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Beginning with a discussion of reflectance spectroscopy, this article attempts to provide a review on fundamental concepts of reflectance spectroscopic techniques. Their applications as well as exploring the role of Near-infrared reflectance spectroscopy that would be used for monitoring and mapping soil characteristics. This technique began to be...
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... study of any soil property is related to the understanding of sensitive areas at the spectrum due to presence of water. The vibrational frequencies of water molecules after 2500 nm affect the water absorption wavelengths (Baumgardner et al., 1985). The 1450 and 1950 nm wavelengths are the absorption bands with sharp peaks (Fig. 8). The broad unordered bands are more common in naturally occurring soils in addition, the highest significant vari- able in determining the reflectance located within a range 2080- 2320mm (Baumgardner et al., 1985and Galvao et al., 2001). The broad unordered bands are more common in naturally occurring soils. Furthermore, the highest significant variable in determining the reflectance changes in the 2080-2320 mm. However, other studies emphasized on the importance role of reflectance spectroscopy and remote sensing to develop spectral models for detecting soil moisture content (Ben-Dor et al., 2002;Whiting et al., ...
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Soil salinization is one of the major degradation processes threatening food security and sustainable development. Detailed soil salinity information is increasingly needed to tackle this global challenge for improving soil management. Soil-visible and near-infrared (Vis-NIR) spectros-copy has been proven to be a potential solution for estimating s...
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... According to Nocita et al. [18] these signatures are kept as references in soil spectral libraries, where they can be utilized for a range of soil research. Significant connections between soil surface and subsurface were also found when the parameters of the soil were determined using reflectance spectroscopic techniques [18][19][20][21]. Reflectance spectroscopy can also be used to infer soil classification, as it can link the reflectance spectra of each horizon in a profile to the corresponding classification. ...
Abstract: Proximal sensing has become increasingly popular due to developments in soil observation technologies and the demands of timely information gathering through contemporary methods. By utilizing the morphological, physical, and chemical characteristics of representative pedogenetic profiles established in various soils of the Sohag governorate, Egypt, the current research addresses the characterization of surface reflectance spectra and links them with the corresponding soil classification. Three primary areas were identified: recently cultivated, old cultivated, and bare soils. For morphological analysis, a total of 25 soil profiles were chosen and made visible. In the dark room, an ASD Fieldspec portable spectroradiometer (350-2500 nm) was used to measure the spectrum. Based on how similar their surface spectra were, related soils were categorized. Ward's method served as the basis for the grouping. Despite the fact that the VIS-NIR spectra of the surface soils from various land uses have a similar reflectance shape, it is still possible to compare the soil reflectance curves and the effects of the surface soils. As a result, three groups of soil curves representing various land uses were observed. Cluster analysis was performed on the reflectance data in four ranges (350-750, 751-1150, 1151-1850, and 1851-2500 nm). The groups derived from the soil surface ranges of 350-750 nm and 751-1150 nm were not the same as those derived from the ranges of 1151-1850 nm and 1851-2500 nm. The last two categories are strikingly comparable to various land uses with marginally similar features. Based on the ranges of 1151-1850 nm and 1851-2500 nm in surface spectral data, the dendrogram effectively separated and combined the profiles into two separate clusters. These clusters matched different land uses exactly. The results can be used to promote the widespread usage of in situ hyperspectral data sets for the investigation of various soil characteristics.
... 1450 nm, 1950 nm and 2500 nm), which are significantly influenced by the water absorption associated with the leaf water volume. A complex interplay of many variables, some of which are interrelated and relate to the category and growth of crop, management, ecological conditions and measurement circumstances, influences the spectral signature of the crop (Mohamed et al., 2018). The physiological development of the crop (green to old age), the fraction of nonphotosynthetic part such as ground and junk visible on a remote sensing picture, and the closure of the canopy (ground cover) are all directly determined by the growth phases of crop significant influence on the reflectivity of the crop. ...
