Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data.

The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically- based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C) top-of-atmosphere (TOA) reflectance data into crop traits. To achieve this, a training dataset was generated using the leaf-canopy RTM PROSAIL in combination with the atmospheric model 6SV. Gaussian process regression (GPR) retrieval models were then established for eight essential crop traits namely leaf chlorophyll content, leaf water content, leaf dry matter content, fractional vegetation cover, leaf area index (LAI), and upscaled leaf variables (i.e., canopy chlorophyll content, canopy water content and canopy dry matter content). An important pre-requisite for implementation into GEE is that the models are sufficiently light in order to facilitate efficient and fast processing. Successful reduction of the training dataset by 78% was achieved using the active learning technique Euclidean distance-based diversity (EBD). With the EBD-GPR models, highly accurate validation results of LAI and upscaled leaf variables were obtained against in situ field data from the validation study site Munich-North-Isar (MNI), with normalized root mean square errors (NRMSE) from 6% to 13%. Using an independent validation dataset of similar crop types (Italian Grosseto test site), the retrieval models showed moderate to good performances for canopy-level variables, with NRMSE ranging from 14% to 50%, but failed for the leaf-level estimates. Obtained maps over the MNI site were further compared against Sentinel-2 Level 2 Prototype Processor (SL2P) vegetation estimates generated from the ESA Sentinels' Application Platform (SNAP) Biophysical Processor, proving high consistency of both retrievals (R 2 from 0.80 to 0.94). Finally, thanks to the seamless GEE processing capability, the TOA-based mapping was applied over the entirety of Germany at 20 m spatial resolution including information about prediction uncertainty. The obtained maps provided confidence of the developed EBD-GPR retrieval models for integration in the GEE framework and national scale mapping from S2-L1C imagery. In summary, the proposed retrieval workflow demonstrates the possibility of routine processing of S2 TOA data into crop traits maps at any place on Earth as required for operational agricultural applications.

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review.

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under shortterm, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.

Hybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery.

The recently launched and upcoming hyperspectral satellite missions, featuring contiguous visible-to-shortwave infrared spectral information, are opening unprecedented opportunities for the retrieval of a broad set of vegetation traits with enhanced accuracy through novel retrieval schemes. In this framework, we exploited hyperspectral data cubes collected by the new-generation PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency to develop and test a hybrid retrieval workflow for crop trait mapping. Crop traits were mapped over an agricultural area in north-east Italy (Jolanda di Savoia, FE) using PRISMA images collected during the 2020 and 2021 vegetative seasons. Leaf chlorophyll content, leaf nitrogen content, leaf water content and the corresponding canopy level traits scaled through leaf area index were estimated using a hybrid retrieval scheme based on PROSAIL-PRO radiative transfer simulations coupled with a Gaussian processes regression algorithm. Active learning algorithms were used to optimise the initial set of simulated data by extracting only the most informative samples. The accuracy of the proposed retrieval scheme was evaluated against a broad ground dataset collected in 2020 in correspondence of three PRISMA overpasses. The results obtained were positive for all the investigated variables. At the leaf level, the highest accuracy was obtained for leaf nitrogen content (LNC: r2=0.87, nRMSE=7.5%), while slightly worse results were achieved for leaf chlorophyll content (LCC: r2=0.67, nRMSE=11.7%) and leaf water content (LWC: r2=0.63, nRMSE=17.1%). At the canopy level, a significantly higher accuracy was observed for nitrogen content (CNC: r2=0.92, nRMSE=5.5%) and chlorophyll content (CCC: r2=0.82, nRMSE=10.2%), whereas comparable results were obtained for water content (CWC: r2=0.61, nRMSE=16%). The developed models were additionally tested against an independent dataset collected in 2021 to evaluate their robustness and exportability. The results obtained (i. e., LCC: r2=0.62, nRMSE=27.9%; LNC: r2=0.35, nRMSE=28.4%; LWC: r2=0.74, nRMSE=20.4%; LAI: r2=0.84, nRMSE=14.5%; CCC: r2=0.79, nRMSE=18.5%; CNC: r2=0.62, nRMSE=23.7%; CWC: r2=0.92, nRMSE=16.6%) evidence the transferability of the hybrid approach optimised through active learning for most of the investigated traits. The developed models were then used to map the spatial and temporal variability of the crop traits from the PRISMA images. The high accuracy and consistency of the results demonstrates the potential of spaceborne imaging spectroscopy for crop monitoring, paving the path towards routine retrievals of multiple crop traits over large areas that could drive more effective and sustainable agricultural practices worldwide.

