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Advances in Remote Sensing of Vegetation Traits Retrieval Based on Hyperspectral Data Analysis

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 11175

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Special Issue Editors

Dr. Gabriele Candiani

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Dr. Mirco Boschetti

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Institute for the Electromagnetic Sensing of Environment, National Research Council, Via Corti 12, 20133 Milan, Italy
Interests: optical remote sensing; imaging spectroscopy; vegetation properties retrieval; radiative transfer models; agricultural monitoring and digital application
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Dr. Jochem Verrelst

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Laboratory for Earth Observation, Image Processing Laboratory—Scientific Park, University of Valencia, C/ Catedrático José Beltrán, 2, 46980 Paterna, Valencia, Spain
Interests: imaging spectroscopy; vegetation properties retrieval; FLEX, vegetation fluorescence; optical remote sensing; radiative transfer models; retrieval methods
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Dr. Katja Berger

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1. Laboratory for Earth Observation, Image Processing Laboratory - Scientific Park, University of Valencia, C/ Catedrático José Beltrán, 2, 46980 Paterna, Valencia, Spain
2. Mantle Labs GmbH, Vienna, Austria
Interests: agriculture; hybrid retrieval; hyperspectral remote sensing; machine learning methods; active learning
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Dr. Roshanak Darvishzadeh

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Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands
Interests: remote sensing; vegetation physiological properties; imaging spectroscopy; radiative transfer models; smart agriculture; biodiversity
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Special Issue Information

Dear Colleagues,

Biophysical and biochemical vegetation traits are important variables in the field of “agriculture and food security” as well as “forestry and biodiversity assessment”. The monitoring of traits such as leaf area index, leaf/canopy chlorophyll and water contents, plant nitrogen content and leaf mass per area is fundamental to assess actual crop nutritional status, grain quality and yield in precision agriculture or to identify forest structure and quantify functional diversity in forest ecosystems. Moreover, the detection and quantification of non-photosynthetic vegetation biomass (or coverage), in agricultural fields (i.e. senescent standing-dead vegetation or crop residue) or in natural ecosystems, represent an essential information for carbon cycle assessment, providing an important contribution to the monitoring and implemention of a conservative and sustainable agriculture.

Traditionally, the estimation of such traits from remotely sensed data has been performed through either parametric and non-parametric regression methods or through physically-based methods. Recently, the hybrid approach, which includes elements of both non-parametric and physically-based methods, was introduced as a promising solution for traits estimation.

In this context, hyperspectral sensors, featuring hundreds of spectral bands in the range of 400–2500 nm, can play an important role in the estimation of vegetation traits. In the next years, several spaceborne hyperspectral missions (e.g. ESA CHIME, NASA SBG) will be launched, following precursor missions such as PRISMA and EnMap, providing an unprecedented amount of hyperspectral data which will allow the assessment of specific vegetation traits at a higher accuracy with respect to multi-spectral systems. Moreover, the availability of miniaturized hyperspectral sensors opens a new era for UAV imaging spectroscopy, which can be exploited in forestry monitoring, high throughput phenotyping (HTP) and precision farming applications.

Despite the good results provided by the scientific literature, several issues still remain open. These issues include points related to which approach is better for spectral and/or sampling dimensionality reduction, which retrieval algorithm and feature selection approach (e.g. full spectrum, expert/physically-based band selection, automatic band selection, PCA) is more suited for each variable or how much a developed approach is transferable to other contexts, across different years, sites and crop/forest types. This special issue aims at addressing some of these challenges, related to the retrieval of vegetation traits from hyperspectral data.

Original research articles, review articles, short communications or technical notes are welcome. Research topics may include (but are not limited to) the following:

Methods

new methodologies for the estimation of biophysical and biochemical vegetation traits from hyperspectral sensors
assessment of the impact of spectral/sampling dimensionality reduction (e.g. full spectrum, expert /physically based band selection, automatic band selection, PCA) for specific traits
performance comparison of different algorithms/methods/approaches for the estimation of vegetation traits
evaluation of algorithms/methods/approaches transferability on independent data set in different conditions (i.e. across different years, sites and crop types), e.g. through analysis of uncertainties

Applications

Prototype products from hyperspectral data for agriculture and forestry monitoring
Exploitation of products from hyperspectral data in precision agriculture workflows
UAV/aerial hyperspectral data for high throughput phenotyping application and evaluation of the scaling-up to satellite level of developed methods
Analysis of hyperspectral imagery for forest biodiversity assessment
NPV detection and quantification in natural and agricultural environment for land use monitoring and carbon cycle assessment