The integration of remote sensing technology in soil analysis represents a monumental leap in agricultural sciences. By harnessing the power of satellite imagery, scientists can now monitor and evaluate soil conditions across vast areas with unprecedented accuracy. This non-invasive technique allows for the continuous observation of soil health, aiding in the detection of changes over time due to natural or anthropogenic factors. The data collected from these observations are crucial for developing sustainable farming practices that not only increase crop yields but also protect the environment. Furthermore, the ability to analyze soil properties on such a scale is instrumental in managing land resources effectively, ensuring that the delicate balance between food production and ecological stewardship is maintained. As the global population continues to grow, the role of remote sensing in soil conservation and agricultural productivity becomes ever more essential, offering a beacon of hope for future generations.
... SOC exhibits distinct absorption features in two crucial spectra, namely visible-near infrared (VNIR: 400-1300 nm) and the short-wave infrared (SWIR: 1300-2500 nm) Goetz et al., 1985). In particular, soil spectroscopy capitalizes on the association between spectra and chromophores, providing a framework for quantifying the concentration of SOC (Mohamed et al., 2018;Zhang et al., 2021). Laboratory-based soil spectroscopy has transitioned its application to proximal and remote sensing platforms, enabling the estimation of SOC at varying scale (Castaldi et al., 2018;Shi et al., 2020). ...
Predicting Soil Organic Carbon (SOC) accurately and generating SOC distribution map holds potential for assisting farmers in assessing soil fertility, optimizing and enhancing the resource use efficiency. This study used Mica Sense Red Edge sensor mounted onboard Idea forge Q4c Unmanned Aerial System (UAS) to assess the distribution of SOC in the experimental site. Random Forest (RF) and Support Vector Machine (SVM) techniques were developed with both UAS as well as Sentinel datasets for SOC prediction. Overall, the UAS dataset exhibited greater accuracy in prediction of SOC compared to Sentinel Datasets. Random forest model provided an accurate prediction of SOC when used with the UAS dataset (RPD = 1.09, R2CV = 0.25, RPIQ = 2.57 and RMSECV = 0.06), whereas the Sentinel 2A dataset provided a better prediction of SOC with SVM model (RPD = 0.96, R2CV = 0.10, RPIQ = 0.96 and RMSECV = 0.07). The prediction map of SOC was generated using the UAS dataset with the RF model because it was found to be more accurate compared to the Sentinel and SVM model. The accuracy assessment indicators indicated that UAS based SOC prediction is having the potential in achieving more accurate predictions of SOC, which will offer an optimized agricultural practice and insights for supporting informed decision-making.
... This difference can be attributed to the fact that VH is lower than VV over bare soil with low roughness, as only a limited portion of the signal is polarized and returns to the sensor. This spectral behavior aligns with the expected spectral signature of soil [68]. The interaction between incident radiation and soil particles can explain the saturation behavior observed. ...
... MARS is mainly used for regression analysis in various soil mapping studies (Jeihouni et al. 2020; Mohamed et al. 2018). It is a non-parametric algorithm that works by building a piecewise linear model that best fits the training data. ...
Around 70% of surface in Extremadura, Spain, faces a critical risk of degradation processes, highlighting the necessity for regional-scale soil property mapping to monitor degradation trends. This study aimed to generate the most reliable soil property maps, employing the most accurate methods for each case. To achieve this, six different machine learning (ML) techniques were tested to map nine soil properties across three depth intervals (0–5, 5–10 and > 10 cm). Additionally, 22 environmental covariates were utilized as inputs for model performance. Results revealed that the Random Forest (RF) model exhibited the highest precision, followed by Cubist, while Support Vector Machine showed effectiveness with limited data availability. Moreover, the study highlighted the influence of sample size on model performance. Concerning environmental covariates, vegetation indices along with selected topographic indices proved optimal for explaining the spatial distribution of soil physical properties, whereas climatic variables emerged as crucial for mapping the spatial distribution of chemical properties and key nutrients at a regional scale. Despite providing an initial insight into the regional soil property distribution using ML, future work is warranted to ensure a robust, up-to-date, and equitable database for accurate monitoring of soil degradation processes arising from various land uses.