Ultrafast hot-hole injection modifies hot-electron dynamics in Au/p-GaN heterostructures.

A fundamental understanding of hot-carrier dynamics in photo-excited metal nanostructures is needed to unlock their potential for photodetection and photocatalysis. Despite numerous studies on the ultrafast dynamics of hot electrons, so far, the temporal evolution of hot holes in metal-semiconductor heterostructures remains unknown. Here, we report ultrafast (t < 200 fs) hot-hole injection from Au nanoparticles into the valence band of p-type GaN. The removal of hot holes from below the Au Fermi level is observed to substantially alter the thermalization dynamics of hot electrons, reducing the peak electronic temperature and the electron-phonon coupling time of the Au nanoparticles. First-principles calculations reveal that hot-hole injection modifies the relaxation dynamics of hot electrons in Au nanoparticles by modulating the electronic structure of the metal on timescales commensurate with electron-electron scattering. These results advance our understanding of hot-hole dynamics in metal-semiconductor heterostructures and offer additional strategies for manipulating the dynamics of hot carriers on ultrafast timescales.

Mapping landscape canopy nitrogen content from space using PRISMA data.

Satellite imaging spectroscopy for terrestrial applications is reaching maturity with recently launched and upcoming science-driven missions, e.g. PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), respectively. Moreover, the high-priority mission candidate Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is expected to globally provide routine hyperspectral observations to support new and enhanced services for, among others, sustainable agricultural and biodiversity management. Thanks to the provision of contiguous visible-to-shortwave infrared spectral data, hyperspectral missions open enhanced opportunities for the development of new-generation retrieval models of multiple vegetation traits. Among these, canopy nitrogen content (CNC) is one of the most promising variables given its importance for agricultural monitoring applications. This work presents the first hybrid CNC retrieval model for the operational delivery from spaceborne imaging spectroscopy data. To achieve this, physically-based models were combined with machine learning regression algorithms and active learning (AL). The key concepts involve: (1) coupling the radiative transfer models PROSPECT-PRO and SAIL for the generation of a wide range of vegetation states as training data, (2) using dimensionality reduction to deal with collinearity, (3) applying an AL technique in combination with Gaussian process regression (GPR) for fine-tuning the training dataset on in field collected data, and (4) adding non-vegetated spectra to enable the model to deal with spectral heterogeneity in the image. The final CNC model was successfully validated against field data achieving a low root mean square error (RMSE) of 3.4 g/m2 and coefficient of determination (R 2) of 0.7. The model was applied to a PRISMA image acquired over agricultural areas in the North of Munich, Germany. Mapping aboveground CNC yielded reliable estimates over the whole landscape and meaningful associated uncertainties. These promising results demonstrate the feasibility of routinely quantifying CNC from space, such as in an operational context as part of the future CHIME mission.

Optical Excitation of a Nanoparticle Cu/p-NiO Photocathode Improves Reaction Selectivity for CO2 Reduction in Aqueous Electrolytes.

We report the light-induced modification of catalytic selectivity for photoelectrochemical CO2 reduction in aqueous media using copper (Cu) nanoparticles dispersed onto p-type nickel oxide (p-NiO) photocathodes. Optical excitation of Cu nanoparticles generates hot electrons available for driving CO2 reduction on the Cu surface, while charge separation is accomplished by hot-hole injection from the Cu nanoparticles into the underlying p-NiO support. Photoelectrochemical studies demonstrate that optical excitation of plasmonic Cu/p-NiO photocathodes imparts increased selectivity for CO2 reduction over hydrogen evolution in aqueous electrolytes. Specifically, we observed that plasmon-driven CO2 reduction increased the production of carbon monoxide and formate, while simultaneously reducing the evolution of hydrogen. Our results demonstrate an optical route toward steering the selectivity of artificial photosynthetic systems with plasmon-driven photocathodes for photoelectrochemical CO2 reduction in aqueous media.

