Article
Environmental Sciences
A. Hornero, P. J. Zarco-Tejada, J. L. Quero, P. R. J. North, F. J. Ruiz-Gomez, R. Sanchez-Cuesta, R. Hernandez-Clemente
Summary: This study established a classification model using spectral and thermal imagery, together with a 3-D radiative transfer model, which achieved up to 82% accuracy in detecting holm oak decline, allowing early identification up to two years in advance.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Biodiversity Conservation
James Bramich, Christopher J. S. Bolch, Andrew Fischer
Summary: This study validates an improved version of a semi-analytical chl-a retrieval algorithm using Sentinel 2 platform, which includes a red-edge band at 704 nm and offers higher spatial resolution than other satellite platforms. By replacing a fixed chl-a specific absorption coefficient with a variable model, the algorithm achieved better performance in predicting chl-a concentrations, demonstrating a significant reduction in error and bias compared to the default algorithm.
ECOLOGICAL INDICATORS
(2021)
Article
Geography, Physical
Kai Zhu, Jinghua Chen, Shaoqiang Wang, Hongliang Fang, Bin Chen, Leiming Zhang, Yuelin Li, Chen Zheng, Muhammad Amir
Summary: This study developed a vertically layered hyperspectral system and proposed an innovative framework for assessing vertical characteristics of SIF and improving the estimation of GPP based on SIF in a subtropical evergreen forest. The study found that parameters of the adjacent vertical layer mainly influenced the vertical characteristics of SIF, except for the effects of LAI on the observed understory SIFU. Furthermore, substituting the observed SIFTOC with the total emitted SIF can enhance the correlation between GPP and SIF.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2023)
Article
Ecology
Prachi Singh, Prashant K. Srivastava, Jochem Verrelst, R. K. Mall, Juan Pablo Rivera, Vikas Dugesar, Rajendra Prasad
Summary: Forests are vital for the ecosystem as they affect primary productivity, biological cycles, and provide habitat for flora and fauna. In order to effectively monitor various physiological activities like photosynthesis and respiration in forest canopies, accurate information about biochemical variables like Leaf Chlorophyll Content (LCC) is crucial. This study focused on optimizing the inversion of LCC using a Radiative Transfer Model (RTM) and evaluating different cost functions (CFs) to improve accuracy. The Bhattacharyya divergence, an information measure, performed the best in LCC inversion, with high R2, low RMSE, and low NRMSE during validation. The optimized inversion strategy was then used for detailed mapping of forest LCC using UAV-acquired multispectral imagery.
ECOLOGICAL INFORMATICS
(2023)
Article
Biotechnology & Applied Microbiology
Malini Roy Choudhury, Jack Christopher, Sumanta Das, Armando Apan, Neal W. Menzies, Scott Chapman, Vincent Mellor, Yash P. Dang
Summary: Plants grown on sodic soils may suffer from macronutrient deficiencies, which can affect their health and growth. This study proposes a novel approach using hyperspectral sensing to determine macronutrient and chlorophyll variations/deficiencies of different wheat genotypes grown under sodic soil conditions. The results demonstrate that hyperspectral sensing can efficiently detect plant macronutrient and chlorophyll concentrations.
ENVIRONMENTAL TECHNOLOGY & INNOVATION
(2022)
Article
Environmental Sciences
Shiyun Yin, Kai Zhou, Lin Cao, Xin Shen
Summary: This study combined LiDAR and hyperspectral images obtained by a UAV with the PROSAIL model to accurately assess the distribution characteristics of pigment content in Ginkgo canopies. A new approach developed in this study showed significantly higher accuracies compared to traditional vegetation index models.
Article
Environmental Sciences
Nikola Gizdavec, Mateo Gasparovic, Slobodan Miko, Borna Luzar-Oberiter, Nikolina Ilijanic, Zoran Peh
Summary: This study explores the potential use of Sentinel-2A imagery for rock unit determination in the Croatian karst region and successfully demonstrates the enhancement of existing geological maps and mineral resources exploration through the use of spectral signature data.
