The possibilities of using thermal infrared imaging data for detecting the main parameters of arable soil fertility

The analysis of the possibility of using the thermal infrared images for detecting soil fertility parameters of gray forest and alluvial arable soils was carried out by the example of a test filed in Tula region of Russia. Together with the sampling of 25 soil probes from the 0–10 cm layer, the open surface of the soil was photographed using a FLIR VUE 512 thermal imager, and the spectral reflectance of the soil was measured. According to the results of the correlation analysis, it was found that the closest correlations for thermal images are observed with the following parameters of soil fertility: the content of humus, nitrogen, exchangeable magnesium and potassium. The correlation coefficient between the humus content and the reflectance in the visible and near IR-regions, as well as with the average value of the reflectance in thermal band exceeds 0.81. In different diapasons of the visible spectrum, the spectral reflectance correlation with the content of exchangeable magnesium and potassium is lower than in the thermal band, where the correlation coefficient with the content of exchangeable magnesium is 0.81, and with the content of exchangeable potassium is 0.65. Power regression equations were constructed for detecting such soil fertility parameters as humus content (R2 = 0.74), exchangeable potassium content (R2 = 0.68), and exchangeable magnesium content (R2 = 0.72) by reflection in the thermal band of the spectrum. The regressions obtained with the thermal imager data and with the spectral reflectance data in the visible and near IR-bands are similar in quality for detecting humus and exchangeable potassium content, while for detecting exchangeable magnesium content they are a bit higher. The obtained results show that thermal infrared images are applicable for detecting the most significant parameters of soil fertility in the test field and can be used as a basis for their real-time remote sensing monitoring

A new method for the identification of archaeological soils by their spectral signatures in the vis-NIR region

This paper introduces a statistical method to identify spectral signatures of buried archaeological remains and distinguish them from spectra of the background soil in the visible to near infrared region. The proposed method is based on the Principal Component Analysis (PCA). The difference between an archaeological spectrum and non-archaeological soil spectra is quantified by a so-called R value. R values larger than 1 indicate that the spectrum represents an archaeological material. The method is successfully applied to samples from five study sites in Italy and Hungary with special conditions. The reflection spectra are taken in a time-efficient way with a field spectrometer. The method works best if background non-archaeological soil spectra are gathered from the same area, around the targeted archaeological site. Also, it can work without such local background spectra (but with lower accuracy) using background spectra from existing spectral libraries. This indicates that the method can, in principle, be applied to any archaeological site which is spectrally distinct from its surroundings. The paper highlights that this method does not require high spectral resolution and thus has the advantage of using low spectral resolution spectrometers which can eventually be applied for continuous 2D imaging applications with high temporal resolution. Additional studies are needed to further improve the method and to investigate under which conditions it works well or only with limited accuracy

Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy

Soil organic carbon (SOC) is an important soil property that has profound impact on soil quality and plant growth. With 140 soil samples collected from Ebinur Lake Wetland National Nature Reserve, Xinjiang Uyghur Autonomous Region of China, this research evaluated the feasibility of visible/near infrared (VIS/NIR) spectroscopy data (350–2,500 nm) and simulated EO-1 Hyperion data to estimate SOC in arid wetland regions. Three machine learning algorithms including Ant Colony Optimization-interval Partial Least Squares (ACO-iPLS), Recursive Feature Elimination-Support Vector Machine (RF-SVM), and Random Forest (RF) were employed to select spectral features and further estimate SOC. Results indicated that the feature wavelengths pertaining to SOC were mainly within the ranges of 745–910 nm and 1,911–2,254 nm. The combination of RF-SVM and first derivative pre-processing produced the highest estimation accuracy with the optimal values of Rt (correlation coefficient of testing set), RMSEt and RPD of 0.91, 0.27% and 2.41, respectively. The simulated EO-1 Hyperion data combined with Support Vector Machine (SVM) based recursive feature elimination algorithm produced the most accurate estimate of SOC content. For the testing set, Rt was 0.79, RMSEt was 0.19%, and RPD was 1.61. This practice provides an efficient, low-cost approach with potentially high accuracy to estimate SOC contents and hence supports better management and protection strategies for desert wetland ecosystems

Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP

Soil salinization is the primary obstacle to the sustainable development of agriculture and eco-environment in arid regions. The accurate inversion of the major water-soluble salt ions in the soil using visible-near infrared (VIS-NIR) spectroscopy technique can enhance the effectiveness of saline soil management. However, the accuracy of spectral models of soil salt ions turns out to be affected by high dimensionality and noise information of spectral data. This study aims to improve the model accuracy by optimizing the spectral models based on the exploration of the sensitive spectral intervals of different salt ions. To this end, 120 soil samples were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. After determining the raw reflectance spectrum and content of salt ions in the lab, the spectral data were pre-treated by standard normal variable (SNV). Subsequently the sensitive spectral intervals of each ion were selected using methods of gray correlation (GC), stepwise regression (SR) and variable importance in projection (VIP). Finally, the performance of both models of partial least squares regression (PLSR) and support vector regression (SVR) was investigated on the basis of the sensitive spectral intervals. The results indicated that the model accuracy based on the sensitive spectral intervals selected using different analytical methods turned out to be different: VIP was the highest, SR came next and GC was the lowest. The optimal inversion models of different ions were different. In general, both PLSR and SVR had achieved satisfactory model accuracy, but PLSR outperformed SVR in the forecasting effects. Great difference existed among the optimal inversion accuracy of different ions: the predicative accuracy of Ca2+, Na+, Cl−, Mg2+ and SO42− was very high, that of CO32− was high and K+ was relatively lower, but HCO3− failed to have any predicative power. These findings provide a new approach for the optimization of the spectral model of water-soluble salt ions and improvement of its predicative precision