Dr. Gabriele Candiani
Dr. Mirco Boschetti
Dr. Jochem Verrelst
Dr. Katja Berger
Dr. Roshanak Darvishzadeh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Keywords

hyperspectral sensors (satellite, aerial and UAV)
vegetation biophysical parameters
agriculture and food security
forestry and biodiversity
radiative transfer modeling
deep learning and/or machine learning
hybrid approach
precision farming
non-photosynthetic vegetation and carbon cycle
high throughput phenotyping

Published Papers (9 papers)

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Research

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21 pages, 28215 KiB

Open AccessArticle

Spatial Resolution Enhancement of Vegetation Indexes via Fusion of Hyperspectral and Multispectral Satellite Data

by Luciano Alparone, Alberto Arienzo and Andrea Garzelli

Remote Sens. 2024, 16(5), 875; https://doi.org/10.3390/rs16050875 - 01 Mar 2024

Viewed by 716

Abstract

The definition and calculation of a spectral index suitable for characterizing vegetated landscapes depend on the number and widths of the bands of the imaging instrument. Here, we point out the advantages of performing the fusion of hyperspectral (HS) satellite data with the multispectral (MS) bands of Sentinel-2 to calculate such vegetation indexes as the normalized area over reflectance curve (NAOC) and the red-edge inflection point (REIP), which benefit from the availability of quasi-continuous pixel spectra. Unfortunately, MS data may be acquired from satellite platforms with very high spatial resolution; HS data may not. Despite their excellent spectral resolution, satellite imaging spectrometers currently resolve areas not greater than 30 × 30 m², where different thematic classes of landscape may be mixed together to form a unique pixel spectrum. A way to resolve mixed pixels is to perform the fusion of the HS dataset with the same dataset produced by an MS scanner that images the same scene with a finer spatial resolution. The HS dataset is sharpened from 30 m to 10 m by means of the Sentinel-2 bands that have all been previously brought to 10 m. To do so, the hyper-sharpening protocol, that is,

m : n

fusion, is exploited in two nested steps: the first one to bring the 20 m bands of Sentinel-2 all to 10 m, the second one to sharpen all the 30 m HS bands to 10 m by using the Sentinel-2 bands previously hyper-sharpened to 10 m. Results are presented on an agricultural test site in The Netherlands imaged by Sentinel-2 and by the satellite imaging spectrometer recently launched as a part of the environmental mapping and analysis program (EnMAP). Firstly, the excellent match of statistical consistency of the fused HS data to the original MS and HS data is evaluated by means of analysis tools, existing and developed ad hoc for this specific case. Then, the spatial and radiometric accuracy of REIP and NAOC calculated from fused HS data are analyzed on the classes of pure and mixed pixels. On pure pixels, the values of REIP and NAOC calculated from fused data are consistent with those calculated from the original HS data. Conversely, mixed pixels are spectrally unmixed by the fusion process to resolve the 10 m scale of the MS data. How the proposed method can be used to check the temporal evolution of vegetation indexes when a unique HS image and many MS images are available is the object of a final discussion. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Vegetation Traits Retrieval Based on Hyperspectral Data Analysis)

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0 pages, 4813 KiB

Open AccessArticle

Grassland Chlorophyll Content Estimation from Drone Hyperspectral Images Combined with Fractional-Order Derivative

by Aiwu Zhang, Shengnan Yin, Juan Wang, Nianpeng He, Shatuo Chai and Haiyang Pang

Remote Sens. 2023, 15(23), 5623; https://doi.org/10.3390/rs15235623 - 04 Dec 2023

Viewed by 1038

Abstract

Chlorophyll plays a critical role in assessing the photosynthetic capacity and health of grasslands. However, existing studies on the hyperspectral inversion of chlorophyll have mainly focused on field crops, leading to limited accuracy when applied to natural grasslands due to their complex canopy structures and species diversity. This study aims to address this challenge by extrapolating the measured leaf chlorophyll to the canopy level using the green vegetation coverage approach. Additionally, fractional-order derivative (FOD) methods are employed to enhance the sensitivity of hyperspectral data to chlorophyll. Several FOD spectral indices are developed to minimize interference from factors such as bare soil and hay, resulting in improved chlorophyll estimation accuracy. The study utilizes partial least squares regression (PLSR) and support vector machine regression (SVR) to construct inversion models based on full-band FOD, two-band FOD spectral indices, and their combination. Through comparative analysis, the optimal model for estimating grassland chlorophyll content is determined, yielding an R² value of 0.808, RMSE value of 1.720, and RPD value of 2.347. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Vegetation Traits Retrieval Based on Hyperspectral Data Analysis)