Exploring the spatial relationship between airborne-derived red and far-red sun-induced fluorescence and process-based GPP estimates in a forest ecosystem.

Terrestrial gross primary productivity (GPP) plays an essential role in the global carbon cycle, but the quantification of the spatial and temporal variations in photosynthesis is still largely uncertain. Our work aimed to investigate the potential of remote sensing to provide new insights into plant photosynthesis at a fine spatial resolution. This goal was achieved by exploiting high-resolution images acquired with the FLuorescence EXplorer (FLEX) airborne demonstrator HyPlant. The sensor was flown over a mixed forest, and the images collected were elaborated to obtain two independent indicators of plant photosynthesis. First, maps of sun-induced chlorophyll fluorescence (F), a novel indicator of plant photosynthetic activity, were successfully obtained at both the red and far-red peaks (r2 = 0.89 and p < 0.01, r2 = 0.77 and p < 0.01, respectively, compared to top-of-canopy ground-based measurements acquired synchronously with the overflight) over the forested study area. Second, maps of GPP and absorbed photosynthetically active radiation (APAR) were derived using a customised version of the coupled biophysical model Breathing Earth System Simulator (BESS). The model was driven with airborne-derived maps of key forest traits (i.e., leaf chlorophyll content (LCC) and leaf area index (LAI)) and meteorological data providing a high-resolution snapshot of the variables of interest across the study site. The LCC and LAI were accurately estimated (RMSE = 5.66 µg cm-2 and RMSE = 0.51 m2m-2, respectively) through an optimised Look-Up-Table-based inversion of the PROSPECT-4-INFORM radiative transfer model, ensuring the accurate representation of the spatial variation of these determinants of the ecosystem's functionality. The spatial relationships between the measured F and modelled BESS outputs were then analysed to interpret the variability of ecosystem functioning at a regional scale. The results showed that far-red F is significantly correlated with the GPP (r2 = 0.46, p < 0.001) and APAR (r2 = 0.43, p < 0.001) in the spatial domain and that this relationship is nonlinear. Conversely, no statistically significant relationships were found between the red F and the GPP or APAR (p > 0.05). The spatial relationships found at high resolution provide valuable insight into the critical role of spatial heterogeneity in controlling the relationship between the far-red F and the GPP, indicating the need to consider this heterogeneity at a coarser resolution.

Hot Hole Collection and Photoelectrochemical CO2 Reduction with Plasmonic Au/p-GaN Photocathodes.

Harvesting nonequilibrium hot carriers from plasmonic-metal nanostructures offers unique opportunities for driving photochemical reactions at the nanoscale. Despite numerous examples of hot electron-driven processes, the realization of plasmonic systems capable of harvesting hot holes from metal nanostructures has eluded the nascent field of plasmonic photocatalysis. Here, we fabricate gold/p-type gallium nitride (Au/p-GaN) Schottky junctions tailored for photoelectrochemical studies of plasmon-induced hot-hole capture and conversion. Despite the presence of an interfacial Schottky barrier to hot-hole injection of more than 1 eV across the Au/p-GaN heterojunction, plasmonic Au/p-GaN photocathodes exhibit photoelectrochemical properties consistent with the injection of hot holes from Au nanoparticles into p-GaN upon plasmon excitation. The photocurrent action spectrum of the plasmonic photocathodes faithfully follows the surface plasmon resonance absorption spectrum of the Au nanoparticles and open-circuit voltage studies demonstrate a sustained photovoltage during plasmon excitation. Comparison with Ohmic Au/p-NiO heterojunctions confirms that the vast majority of hot holes generated via interband transitions in Au are sufficiently hot to inject above the 1.1 eV interfacial Schottky barrier at the Au/p-GaN heterojunction. We further investigated plasmon-driven photoelectrochemical CO2 reduction with the Au/p-GaN photocathodes and observed improved selectivity for CO production over H2 evolution in aqueous electrolytes. Taken together, our results offer experimental validation of photoexcited hot holes more than 1 eV below the Au Fermi level and demonstrate a photoelectrochemical platform for harvesting hot carriers to drive solar-to-fuel energy conversion.

