Enhancing Solar-Induced Fluorescence Interpretation: Quantifying Fractional Sunlit Vegetation Cover Using Linear Spectral Unmixing

Moncholi-Estornell, Adrián; Cendrero-Mateo, Maria Pilar; Antala, Michal; Cogliati, Sergio; Moreno, José; Van Wittenberghe, Shari

doi:10.3390/rs15174274

Cited by 6 publications

(4 citation statements)

References 52 publications

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“…For these models, we chose the bestperforming predictors: NIRv, fAPAR and B7 (red edge). Note that we decided to use fAPAR instead of fCover because they are highly correlated [60], and in this study similar results were found in both biophysical traits when describing SIF 760 spatial heterogeneity. Furthermore, fAPAR presented slightly better results than fCover for SIF 687 for ensemble decision trees and for spatial heterogeneity coefficient SCL-15 methods.…”

Section: Discussionsupporting

confidence: 65%

“…Two scene classification maps with 5 (SCL-5) and 15 classes (SCL-15) were produced using supervised and unsupervised approaches, respectively. Information from the Urban Atlas layer was used for creating a simpler SCL-5 (containing five classes defined as crops, pasture, water, forest, other) using semi-automatic classification plugin [60] on S2 bands in the QGIS environment ("QGIS Geographic Information System," 2021) (Figure 4A)). Another SCL-15 map was produced using k-means clustering on the S2 dataset in SAGA with 15 clusters (the same number of land cover types for Braccagni image as in the Urban Atlas layer).…”

Section: Spatial Heterogeneity Coefficientmentioning

confidence: 99%

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Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale

Jantol,

Prikaziuk,

Celesti

et al. 2023

Remote Sensing

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Current and upcoming Sun-Induced chlorophyll Fluorescence (SIF) satellite products (e.g., GOME, TROPOMI, OCO, FLEX) have medium-to-coarse spatial resolutions (i.e., 0.3–80 km) and integrate radiances from different sources into a single ground surface unit (i.e., pixel). However, intrapixel heterogeneity, i.e., different soil and vegetation fractional cover and/or different chlorophyll content or vegetation structure in a fluorescence pixel, increases the challenge in retrieving and quantifying SIF. High spatial resolution Sentinel-2 (S2) data (20 m) can be used to better characterize the intrapixel heterogeneity of SIF and potentially extend the application of satellite-derived SIF to heterogeneous areas. In the context of the COST Action Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), in which this study was conducted, we proposed direct (i.e., spatial heterogeneity coefficient, standard deviation, normalized entropy, ensemble decision trees) and patch mosaic (i.e., local Moran’s I) approaches to characterize the spatial heterogeneity of SIF collected at 760 and 687 nm (SIF760 and SIF687, respectively) and to correlate it with the spatial heterogeneity of selected S2 derivatives. We used HyPlant airborne imagery acquired over an agricultural area in Braccagni (Italy) to emulate S2-like top-of-the-canopy reflectance and SIF imagery at different spatial resolutions (i.e., 300, 20, and 5 m). The ensemble decision trees method characterized FLEX intrapixel heterogeneity best (R2 > 0.9 for all predictors with respect to SIF760 and SIF687). Nevertheless, the standard deviation and spatial heterogeneity coefficient using k-means clustering scene classification also provided acceptable results. In particular, the near-infrared reflectance of terrestrial vegetation (NIRv) index accounted for most of the spatial heterogeneity of SIF760 in all applied methods (R2 = 0.76 with the standard deviation method; R2 = 0.63 with the spatial heterogeneity coefficient method using a scene classification map with 15 classes). The models developed for SIF687 did not perform as well as those for SIF760, possibly due to the uncertainties in fluorescence retrieval at 687 nm and the low signal-to-noise ratio in the red spectral region. Our study shows the potential of the proposed methods to be implemented as part of the FLEX ground segment processing chain to quantify the intrapixel heterogeneity of a FLEX pixel and/or as a quality flag to determine the reliability of the retrieved fluorescence.

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Section: Discussionsupporting

confidence: 65%

Section: Spatial Heterogeneity Coefficientmentioning

confidence: 99%

Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale

Jantol,

Prikaziuk,

Celesti

et al. 2023

Remote Sensing

Self Cite

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“…Conversely, for the garden area, a building with 190 planter bags with uniform interspatial distance was modelled. These planter bags, filled with soil, were represented as rooms with a soil bed thickness of 300 mm and the foliage via plants was modelled as a roof with both shading impact and an SRI of 50 (corresponding to green) 27 .…”

Section: Methodsmentioning

confidence: 99%

Mitigating urban heat island and enhancing indoor thermal comfort using terrace garden

Visvanathan,

Patil,

Suryawanshi

et al. 2024

Sci Rep

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The United Nations advocates for sustainable urban planning and design, emphasizing green infrastructure initiatives to mitigate urban heat island effects and enhance the resilience and livability of cities globally. To address urban heat challenges, a study was conducted in Chennai, India, from April to June 2023. The study focused on assessing temperature dynamics on a building's terrace by comparing a well-maintained garden area with an exposed region. Temperature and humidity sensors were deployed in both the garden and exposed areas of the terrace, as well as within rooms beneath it, to monitor hourly temperature fluctuations. The findings indicate a significant reduction in internal room temperatures in areas with rooftop gardens, ranging from 4 to 11 °C, depending on the time of year and sun's position, compared to rooms with fully exposed roof configurations. Additionally, simulation studies were performed to validate these findings, suggesting that optimizing the distribution of soil beds and plant density across the roof could yield an additional temperature reduction of 3–4 °C, resulting in an overall difference of up to 14–15 °C. The study highlights the efficacy of rooftop gardens in providing cooling effects during daylight hours and maintaining temperature parity post-sunset. Through analysis of sensor data, the research elucidates the intricate relationship between green infrastructure and thermal comfort, offering insights for energy-efficient building design and resilient urban planning. The findings underscore the potential of rooftop gardens in fostering a more comfortable, energy-efficient, and sustainable urban living environment.

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“…HSI has many applications, such as mining [3], analyzing crops in agriculture [4,5], astroplanetary exploration [6], medical images [7], mineral exploration [8], the detection of soil-borne viruses [9] and the measurement of water quality [10]. High-spectral-resolution images often contain noise, which can significantly reduce their performance and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Weighted Group Sparse Regularized Tensor Decomposition for Hyperspectral Image Denoising

Wang,

Zhu,

Liu

et al. 2023

Applied Sciences

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Hyperspectral imaging (HSI) has been used in a wide range of applications in recent years. But in the process of image acquisition, hyperspectral images are subject to various types of noise interference. Noise reduction algorithms can be used to enhance the quality of images and make it easier to detect and analyze features of interest. To realize better image recovery, we propose a weighted group sparsity-regularized low-rank tensor ring decomposition (LRTRDGS) method for hyperspectral image recovery. Tensor ring decomposition can be utilized by this approach to investigate self-similarity and global spectral correlation. Furthermore, weighted group sparsity regularization can be employed to depict the sparsity structure of the group along the spectral dimension of the spatial difference image. Moreover, we solve the proposed model using a symmetric alternating direction method multiplier with the addition of a proximity term. The experimental data verify the effectiveness of our proposed method.

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Enhancing Solar-Induced Fluorescence Interpretation: Quantifying Fractional Sunlit Vegetation Cover Using Linear Spectral Unmixing

Cited by 6 publications

References 52 publications

Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale

Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale

Mitigating urban heat island and enhancing indoor thermal comfort using terrace garden

Weighted Group Sparse Regularized Tensor Decomposition for Hyperspectral Image Denoising

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