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16 pages, 2901 KiB

Open AccessArticle

Full-Season Crop Phenology Monitoring Using Two-Dimensional Normalized Difference Pairs

by Louis Longchamps and William Philpot

Remote Sens. 2023, 15(23), 5565; https://doi.org/10.3390/rs15235565 - 30 Nov 2023

Viewed by 840

Abstract

The monitoring of crop phenology informs decisions in environmental and agricultural management at both global and farm scales. Current methodologies for crop monitoring using remote sensing data track crop growth stages over time based on single, scalar vegetative indices (e.g., NDVI). Crop growth and senescence are indistinguishable when using scalar indices without additional information (e.g., planting date). By using a pair of normalized difference (ND) metrics derived from hyperspectral data—one primarily sensitive to chlorophyll concentration and the other primarily sensitive to water content—it is possible to track crop characteristics based on the spectral changes only. In a two-dimensional plot of the metrics (ND-space), bare soil, full canopy, and senesced vegetation data all plot in separate, distinct locations regardless of the year. The path traced in the ND-space over the growing season repeats from year to year, with variations that can be related to weather patterns. Senescence follows a return path that is distinct from the growth path. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Vegetation Traits Retrieval Based on Hyperspectral Data Analysis)

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23 pages, 34669 KiB

Open AccessArticle

Assessment of Combined Reflectance, Transmittance, and Absorbance Hyperspectral Sensors for Prediction of Chlorophyll a Fluorescence Parameters

by Renan Falcioni, Werner Camargos Antunes, Roney Berti de Oliveira, Marcelo Luiz Chicati, José Alexandre M. Demattê and Marcos Rafael Nanni

Remote Sens. 2023, 15(20), 5067; https://doi.org/10.3390/rs15205067 - 22 Oct 2023

Viewed by 1336

Abstract

Photosynthesis is a key process in plant physiology. Understanding its mechanisms is crucial for optimizing crop yields and for environmental monitoring across a diverse range of plants. In this study, we employed reflectance, transmittance, and absorbance hyperspectral sensors and utilized multivariate statistical techniques to improve the predictive models for chlorophyll a fluorescence (ChlF) parameters in Hibiscus and Geranium model plants. Our objective was to identify spectral bands within hyperspectral data that correlate with ChlF indicators using high-resolution data spanning the electromagnetic spectrum from ultraviolet to shortwave infrared (UV–VIS–NIR–SWIR). Utilizing the hyperspectral vegetation indices (HVIs) tool to align importance projection for wavelength preselection and select the most responsive wavelength by variable importance projection (VIP), we optimized partial least squares regression (PLSR) models to enhance predictive accuracy. Our findings revealed a strong relationship between hyperspectral sensor data and ChlF parameters. Employing principal component analysis, kappa coefficients (k), and accuracy (Acc) evaluations, we achieved values exceeding 86% of the predicted ChlF parameters for both Hibiscus and Geranium plants. Regression models for parameters such as Ψ(EO), ϕ(PO), ϕ(EO), ϕ(DO), δRo, ρRo, Kn, Kp, SFI(abs), PI(abs), and D.F. demonstrated model accuracies close to 0.84 for R² and approximately 1.96 for RPD. The spectral regions linked with these parameters included blue, green, red, infrared, SWIR1, and SWIR2, emphasizing their relevance for noninvasive evaluations. This research demonstrates the ability of hyperspectral sensors to noninvasively predict chlorophyll a fluorescence (ChlF) parameters, which are essential for assessing photosynthetic efficiency in plants. Notably, hyperspectral absorbance data were more accurate in predicting JIP-test-based chlorophyll a kinetic parameters. In conclusion, this study underscores the potential of hyperspectral sensors for deepening our understanding of plant photosynthesis and monitoring plant health. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Vegetation Traits Retrieval Based on Hyperspectral Data Analysis)

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19 pages, 5180 KiB

Open AccessArticle

Self-Supervised Convolutional Neural Network Learning in a Hybrid Approach Framework to Estimate Chlorophyll and Nitrogen Content of Maize from Hyperspectral Images

by Ignazio Gallo, Mirco Boschetti, Anwar Ur Rehman and Gabriele Candiani

Remote Sens. 2023, 15(19), 4765; https://doi.org/10.3390/rs15194765 - 28 Sep 2023