Article
Environmental Sciences
Quanjun Jiao, Qi Sun, Bing Zhang, Wenjiang Huang, Huichun Ye, Zhaoming Zhang, Xiao Zhang, Binxiang Qian
Summary: Canopy chlorophyll content (CCC) is an important indicator for monitoring crop growth and estimating crop productivity. This study proposes an improved CCC retrieval method by combining the PROSAIL model and machine learning algorithms, using crop-specific leaf angle information. The results show that this method can improve the accuracy of CCC retrieval for different crops.
Article
Environmental Sciences
Weihua Chen, Jie Pan, Yulin Sun
Summary: This study explores the fusion method of GF-5 and Sentinel-2A images and achieves tree species classification using a random forest classifier. The results show that the fused image has higher spatial integration and spectral fidelity, and the classification accuracy is significantly improved.
Article
Remote Sensing
Achraf Makhloufi, Abdelaziz Kallel, Rayda Chaker, Jean-Philippe Gastellu-Etchegorry
Summary: This study aims to estimate and monitor the biophysical properties of olive trees in super-intensive groves using innovative forward/backward radiative transfer modeling. The model utilizes the DART model to simulate realistic olive tree mock-ups with high accuracy and neural networks for property estimation. The dataset covers various biophysical and structural properties of olive trees to ensure accurate retrieval and validation is done using in-situ measurements.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2021)
Article
Astronomy & Astrophysics
Lingzhi Sun, Paul G. Lucey, Casey Honniball, Macey Sandford, Emily S. Costello, Liliane Burkhard, Reilly Brennan, Chiara Ferrari-Wong
Summary: The study analyzed polarized spectra of eight Apollo soils, finding a linear correlation between reflectance and the difference of perpendicular and parallel branches, and inferring a connection to grain size, compositions, and maturity of lunar soils.
Article
Geography, Physical
Mahlatse Kganyago, Georgios Ovakoglou, Paidamwoyo Mhangara, Clement Adjorlolo, Thomas Alexandridis, Giovanni Laneve, Juan Suarez Beltran
Summary: This study aimed to evaluate the contribution of view and illumination geometries to the accuracy of retrieving BVs using the Random Forest model. The results showed that incorporating geometric covariates improved the accuracy of estimating LAI and CCC, while negligible improvements were achieved for LC ab . It is recommended to incorporate per-pixel view and illumination geometry, especially when using wide-view sensors, to improve the accuracy of retrieving BVs.
GISCIENCE & REMOTE SENSING
(2023)
Article
Environmental Sciences
Anushree Badola, Santosh K. Panda, Dar A. Roberts, Christine F. Waigl, Uma S. Bhatt, Christopher W. Smith, Randi R. Jandt
Summary: The study simulated hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the AVIRIS-NG sensor, achieving a high classification accuracy of 89% for tree species classification. The simulated hyperspectral data showed a high correlation with real AVIRIS-NG data and outperformed Sentinel-2 data in terms of accuracy. The study demonstrated the potential of generating low-cost and high-quality hyperspectral data from Sentinel-2 data for improved land cover and vegetation mapping in boreal forests.
Article
Remote Sensing
Katarzyna Kubiak, Jan Kotlarz, Marcin Spiralski, Jakub Szymanski
Summary: This study examines the differences in UV/VIS/NIR spectral signatures of maize leaves under various fertilization rates and develops remote sensing indicators to determine the fertilization level.
