Fractional abundances study of macronutrients in soil using hyperspectral remote sensing

Patel, Ajay Kumar; Ghosh, Jayanta K.; Sayyad, Sameer U.

doi:10.1080/10106049.2020.1720315

Cited by 18 publications

(7 citation statements)

References 58 publications

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“…Hyperspectral prediction models established using RF and ANN algorithms could estimate the SOM content in red soil plantations. In addition, PLSR, RF, SVM, and ANN are the four modeling methods shown to have excellent performance in studies on the establishment of hyperspectral prediction models [20,51,67,68], among which PLSR is the most extensively applied linear fitting method [29]. However, the relationship between the SOM content in an area and related spectral features should be more complex than a simple linear relationship.…”

Section: Comparison Of Linear and Non-linear Modeling Algorithmsmentioning

confidence: 99%

Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm

et al. 2021

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Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Forest Farm, Guangxi, China. The Ranger and Lasso algorithms were used to screen spectral bands. Subsequently, models were established using four algorithms: partial least squares regression, random forest (RF), a support vector machine, and an artificial neural network (ANN). The optimal model was then selected. The results showed that the modeling accuracy was higher when band selection was based on the Ranger algorithm than when it was based on the Lasso algorithm. ANN modeling had the best goodness of fit, and the model established by RF had the most stable modeling results. Based on the above results, a new method is proposed in this study for band selection in the early phase of soil hyperspectral modeling. The Ranger algorithm can be applied to screen the spectral bands, and ANN or RF can then be selected to construct the prediction model based on different datasets, which is applicable to establish the prediction model of SOM content in red soil plantations. This study provides a reference for the remote sensing of soil fertility in forests of different soil types and a theoretical basis for developing portable equipment for the hyperspectral measurement of SOM content in forest habitats.

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Section: Comparison Of Linear and Non-linear Modeling Algorithmsmentioning

confidence: 99%

Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm

et al. 2021

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“…Several studies that have been conducted over the world to monitor performances of hyperspectral imagery and multispectral imagery in precision agriculture applications, notably for estimating Nutrient content in soil and crops. For instance, in a recent study, [8] used the Derivative Analysis for Spectral Unmixing (DASU) approach on hyperspectral signatures of different types of soils and found that endmember features of NPK compost and soil have diagnostic spectral absorption bands respectively. Another study [20] compared Visible and NIR spectroscopy with Landsat 8 multispectral imagery to investigate the spatial variability soil nutrients under arid climate.…”

Section: Satellite Mounted Sensorsmentioning

confidence: 99%

“…Soil NPK rates, a proxy for soil fertility [7], and their quantification is usually based on a conventional approach consisting of using punctual soil sampling and laboratory-based soil chemical analysis. However, these methods are proved to be costly and have limitation in time and space applications, especially if we plan to apply variable rate fertilization over large fields [8] and different time periods. Alternatively, previous studies have proven that multispectral and hyperspectral remote sensing datasets can be employed for successful determination and assessment of NPK.…”

Section: Introductionmentioning

confidence: 99%

“…In this context, the soil spectral signature in response to a variation of N content in soil samples is analyzed, with a consideration of the tradeoff between the spatial and spectral resolution. While some investigations examined direct soil reflectance at different wavelengths ranging in the NIR (1100-2300 nm [16,17] and up to the MIR (2500-25000 nm) [18] to the variation of soil N content, others investigated the potential of spectral derivative of soil hyperspectral signature to draw insights about soil N status [8]. In the same scope, we find works that combined the high spectral resolution of Vis-NIR spectroscopy signal, with the high spatial resolution and coverage of satellite imagery to map the spatial variability of soil in N [19,20].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Sensors Remote Sensing Applications for Assessing, Monitoring, and Mapping NPK Content in Soil and Crops in African Agricultural Land