Near-Unity Absorption in van der Waals Semiconductors for Ultrathin Optoelectronics.

We demonstrate near-unity, broadband absorbing optoelectronic devices using sub-15 nm thick transition metal dichalcogenides (TMDCs) of molybdenum and tungsten as van der Waals semiconductor active layers. Specifically, we report that near-unity light absorption is possible in extremely thin (<15 nm) van der Waals semiconductor structures by coupling to strongly damped optical modes of semiconductor/metal heterostructures. We further fabricate Schottky junction devices using these highly absorbing heterostructures and characterize their optoelectronic performance. Our work addresses one of the key criteria to enable TMDCs as potential candidates to achieve high optoelectronic efficiency.

Light-Intensity Switching of Graphene/WSe2 Synaptic Devices.

2D van der Waals heterojunctions (vdWH) have emerged as an attractive platform for the realization of optoelectronic synaptic devices, which are critical for energy-efficient computing systems. Photogating induced by charge traps at the interfaces indeed results in ultrahigh responsivity and tunable photoconductance. Yet, optical potentiation and depression remain mostly modulated by gate bias, requiring relatively high energy inputs. Thus, advanced all-optical synapse switching strategies are still needed. In this work, a reversible switching between positive photoconductivity (PPC) and negative photoconductivity (NPC) is achieved in graphene/WSe2 vdWH solely through light-intensity modulation. Consequently, the graphene/WSe2 synaptic device shows tunable optical potentiation and depression behavior with an ultralow power consumption of 127 aJ. The study further unravels the complex interplay of gate bias and incident light power in determining the sign and magnitude of the photocurrent, showing the critical role of charge trapping and photogating at interfaces. Interestingly, it is found that switching between PPC to NPC can be also obtained at 0 mV drain-source voltage. Overall, the reversible potentiation/depression effect based on light intensity modulation and its combination with additional gate bias tunability is very appealing for the development of energy-efficient optical communications and neuromorphic computing.

Ultrafast hot-carrier dynamics in ultrathin monocrystalline gold.

Applications in photodetection, photochemistry, and active metamaterials and metasurfaces require fundamental understanding of ultrafast nonthermal and thermal electron processes in metallic nanosystems. Significant progress has been recently achieved in synthesis and investigation of low-loss monocrystalline gold, opening up opportunities for its use in ultrathin nanophotonic architectures. Here, we reveal fundamental differences in hot-electron thermalisation dynamics between monocrystalline and polycrystalline ultrathin (down to 10 nm thickness) gold films. Comparison of weak and strong excitation regimes showcases a counterintuitive unique interplay between thermalised and non-thermalised electron dynamics in mesoscopic gold with the important influence of the X-point interband transitions on the intraband electron relaxation. We also experimentally demonstrate the effect of hot-electron transfer into a substrate and the substrate thermal properties on electron-electron and electron-phonon scattering in ultrathin films. The hot-electron injection efficiency from monocrystalline gold into TiO2, approaching 9% is measured, close to the theoretical limit. These experimental and modelling results reveal the important role of crystallinity and interfaces on the microscopic electronic processes important in numerous applications.

Quantum-mechanical effects in photoluminescence from thin crystalline gold films.

Luminescence constitutes a unique source of insight into hot carrier processes in metals, including those in plasmonic nanostructures used for sensing and energy applications. However, being weak in nature, metal luminescence remains poorly understood, its microscopic origin strongly debated, and its potential for unraveling nanoscale carrier dynamics largely unexploited. Here, we reveal quantum-mechanical effects in the luminescence emanating from thin monocrystalline gold flakes. Specifically, we present experimental evidence, supported by first-principles simulations, to demonstrate its photoluminescence origin (i.e., radiative emission from electron/hole recombination) when exciting in the interband regime. Our model allows us to identify changes to the measured gold luminescence due to quantum-mechanical effects as the gold film thickness is reduced. Excitingly, such effects are observable in the luminescence signal from flakes up to 40 nm in thickness, associated with the out-of-plane discreteness of the electronic band structure near the Fermi level. We qualitatively reproduce the observations with first-principles modeling, thus establishing a unified description of luminescence in gold monocrystalline flakes and enabling its widespread application as a probe of carrier dynamics and light-matter interactions in this material. Our study paves the way for future explorations of hot carriers and charge-transfer dynamics in a multitude of material systems.