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions

Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture

Soil organic matter (SOM) is an important parameter of soil fertility, and visible and near-infrared (VIS–NIR) spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM) in the field. Interest in using spectral classification to predict soils in the moist conditions to minimize the influence of SM is growing. The objective of this study was to investigate the transferability of two approaches, SM–based cluster method with known SM (classifying the VIS–NIR spectra into different SM clusters to develop models separately), the normalized soil moisture index (NSMI)–based cluster method with unknown SM (utilizing NSMI to indicate the SM and establish models separately), to predict SOM directly in moist soil spectra. One hundred and twenty one soil samples were collected from Central China, and eight SM levels were obtained for each sample through rewetting experiments. Their reflectance spectra and SOM concentrations were measured in the laboratory. Partial least square-support vector machine (PLS-SVM) was employed to construct SOM prediction models. Specifically, prediction models were developed for NSMI–based clusters with unknown SM data. The models were assessed through three statistics in the processes of calibration and validation: the coefficient of determination (R2), root mean square error (RMSE) and the ratio of the performance to deviation (RPD). Results showed that the variable SM led to reduced VIS–NIR reflectance nonlinearly across the entire spectral range. NSMI was an effective spectral index to indicate the SM. Classifying the VIS–NIR spectra into different SM clusters in known SM states could improve the performance of PLS-SVM models to acceptable prediction accuracies (R2cv = 0.69–0.77, RPD = 1.79–2.08). The estimation of SOM, when using the NSMI–based cluster method with unknown SM (RPD = 1.95–2.04), was similar to the use of the SM–based cluster method with known SM (RPD = 1.79–2.08). The predictive results (RPD = 1.87–2.06) demonstrated that the NSMI-–based cluster method has potential for application outside the laboratory for SOM prediction without knowing the SM explicitly, and this method is also easy to carry out and only requires spectral information

Soil moisture monitoring using Earth observation methods

Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografieFaculty of SciencePřírodovědecká fakult

Contribution to the application of near ground L-band radiometry

Premi HEMAV 2019 al millor TFGARIEL is an L-band radiometer adapted from Earth Observation satellite technology for use in terrestrial, near to ground surveys of moisture. The key technical benefits are compact size, lightweight, mobility and high pixel density (up to 1m2). This project demonstrates the capability of high spatial and temporal resolution L-Band radiometry to produce detailed soil moisture contour maps within a 1 km2 area. The study was performed prior, during and after 12 mm of rainfall to determine the soil surface absorption and adsorption behaviour in relation to surface moisture. The radiometer was equipped with photodiodes to enable the normalised difference vegetation index (NDVI) data to be extracted concurrently. Hence this is a very near ground, high resolution and high precision study of soil moisture derived from L-band emissivity. 
 The project is focused on the technology application and production of useful products in the form of moisture contour maps and vegetation detection. The radiometer functioned admirably during the consecutive test campaigns and in conditions that varied from direct sun to rain and mud. Patterns of soil moisture over time and within specific sub-areas of the field are identified and quantified. The intra-field differences appear to primarily be related to soil type and soil surface characteristics which were qualitatively assessed in this study as quantified approaches are available in empirical and theoretical studies. Average field moistures are measured daily and differentiation is made between soil types within the field. The effect of dry and moist surface emissivity on retrieved moisture is noted, as is the effect of vegetation on soil surface emissivity with the aid of the vegetation index. Comparisons are drawn to the highest resolution satellite imagery (30 m spatial, 3 day temporal) and highlight the limitations and richness of local data that is missed in relation to local soil moisture surface absorption patterns during rainfall. The radiometer is shown to achieve very high resolution and precision that is not possible from satellite or even light aircraft. Furthermore, it is shown to be able to study ground conditions when they are occluded from satellite and hence the moisture profile maps presented are unique in their detail.Award-winnin

Site-specific seeding using multi-sensor and data fusion techniques : a review

Site-specific seeding (SSS) is a precision agricultural (PA) practice aiming at optimizing seeding rate and depth, depending on the within field variability in soil fertility and yield potential. Unlike other site-specific applications, SSS was not adopted sufficiently by farmers due to some technological and practical challenges that need to be overcome. Success of site-specific application strongly depends on the accuracy of measurement of key parameters in the system, modeling and delineation of management zone maps, accurate recommendations and finally the right choice of variable rate (VR) technologies and their integrations. The current study reviews available principles and technologies for both map-based and senor-based SSS. It covers the background of crop and soil quality indicators (SQI), various soil and crop sensor technologies and recommendation approaches of map-based and sensor-based SSS applications. It also discusses the potential of socio-economic benefits of SSS against uniform seeding. The current review proposes prospective future technology synthesis for implementation of SSS in practice. A multi-sensor data fusion system, integrating proper sensor combinations, is suggested as an essential approach for putting SSS into practice