REMOTE SENSING LETTERS
(2023)
Article
Remote Sensing
Sheng Wang, Kaiyu Guan, Zhihui Wang, Elizabeth A. Ainsworth, Ting Zheng, Philip A. Townsend, Nanfeng Liu, Emerson Nafziger, Michael D. Masters, Kaiyuan Li, Genghong Wu, Chongya Jiang
Summary: The study shows that using hyperspectral imaging can accurately estimate critical crop traits, such as nitrogen, chlorophyll, and photosynthetic capacity, and evaluate the impact of nitrogen deficiency on crop yield. By combining process-based and data-driven approaches, it is possible to predict crop traits more effectively, facilitating precision agricultural management.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Environmental Sciences
A. Hornero, P. R. J. North, P. J. Zarco-Tejada, U. Rascher, M. P. Martin, M. Migliavacca, R. Hernandez-Clemente
Summary: A major international effort has been made to monitor sun-induced chlorophyll fluorescence (SIF) from space, however, the effect of spatial heterogeneity on SIF retrievals remains uncharacterized. This study demonstrates that considering the contribution of understory vegetation significantly improves the calculation of SIF within heterogeneous canopies.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Environmental Sciences
A. Hornero, P. J. Zarco-Tejada, J. L. Quero, P. R. J. North, F. J. Ruiz-Gomez, R. Sanchez-Cuesta, R. Hernandez-Clemente
Summary: This study established a classification model using spectral and thermal imagery, together with a 3-D radiative transfer model, which achieved up to 82% accuracy in detecting holm oak decline, allowing early identification up to two years in advance.
REMOTE SENSING OF ENVIRONMENT
(2021)
Article
Geochemistry & Geophysics
Maria D. Raya-Sereno, Maria Alonso-Ayuso, Jose L. Pancorbo, Jose L. Gabriel, Carlos Camino, Pablo J. Zarco-Tejada, Miguel Quemada
Summary: This study evaluated the potential of hyperspectral airborne imagers and ground-level sensors to identify nitrogen fertilizer rates and residual nitrogen effects from previous crop fertilization. Results showed that remote sensing was effective in detecting nitrogen rates in early growth stages and had potential to detect residual nitrogen in crop rotation.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Green & Sustainable Science & Technology
Chen Xu, Xianliang Zhang, Rocio Hernandez-Clemente, Wei Lu, Ruben D. Manzanedo
Summary: This study aims to improve the distribution of forest types to be more realistic and useful by considering the actual forest attributes and linking them with climate. Forest types were classified using unsupervised cluster analysis method, combining climate variables with NDVI data. The resulting forest type distribution can provide valuable information for forest managers, conservationists, and forest ecologists.
Article
Ecology
Rocio Hernandez-Clemente, Alberto Hornero
Summary: Assessing the impacts of desertification and remote sensing modeling in ecosystem change analysis are crucial, with challenges such as low vegetation signal-to-noise ratio needing to be addressed.
Article
Environmental Sciences
A. Belwalkar, T. Poblete, A. Longmire, A. Hornero, R. Hernandez-Clemente, P. J. Zarco-Tejada
Summary: Solar-induced chlorophyll fluorescence (SIF) can be used as an indicator of crop photosynthetic activity and vegetation stress. This study compares the effects of spectral resolution on SIF quantification using different hyperspectral imagers. The results show that there is a correlation between SIF quantified from narrow-band and sub-nanometer imagers, with the narrow-band imager producing larger SIF estimates. Ground-level SIF also shows strong relationships with airborne retrievals from both types of imagers. Predictive algorithms using SIF from either the narrow-band or sub-nanometer sensor perform similarly in estimating leaf nitrogen concentration, supporting the use of narrow-band resolution imagery for assessing SIF variability in plant phenotyping and precision agriculture.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Geography, Physical
A. R. Longmire, T. Poblete, J. R. Hunt, D. Chen, P. J. Zarco-Tejada
Summary: This study investigates the physiological traits of wheat using hyperspectral imaging technology and identifies the indicators most closely associated with grain protein content (GPC). The results show that the photochemical reflectance index (PRI) is consistently associated with GPC at both leaf and canopy scale. In commercial crops, the crop water stress index (CWSI) is identified as the strongest indicator of GPC under severe water stress.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2022)
Article
Ecology
R. Hernandez-Clemente, A. Hornero, V. Gonzalez-Dugo, M. Berdugo, J. L. Quero, J. C. Jimenez, F. T. Maestre
Summary: Models derived from satellite image data are needed to monitor the status of terrestrial ecosystems at a large scale. However, there is a lack of remote sensing-based approach for quantifying soil multifunctionality globally. This study aimed to develop a soil multifunctionality model using field data and remote sensing indicators (RSI) from a Landsat dataset. The results showed that a multi-variable RSI model improved the accuracy of quantifying soil multifunctionality. The correlation between RSI and soil variables varied across different RSI.
