Hyperspectral remote sensing applications in soil: a review

Yu, Huan; Kong, Bo; Wang, Qing; Liu, Xian; Liu, Xiangmeng

doi:10.1016/b978-0-08-102894-0.00011-5

Cited by 20 publications

(10 citation statements)

References 116 publications

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“…While multispectral imaging utilizes only a few wavelengths, hyperspectral imaging, with typically over 100 wavelengths, produces extensive datasets necessitating dimensionality reduction and machine learning for analysis. [8][9][10] When spectral imaging involves diffusely reflected light, the measured spectrum at each pixel depends on the local absorbing and scattering properties of the object. Optical properties of biological tissue components, such as oxyhemoglobin, deoxyhemoglobin, melanin, and water, are crucial for interpreting these spectra.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging at visible to near-infrared wavelengths with deep learning to rapidly quantify tissue oxygenation, blood, and melanin content

Livecchi,

Jacques,

Subhash

et al. 2024

Multiscale Imaging and Spectroscopy V

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Hyperspectral imaging can capture light reflected from tissue with high spectral and spatial resolution. Fitting algorithms can be applied to the spectrum at each pixel to estimate tissue chromophore concentrations, including blood, melanin, water, and fat. Traditional fitting methods are computationally intensive and slow when applied over an entire image. This study developed an artificial neural network (ANN) to rapidly calculate tissue oxygenation, blood, and melanin content from hyperspectral images. Linearly polarized light from a halogen lamp was delivered through a ring illuminator placed 20 cm from the tissue surface. A 1024x1224 pixel hyperspectral camera captured diffusely reflected light through an orthogonal polarizer at 299 wavelengths between 400-1000nm. To train an ANN, diffusion theory was used to generate reflectance spectra from 440-800nm for a uniform tissue containing 24,000 random combinations of physiologically relevant concentrations of oxyhemoglobin, deoxyhemoglobin, melanin, and scattering. The ANN was then tested by generating another 6,000 reflectance spectra from diffusion theory using physiological values and comparing the chromophore concentrations output by the ANN to ground truth values. The ANN demonstrated a root-mean-square error less than 0.01 in predicting each chromophore concentration from reflectance spectra simulated by diffusion theory. An in vivo finger occlusion experiment demonstrated the ability of the system to quantify changes in oxygen saturation and blood volume. This work demonstrates a new deep learning approach to rapidly process hyperspectral image data and accurately quantify tissue components.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging at visible to near-infrared wavelengths with deep learning to rapidly quantify tissue oxygenation, blood, and melanin content

Livecchi,

Jacques,

Subhash

et al. 2024

Multiscale Imaging and Spectroscopy V

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“…Recently, the potential relationship between spectral values and soil salinity has been the basic theory for estimating soil salinity by the hyperspectral technique [7][8][9]. However, mixed hyperspectral limits the accuracy of soil salt content (SSC) assessments in areas where the soil surface is partially covered with vegetation [10].…”

Section: Introductionmentioning

confidence: 99%

Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm

Cao

Yang

et al. 2023

IJERPH

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Hyperspectral technology has proven to be an effective method for monitoring soil salt content (SSC). However, hyperspectral estimation capabilities are limited when the soil surface is partially vegetated. This work aimed to (1) quantify the influences of different fraction vegetation coverage (FVC) on SSC estimation by hyperspectra and (2) explore the potential for a non-negative matrix factorization algorithm (NMF) to reduce the influence of various FVCs. Nine levels of mixed hyperspectra were measured from simulated mixed scenes, which were performed by strictly controlling SSC and FVC in the laboratory. NMF was implemented to extract soil spectral signals from mixed hyperspectra. The NMF-extracted soil spectra were used to estimate SSC using partial least squares regression. Results indicate that SSC could be estimated based on the original mixed spectra within a 25.76% FVC (R2cv = 0.68, RMSEcv = 5.18 g·kg−1, RPD = 1.43). Compared with the mixed spectra, NMF extraction of soil spectrum improved the estimation accuracy. The NMF-extracted soil spectra from FVC below 63.55% of the mixed spectra provided acceptable estimation accuracies for SSC with the lowest results of determination of the estimation R2cv = 0.69, RMSEcv = 4.15 g·kg−1, and RPD = 1.8. Additionally, we proposed a strategy for the model performance investigation that combines spearman correlation analysis and model variable importance projection analysis. The NMF-extracted soil spectra retained the sensitive wavelengths that were significantly correlated with SSC and participated in the operation as important variables of the model.

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“…Remote sensing (RS), including multispectral and hyperspectral imagery, is tested for soil properties estimation on large spatial scales [35][36][37]. In general, poorer relationships between soil properties and spectral features of satellite imaging spectroscopy as compared to the relationships between soil properties and laboratory spectroscopy are achieved due to, for example, the weather conditions, land cover, the spectral resolution of the sensors, etc.…”

Section: Introductionmentioning

confidence: 99%

Visible Near-Infrared Spectroscopy and Pedotransfer Function Well Predict Soil Sorption Coefficient of Glyphosate

et al. 2023

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The soil sorption coefficient (Kd) of glyphosate mainly controls its transport and fate in the environment. Laboratory-based analysis of Kd is laborious and expensive. This study aimed to test the feasibility of visible near-infrared spectroscopy (vis–NIRS) as an alternative method for glyphosate Kd estimation at a country scale and compare its accuracy against pedotransfer function (PTF). A total of 439 soils with a wide range of Kd values (37–2409 L kg−1) were collected from Denmark (DK) and southwest Greenland (GR). Two modeling scenarios were considered to predict Kd: a combined model developed on DK and GR samples and individual models developed on either DK or GR samples. Partial least squares regression (PLSR) and artificial neural network (ANN) techniques were applied to develop vis–NIRS models. Results from the best technique were validated using a prediction set and compared with PTF for each scenario. The PTFs were built with soil texture, OC, pH, Feox, and Pox. The ratio of performance to interquartile distance (RPIQ) was 1.88, 1.70, and 1.50 for the combined (ANN), DK (ANN), and GR (PLSR) validation models, respectively. vis–NIRS obtained higher predictive ability for Kd than PTFs for the combined dataset, whereas PTF resulted in slightly better estimations of Kd on the DK and GR samples. However, the differences in prediction accuracy between vis–NIRS and PTF were statistically insignificant. Considering the multiple advantages of vis–NIRS, e.g., being rapid and non-destructive, it can provide a faster and easier alternative to PTF for estimating glyphosate Kd.

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Hyperspectral remote sensing applications in soil: a review

Cited by 20 publications

References 116 publications

Hyperspectral imaging at visible to near-infrared wavelengths with deep learning to rapidly quantify tissue oxygenation, blood, and melanin content

Hyperspectral imaging at visible to near-infrared wavelengths with deep learning to rapidly quantify tissue oxygenation, blood, and melanin content

Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm

Visible Near-Infrared Spectroscopy and Pedotransfer Function Well Predict Soil Sorption Coefficient of Glyphosate

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