Cited by 1 | Viewed by 974

Abstract

The new generation of available (i.e., PRISMA, ENMAP, DESIS) and future (i.e., ESA-CHIME, NASA-SBG) spaceborne hyperspectral missions provide unprecedented data for environmental and agricultural monitoring, such as crop trait assessment. This paper focuses on retrieving two crop traits, specifically Chlorophyll and Nitrogen content at the canopy level (CCC and CNC), starting from hyperspectral images acquired during the CHIME-RCS project, exploiting a self-supervised learning (SSL) technique. SSL is a machine learning paradigm that leverages unlabeled data to generate valuable representations for downstream tasks, bridging the gap between unsupervised and supervised learning. The proposed method comprises pre-training and fine-tuning procedures: in the first stage, a de-noising Convolutional Autoencoder is trained using pairs of noisy and clean CHIME-like images; the pre-trained Encoder network is utilized as-is or fine-tuned in the second stage. The paper demonstrates the applicability of this technique in hybrid approach methods that combine Radiative Transfer Modelling (RTM) and Machine Learning Regression Algorithm (MLRA) to set up a retrieval schema able to estimate crop traits from new generation space-born hyperspectral data. The results showcase excellent prediction accuracy for estimating CCC (R2 = 0.8318; RMSE = 0.2490) and CNC (R2 = 0.9186; RMSE = 0.7908) for maize crops from CHIME-like images without requiring further ground data calibration. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Vegetation Traits Retrieval Based on Hyperspectral Data Analysis)

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18 pages, 6052 KiB

Open AccessArticle

Carotenoid Content Estimation in Tea Leaves Using Noisy Reflectance Data

by Rei Sonobe and Yuhei Hirono

Remote Sens. 2023, 15(17), 4303; https://doi.org/10.3390/rs15174303 - 31 Aug 2023

Viewed by 864

Abstract

Quantifying carotenoid content in agriculture is essential for assessing crop nutritional value, improving crop quality, promoting human health, understanding plant stress responses, and facilitating breeding and genetic improvement efforts. Hyperspectral reflectance imaging is a nondestructive and rapid tool for estimating the carotenoid content. In spectrometer reflectance measurements, there are various sources of noise that can compromise the accuracy of carotenoid content estimations. Recently, various machine learning algorithms have been identified as robust against various types of noise, eliminating the need for denoising processes. Specifically, Cubist and the one-dimensional convolutional neural network (1D-CNN) have been used in evaluating vegetation properties based on reflectance data. We used regression models based on Cubist and 1D-CNN to estimate carotenoid content from reflectance data (the spectral resolution was resampled in 5 nm bands across the entire wavelength domain from 400 to 850 nm) with various degrees of Gaussian and spike noise added. The Cubist-based model was the most robust for this purpose: it achieved a ratio of performance to deviation of 1.41, a root mean square error of 1.11 µg/cm², and a coefficient of determination (R²) of 0.496 when applied to reflectance data with a combination of Gaussian (mean: 0; variance: 0.04) and spike noise (density: 0.05; amplitude: 0.05). Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Vegetation Traits Retrieval Based on Hyperspectral Data Analysis)

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17 pages, 3366 KiB

Open AccessArticle

An Approach for Joint Estimation of Grassland Leaf Area Index and Leaf Chlorophyll Content from UAV Hyperspectral Data

by Xiaohua Zhu, Qian Yang, Xinyu Chen and Zixiao Ding

Remote Sens. 2023, 15(10), 2525; https://doi.org/10.3390/rs15102525 - 11 May 2023

Cited by 5 | Viewed by 1620

Abstract

Leaf area index (LAI) and leaf chlorophyll content (Cab) are two important indicators of vegetation growth. Due to the high-coupling of spectral signals of leaf area and chlorophyll content, simultaneous retrieval of LAI and Cab from remotely sensed date is always challenging. In this paper, an approach for joint estimation of grassland LAI and Cab from unmanned aerial vehicle (UAV) hyperspectral data was proposed. Firstly, based on a PROSAIL model, 15 typical hyperspectral vegetation indices (VIs) were calculated and analyzed to identify optimal VIs for LAI and Cab estimation. Secondly, four pairs of VIs were established and their discreteness was also calculated for building a two-dimension matrix. Thirdly, a two-layer VI matrix was generated to determine the relationship of VIs with LAI values and Cab values. Finally, LAI and Cab were jointly retrieved according to the cells of the two-layer matrix. The retrieval reduced the cross-influence between LAI and Cab. Compared with the VI empirical model and the single-layer VI matrix, the accuracy of LAI and Cab retrieved from UAV hyperspectral data based on the two-layer VI matrix was significantly improved (for LAI: R² = 0.73, RMSE = 0.91 m²/m² and u(SD) = 0.82 m²/m²; for Cab: R² = 0.79, RMSE = 11.7 μg/cm² and u(SD) = 10.84 μg/cm²). The proposed method has the potential for rapid retrieval of LAI and Cab from hyperspectral data. As a method similar to look-up table, the two-layer matrix can be used directly for LAI and Cab estimation without the need for prior measurements for training. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Vegetation Traits Retrieval Based on Hyperspectral Data Analysis)