... Infrared (IR) spectroscopy provides a potential solution to the problems of oxalate quantitation (Aleixandre-Tudo et al. 2018;Gobrecht, Roger, and Bellon-Maurel 2014). IR analysis been shown to be more cost-effective compared to other techniques (Jozanikohan and Abarghooei 2022) and minimizes sample preparation and chemical waste, especially when an attenuated total reflectance (ATR) accessory is used (Aleixandre-Tudo et al. 2018;Bushong, Norman, and Slaton 2015;Mohamed et al. 2018;Pavia et al. 2015). IR spectroscopy is suitable for the analysis of heterogeneous samples when combined with chemometric techniques such as partial least-squares regression (PLSR) (Junaedi, Lestari, and Muchtaridi 2021b;Mohamed et al. 2018;Viscarra Rossel et al. 2006). ...
... IR analysis been shown to be more cost-effective compared to other techniques (Jozanikohan and Abarghooei 2022) and minimizes sample preparation and chemical waste, especially when an attenuated total reflectance (ATR) accessory is used (Aleixandre-Tudo et al. 2018;Bushong, Norman, and Slaton 2015;Mohamed et al. 2018;Pavia et al. 2015). IR spectroscopy is suitable for the analysis of heterogeneous samples when combined with chemometric techniques such as partial least-squares regression (PLSR) (Junaedi, Lestari, and Muchtaridi 2021b;Mohamed et al. 2018;Viscarra Rossel et al. 2006). Wavenumbers of absorption peaks commonly associated with biominerals, which are mainly detected in the mid-IR (MIR: 2500-25 000 nm) region, are shown in Table 1. ...
... Multivariate analyses were conducted using OPUS 8.2 (Independent JPEG Group 2018). Models predicting oxalate components based on sample spectral measurements were calibrated by correlating IR spectroscopic data to corresponding reference data using a PLSR algorithm (Mohamed et al. 2018;Nel, Clarke, and Hardie 2023) with the QUANT2 function in OPUS 8.2 (Independent JPEG Group 2018) software. The oxalate components predicted by the models were CaOx and NaOx in solid clay samples and oxalic acid in standard solutions. ...
Quantification of oxalate salts in soil clay minerals is necessary to study oxalate biogeochemistry, but existing analytical techniques are expensive and time-consuming. We aim to develop an efficient attenuated total reflec-tance mid-infrared (MIR) spectroscopic technique to quantify oxalate salts in a clay mineral matrix. We calibrated MIR models for analysis of oxalate anion concentrations in standard solutions (0-0.01 M) by using a partial least-squares regression algorithm. MIR models were also developed for analysis of sodium oxalate (NaOx) and calcium oxalate (CaOx) content in clay mineral mixtures with composition like soils of a semi-arid region and with contrasting concentrations (0-1.0 g g −1 for both oxalate salts) to test sensitivity of analyses. Validation plots (true vs predicted values) showed excellent model fit (R 2 � 0.96) and accuracy (normalized root mean squared error of prediction � 0.06) for CaOx, NaOx and oxalic acid components. Once predictive models are stored in analytical software, MIR spectroscopic analyses of samples are much more efficient than chemical techniques. Our MIR spectral-based models are suitable for direct quantification of oxalate salts in clay mineral mixtures for samples like those used for model calibration.
... The Fraunhofer lines are dark lines that are observed in the spectrum of sunlight and are designated as C , D, and F , with each letter corresponding to a specific wavelength of light that is absorbed by elements in the Sun's atmosphere. These lines are important in astronomy and spectroscopy because they provide a reference for the identification of elements in stars and other celestial objects based on their absorption spectra [19][20][21]. ...
In this paper we design a high-performance multispectral telescope with a concave elliptical grating for a field of view of 3° in the VNIR spectral range of 0.48–0.82 µm, at an altitude of 760 km from the ground, with total length of 140 mm, which has a small volume and a simple structure. The paper reports on the MTF, spot, and field curvature diagrams, which show that it can achieve spectral and spatial resolutions of 25 nm and 5.5 m, respectively, with good image quality (MTF value for all wavelengths is higher than 0.2 at Nyquist frequency of 217 cycles per mm) and has the least possible aberrations, without the need for any lenses.