Misbah¹,

Laamrani²,

Khechba³

et al. 2021

Remote Sensing

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Demand for agricultural products is increasing as population continues to grow in Africa. To attain a higher crop yield while preserving the environment, appropriate management of macronutrients (i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops are of critical prominence. This paper aims to review the state of art of the use of remote sensing in soil agricultural applications, especially in monitoring NPK availability for widely grown crops in Africa. In this study, we conducted a substantial literature review of the use of airborne imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing spectral information, and advances of these applications in farming practices by the African scientific community. Here we aimed to identify knowledge gaps in this field and challenges related to the acquisition, processing, and analysis of hyperspectral imagery for soil agriculture investigations. To do so, publications over the past 10 years (i.e., 2008–2021) in hyperspectral imaging technology and applications in monitoring macronutrients status for crops were reviewed. In this study, the imaging platforms and sensors, as well as the different methods of processing encountered across the literature, were investigated and their benefit for NPK assessment were highlighted. Furthermore, we identified and selected particular spectral regions, bands, or features that are most sensitive to describe NPK content (both in crop and soil) that allowed to characterize NPK. In this review, we proposed a hyperspectral data-based research protocol to quantify variability of NPK in soil and crop at the field scale for the sake of optimizing fertilizers application. We believe that this review will contribute promoting the adoption of hyperspectral technology (i.e., imaging and spectroscopy) for the optimization of soil NPK investigation, mapping, and monitoring in many African countries.

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“…Therefore, it is vital to protect the safety of the urban soil environment by rapid and accurate monitoring of the Zn content. The traditional methods for determining heavy metal content in soil require field sampling followed by laboratory experimentation, but it is time-consuming, costly and inefficient [3,4] . Hyperspectral remote sensing technology has been applied to the prediction of heavy metal contents in soil due to the advantages of rapid, accurate, non-destructive, lower cost, and dynamic monitoring over a large area [5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Inversion and Analysis of Zinc Concentration in Urban Soil in the Urumqi City of China

Zhong,

Eziz,

Ainiwaer

et al. 2023

j. of environ. & earth. sci.

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Excessive accumulation of zinc (Zn) in urban soil can lead to environmental pollution and pose a potential threat to human health and the ecosystem. How to quickly and accurately monitor the urban soil zinc content on a large scale in real time and dynamically is crucial. Hyperspectral remote sensing technology provides a new method for rapid and nondestructive soil property detection. The main goal of this study is to find an optimal combination of spectral transformation and a hyperspectral estimation model to predict the Zn content in urban soil. A total of 88 soil samples were collected to obtain the Zn contents and related hyperspectral data, and perform 18 transformations on the original spectral data. Then, select important wavelengths by Pearson's correlation coefficient analysis (PCC) and CARS. Finally, establish a partial least squares regression model (PLSR) and random forest regression model (RFR) with soil Zn content and important wavelengths. The results indicated that the average Zn content of the collected soil samples is 60.88 mg/kg. Pearson's correlation coefficient analysis (PCC) and CARS for the original and transformed wavelengths can effectively improve the correlations between the spectral data and soil Zn content. The number of important wavelengths selected by CARS is less than the important wavelengths selected by PCC. Partial least squares regression model based on first-order differentiation of the reciprocal by CARS (CARS-RTFD-PLSR) is more stable and has the highest prediction ability (R2 = 0.937, RMSE = 8.914, MAE = 2.735, RPD = 3.985). The CARS-RTFD-PLSR method can be used as a means of prediction of Zn content in soil in oasis cities. The results of the study can provide technical support for the hyperspectral estimation of the soil Zn content.

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Fractional abundances study of macronutrients in soil using hyperspectral remote sensing

Cited by 18 publications

References 58 publications

Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm

Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm

Multi-Sensors Remote Sensing Applications for Assessing, Monitoring, and Mapping NPK Content in Soil and Crops in African Agricultural Land

Hyperspectral Inversion and Analysis of Zinc Concentration in Urban Soil in the Urumqi City of China

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