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Review

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29 pages, 5841 KiB

Open AccessReview

A Systematic Review of Radiative Transfer Models for Crop Yield Prediction and Crop Traits Retrieval

by Rana Ahmad Faraz Ishaq, Guanhua Zhou, Chen Tian, Yumin Tan, Guifei Jing, Hongzhi Jiang and Obaid-ur-Rehman

Remote Sens. 2024, 16(1), 121; https://doi.org/10.3390/rs16010121 - 27 Dec 2023

Viewed by 1007

Abstract

Radiative transfer models (RTMs) provide reliable information about crop yield and traits with high resource efficiency. In this study, we have conducted a systematic literature review (SLR) to fill the gaps in the overall insight of RTM-based crop yield prediction (CYP) and crop traits retrieval. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 76 articles were found to be relevant to crop traits retrieval and 15 for CYP. China had the highest number of RTM applications (33), followed by the USA (13). Crop-wise, cereals, and traits-wise, leaf area index (LAI) and chlorophyll, had a high number of research studies. Among RTMs, the PROSAIL model had the highest number of articles (62), followed by SCOPE (6) with PROSAIL accuracy for CYP (median R² = 0.62) and crop traits (median R² = 0.80). The same was true for crop traits retrieval with LAI (CYP median R² = 0.62 and traits median R² = 0.85), followed by chlorophyll (crop traits median R² = 0.70). Document co-citation analysis also found the relevancy of selected articles within the theme of this SLR. This SLR not only focuses on information about the accuracy and reliability of RTMs but also provides comprehensive insight towards understanding RTM applications for crop yield and traits, further exploring possibilities of new endeavors in agriculture, particularly crop yield modeling. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Vegetation Traits Retrieval Based on Hyperspectral Data Analysis)

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18 pages, 5362 KiB

Open AccessTechnical Note

Optimizing the Retrieval of Wheat Crop Traits from UAV-Borne Hyperspectral Image with Radiative Transfer Modelling Using Gaussian Process Regression

by Rabi N. Sahoo, Shalini Gakhar, Rajan G. Rejith, Jochem Verrelst, Rajeev Ranjan, Tarun Kondraju, Mahesh C. Meena, Joydeep Mukherjee, Anchal Daas, Sudhir Kumar, Mahesh Kumar, Raju Dhandapani and Viswanathan Chinnusamy

Remote Sens. 2023, 15(23), 5496; https://doi.org/10.3390/rs15235496 - 25 Nov 2023

Cited by 1 | Viewed by 1231

Abstract

The advent of high-spatial-resolution hyperspectral imagery from unmanned aerial vehicles (UAVs) made a breakthrough in the detailed retrieval of crop traits for precision crop-growth monitoring systems. Here, a hybrid approach of radiative transfer modelling combined with a machine learning (ML) algorithm is proposed for the retrieval of the leaf area index (LAI) and canopy chlorophyll content (CCC) of wheat cropland at the experimental farms of ICAR-Indian Agricultural Research Institute (IARI), New Delhi, India. A hyperspectral image captured from a UAV platform with spatial resolution of 4 cm and 269 spectral bands ranging from 400 to 1000 nm was processed for the retrieval of the LAI and CCC of wheat cropland. The radiative transfer model PROSAIL was used for simulating spectral data, and eight machine learning algorithms were evaluated for hybrid model development. The ML Gaussian process regression (GPR) algorithm was selected for the retrieval of crop traits due to its superior accuracy and lower associated uncertainty. Simulated spectra were sampled for training GPR models for LAI and CCC retrieval using dimensionality reduction and active learning techniques. LAI and CCC biophysical maps were generated from pre-processed hyperspectral data using trained GPR models and validated against in situ measurements, yielding R² values of 0.889 and 0.656, suggesting high retrieval accuracy. The normalised root mean square error (NRMSE) values reported for LAI and CCC retrieval are 8.579% and 14.842%, respectively. The study concludes with the development of optimized GPR models tailored for UAV-borne hyperspectral data for the near-real-time retrieval of wheat traits. This workflow can be upscaled to farmers’ fields, facilitating efficient crop monitoring and management. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Vegetation Traits Retrieval Based on Hyperspectral Data Analysis)

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