... This method allows for precise forecasting and spatial mapping at a chosen scale and high resolution, utilizing machine learning (ML) techniques and data mining algorithms [51]. Indeed, the utilization of pedometric methods, capable of predicting spatial and temporal changes in soil types and characteristics, constitutes the foundation of Digital Soil Mapping (DSM) [52]. ...
The escalating demands for food, fiber, energy, and water due to swift population growth have underscored the necessity for the sustainable utilization of natural resources. The advent of precision farming tools and machinery since the 1990s has markedly enhanced productivity and optimized the employment of inputs in aquaculture. The burgeoning connectivity in rural regions and its improved integration with data from sensor systems, remote sensors, equipment, and smartphones have paved the way for innovative concepts in Digital Aquaculture. Automation is the most effective strategy to manage situations, augment productivity, and reduce manufacturing costs. Biosensors are deployed to control unidentified sensor-based remotely and guided aerial vehicles to apply chemicals or fertilizers while monitoring water quality. A sophisticated aeration system manages the concentration of dissolved oxygen. Another critical aspect is the administration of feeding and automatic biomass estimation. Robotics and automatic feeders are employed in ponds and cages to minimize feed wastage and the Feed Conversion Ratio (FCR), with these tools being dependent on the behaviour of the organisms and the water condition. Post-harvest, farmers acquire information on biomass estimation to attain optimal yield. The most vital element is the automatic monitoring of the health and welfare management of the organism to detect any challenging situations or early signs of anomalies. An underwater surveillance system, a camera-based visual system, collects data on water quality, organism activity, feeding, cage biofouling, and net cleaning. Automation is poised to shape the future of the aquaculture industry to make the nations agriculture sustainable.
... These soils are characterized by the prevalence of salts such as magnesium chloride, enhancing their ability to absorb electromagnetic radiation. Due to their wet, sticky, and dark-colored surface, these soils exhibit low reflectance values despite the high salinity content [12], [13]. ...
The study aimed to separate soil units and predict some of their properties using geomatics techniques and spectral reflectance analysis in the northern part of Basra Governorate in southern Iraq through spectral reflectance study. Chemical properties (Ece, pH, O.C, CEC, CaCO3, ESP, CEC) and physical properties (particle size distribution) were studied, in addition to the assumed composition of prevailing salts in the study area. Three sedimentary soil units were identified (river terraces, river basins, and marshes). Furthermore, there were significant correlations between spectral reflectance of spectral bands 4 and 5 and soil organic carbon content of 0.75 and 0.8, respectively, and with other spectral bands except bands 2 and 8. There were significant relationships between other properties and different spectral bands. Notably, there were no significant correlations between pH, ESP, CaCO3, CaSO4, and all spectral bands. The most predictable soil property through spectral reflectance is the soil's organic carbon content. Bands 4 and 5 are the most commonly used in soil science, especially in agriculture.
... Multi-nutrient soil extractants have proven advantageous due to practical, budgetary, and environmental reasons (Ussiri et al. 1998;Gianello 2012, 2010). More recently alternatives such as soil sensors using spectroscopy have emerged to potentially replace these soil extractant (Mohamed et al. 2018). ...
Fertilizer recommendations (FR) to improve yields and increase profitability are based on relationships between crop yields and soil nutrient levels measured via soil extraction methods. Within these FR, critical soil nutrient (CSN) levels are used to distinguish nutrient deficient from non-deficient soils. The variation in CSN levels is large, implying a risk of over- or under-fertilization. Here, we review and assess the factors influencing the derivation of CSN levels in order to increase both their reliability and applicability within FR systems. The evaluated factors included site conditions, i.e., crop type and location as a surrogate for climate and soil properties, and methodological factors, i.e., the experimental approach (field or pot experiments), and statistical methods and cut-off point. Results showed that the range of values used to define the medium soil fertility classes coincided with the range of CSN levels derived from experimental data. We show that harmonizing methodological aspects can substantially reduce the uncertainty in the CSN levels (> 50%), implying a substantial enhancement of the reliability of FR systems. Inclusion of site conditions might further improve the reliability. To enable reduction in CSN levels requires well-documented field experiments and standardization of data collection and analysis. We foresee the potential for generic FR systems that make use of reliable data, more process-based interpretation of nutrient pools and accounting for the interactions among nutrients.