REMOTE SENSING IN ECOLOGY AND CONSERVATION
(2023)
Article
Biodiversity Conservation
Giovanni Forzieri, Loic P. Dutrieux, Agata Elia, Bernd Eckhardt, Giovanni Caudullo, Flor Alvarez Taboada, Alessandro Andriolo, Flavius Balacenoiu, Ana Bastos, Andrei Buzatu, Fernando Castedo Dorado, Lumir Dobrovolny, Mihai-Leonard Duduman, Angel Fernandez-Carrillo, Rocio Hernandez-Clemente, Alberto Hornero, Savulescu Ionut, Maria J. Lombardero, Samuli Junttila, Petr Lukes, Leonardo Marianelli, Hugo Mas, Marek Mlcousek, Francesco Mugnai, Constantin Netoiu, Christo Nikolov, Nicolai Olenici, Per-Ola Olsson, Francesco Paoli, Marius Paraschiv, Zdenek Patocka, Eduardo Perez-Laorga, Jose Luis Quero, Marius Ruetschi, Sophie Stroheker, Davide Nardi, Jan Ferencik, Andrea Battisti, Henrik Hartmann, Constantin Nistor, Alessandro Cescatti, Pieter S. A. Beck
Summary: The Database of European Forest Insect and Disease Disturbances (DEFID2) is a new database that records insect and disease disturbances in European forests, providing detailed information about these disturbances.
GLOBAL CHANGE BIOLOGY
(2023)
Article
Environmental Sciences
T. Poblete, J. A. Navas-Cortes, A. Hornero, C. Camino, R. Calderon, R. Hernandez-Clemente, B. B. Landa, P. J. Zarco-Tejada
Summary: Infection by Verticillium dahliae (Vd) and Xylella fastidiosa (Xf) poses a threat to olive and almond production globally. High-resolution hyperspectral, narrow-band multispectral, and thermal imagery can detect disease symptoms before they are visible, aiding in the differentiation of infected plants from those affected by environmental stresses. However, the feasibility of using high-resolution commercial satellite multispectral images for vascular disease detection needs further evaluation.
REMOTE SENSING OF ENVIRONMENT
(2023)
Article
Geography, Physical
A. Longmire, T. Poblete, A. Hornero, D. Chen, P. J. Zarco-Tejada
Summary: This study successfully predicted the grain protein content (GPC) of wheat using remote sensing technology and machine learning algorithms, by analyzing the effects of multiple plant traits on GPC. The results showed that the leaf area index (LAI) had a greater influence on GPC in the early stage of the growing season, while the leaf water content (C-w) had a greater influence in the later stage. The study demonstrated the potential of Sentinel-2 time series data for large-scale GPC monitoring.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2023)
Proceedings Paper
Geosciences, Multidisciplinary
A. Hornero, I. Marengo, N. Faria, R. Hernandez-Clemente
Summary: This study evaluates the application of a predictive symbolic classification model for oak decline using multispectral and thermal imagery onboard a drone, along with a 3-D radiative transfer model (RTM) approach in the southern region of Portugal. The results show that the model can efficiently detect and map the progression of the disease, which is essential for preventing the spread of oak decline.
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022)
(2022)
Article
Multidisciplinary Sciences
P. J. Zarco-Tejada, T. Poblete, C. Camino, V. Gonzalez-Dugo, R. Calderon, A. Hornero, R. Hernandez-Clemente, M. Roman-ecija, M. P. Velasco-Amo, B. B. Landa, P. S. A. Beck, M. Saponari, D. Boscia, J. A. Navas-Cortes
Summary: Plant pathogens, such as Xylella fastidiosa, are increasingly threatening global food security, causing significant yield losses. Spectral screening methods are crucial for early detection of non-visual symptoms of infection and prevention of spread. Using airborne spectroscopy, researchers have identified host-specific spectral pathways that can distinguish biotic-induced symptoms, improving detection accuracy and reducing crop losses worldwide.
NATURE COMMUNICATIONS
(2021)