interface was developed in C++, and a touch screen with a resolution of 800×480 pixels was used to display the results. This interface performed statistical analysis, and the results were used to guide the user to identify more field locations to be sampled to increase mapping accuracy. The system was tested in a coffee plantation located in a mountainous area and in a sugarcane plantation in Minas Gerais, Brazil. The system worked well in mapping the soil ECa. The apparent soil electrical conductivities measured using frequencies of 10, 20, 30, and 40 Hz were highly correlated. In the sugarcane field that had more variation in soil texture, a greater number of soil properties presented a significant correlation with the soil ECa. Keywords: Electrical conductivity, Geostatistics, Precision agriculture, Soil properties, Soil sensing, Spatial variability.

Soil properties: their prediction and feature extraction from the LUCAS spectral library using deep convolutional neural networks

2021 ◽  
Author(s):  
Liang Zhong ◽  
Xi Guo ◽  
Zhe Xu ◽  
Meng Ding
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Soil Properties ◽  
Convolutional Neural Networks ◽  
Soil Texture ◽  
Precision Agriculture ◽  
Organic Carbon Content ◽  
Spectral Library ◽  
Deep Convolutional Neural Networks ◽  
Large Soil

<p>Soil, as a non-renewable resource, should be monitored continuously to prevent its degradation and promote sustainable agricultural management. Soil spectroscopy in the visible-near infrared range is a fast and cost-effective analytical technique to predict soil properties. The use of large soil spectral libraries can reduce the work needed to reliably estimate soil properties and obtain robust models capable of widespread applicability. Deep learning is apt for big data analysis, and this approach could herald a profound change in the way we model soil spectral data generally. Accordingly, we explored the modeling potential of deep convolutional neural networks (DCNNs) for soil properties based on a large soil spectral library. The European topsoil dataset provided by the Land Use/Cover Area frame Survey (LUCAS) was used without any pre-processing of spectra or covariates added. Two 16-layer DCNN models (ResNet-16 and VGGNet-16) were successfully used to make regression predictions of seven soil properties and classification predictions of soil texture into four groups and 12 levels. Our results showed that the ResNet-16 and VGGNet-16 models produced highly accurate predictions for most soil properties, being superior to either a shallow convolutional neural network and traditional machine learning approaches. Soil organic carbon content, nitrogen content, cation exchange capacity, pH, and calcium carbonate content were well predicted, having a ratio of performance to deviation (RPD) > 2.0. Soil potassium content was adequately predicted (1.4 ≤ RPD ≤ 2.0) and phosphorous content was poorly predicted (RPD < 1.4). The overall classification accuracy of soil texture was 0.749 (four groups) and 0.566 (12 levels). The position of feature wavelengths differed among the soil properties, for which multiple characteristic peaks were common. This study fully demonstrates the modeling potential of deep learning with soil hyperspectral data, which could bring us closer to achieving precision agriculture.</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.

Comparison of Field and Laboratory VNIR Spectroscopy for Profile Soil Property Estimation

2017 ◽  
Vol 60 (5) ◽  
pp. 1503-1510 ◽  
Author(s):  
Yongjin Cho ◽  
Alexander H. Sheridan ◽  
Kenneth A. Sudduth ◽  
Kristen S. Veum
Keyword(s):  
Soil Properties ◽  
Water Content ◽  
Bulk Density ◽  
Precision Agriculture ◽  
Soil Property ◽  
Soil Cores ◽  
Estimation Errors ◽  
Vnir Spectroscopy ◽  
Dry Soil

Abstract. In-field, in-situ data collection with soil sensors has potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate important soil properties, such as soil carbon, nitrogen, water content, and texture. Most previous work has focused on laboratory-based visible and near-infrared (VNIR) spectroscopy using dried soil. The objective of this research was to compare estimates of laboratory-measured soil properties from a laboratory DRS spectrometer and an in-situ profile DRS spectrometer. Soil cores were obtained to approximately 1 m depth from treatment blocks representing variability in topsoil depth located at the South Farm Research Center of the University of Missouri. Soil cores were split by horizon, and samples were scanned with the laboratory DRS spectrometer in both field-moist and oven-dried conditions. In-situ profile DRS spectrometer scans were obtained at the same sampling locations. Soil properties measured in the laboratory from the cores were bulk density, total organic carbon (TOC), total nitrogen (TN), particulate organic matter carbon and nitrogen (POM-C and POM-N), water content, and texture fractions. The best estimates of TOC, TN, and bulk density were from the laboratory DRS spectra on dry soil (R2 = 0.94, 0.91, and 0.71, respectively). Estimation errors with the field DRS system were at most 25% higher for these soil properties. For POM-C and POM-N, the field system provided estimates of similar accuracy to the best (dry soil) laboratory measurements. Clay and silt texture fraction estimates were most accurate from laboratory DRS spectra on field-moist soil (R2 = 0.91 and 0.93, respectively). Estimation errors for clay and silt were almost doubled with the field DRS system. Considering the efficiency advantages, in-field, in-situ DRS appears to be a viable alternative to laboratory DRS for TOC, TN, POM-C, POM-N, and bulk density estimates, but perhaps not for soil texture estimates. Keywords: In-situ sensing, Precision agriculture, Reflectance spectra, Soil properties, Soil spectroscopy.

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