Understanding Wavelength-Dependent Synergies between Morphology and Photonic Design in TiO2-Based Solar Powered Redox Cells.

Solar powered redox cells (SPRCs) are promising for large-scale and long-term storage of solar-energy, particularly when coupled with redox flow batteries (RFBs). While efforts have primarily focused on heterostructure engineering, the potential of synergistic morphology and photonic design has not been carefully studied. Here, we investigate the wavelength-dependent effects of light-absorption and charge transfer characteristics on the performance of gold decorated TiO2-based SPRC photoanodes operating with RFB-compatible redox couples. Through an in-depth optical and photoelectrochemical characterization of three complementary TiO2 microstructures, namely nanotubes, honeycombs, and nanoparticles, we elucidate the combined effects of nanometer-scale semiconductor morphology and plasmonic design across the visible spectrum. In particular, thin-walled TiO2 nanotubes exhibit a ∼ 50% increase in solar-to-chemical efficiency (STC) compared to thick-walled TiO2 honeycombs thanks to improved charge transfer. Au nanoparticles both increase generation and interfacial charge transfer (above bandgap) and promote hot carrier injection (below bandgap) leading to a further 25% increase in STC. Overall, Au/TiO2 nanotubes achieve a high photocurrent at 0.098 mA/cm2 and an excellent STC of 0.06%, among the highest with respect to the theoretical limit. The incident photon to current efficiency and internal quantum efficiency are also superior to those of bare TiO2 showing maximum values of 54.7% and 67%, respectively. Overall, nanophotonic engineering that synergistically combines morphology optimization and plasmonic sensitization schemes offer new avenues for improving rechargeable solar-energy technologies such as solar redox flow batteries.

Plasmonic Nanofluids: Enhancing Photothermal Gradients toward Liquid Robots.

In situ energy generation in soft, flexible, autonomous devices is challenging due to the need for highly stretchable and fault-resistant components. Nanofluids with pyro-, tribo-, or thermoelectric properties have recently emerged as promising solutions for realizing liquid-based energy harvesters. Yet, large thermal gradients are required for the efficient performance of these systems. In this work, we show that oil-based plasmonic nanofluids uniquely combine high photothermal efficiency with strong heat localization. In particular, we report that oleic acid-based nanofluids containing TiN nanoclusters (0.3 wt %) exhibit 89% photothermal efficiency and can realize thermal gradients as large as 15.5 K/cm under solar irradiation. We experimentally and numerically investigate the photothermal behavior of the nanofluid as a function of solid fraction concentration and irradiation wavelength, clarifying the interplay of thermal and optical properties and demonstrating a dramatic improvement compared with water-based nanofluids. Overall, these results open unprecedented opportunities for the development of liquid-based energy generation systems for soft, stand-alone devices.

Transport and Interfacial Injection of d-Band Hot Holes Control Plasmonic Chemistry.

Harnessing nonequilibrium hot carriers from plasmonic metal nanostructures constitutes a vibrant research field with the potential to control photochemical reactions, particularly for solar fuel generation. However, a comprehensive understanding of the interplay of plasmonic hot-carrier-driven processes in metal/semiconducting heterostructures has remained elusive. In this work, we reveal the complex interdependence among plasmon excitation, hot-carrier generation, transport, and interfacial collection in plasmonic photocatalytic devices, uniquely determining the charge injection efficiency at the solid/liquid interface. Measuring the internal quantum efficiency of ultrathin (14-33 nm) single-crystalline plasmonic gold (Au) nanoantenna arrays on titanium dioxide substrates, we find that the performance of the device is limited by hot hole collection at the metal/electrolyte interface. Our solid- and liquid-state experimental approach, combined with ab initio simulations, demonstrates more efficient collection of high-energy d-band holes traveling in the [111] orientation, enhancing oxidation reactions on {111} surfaces. These findings establish new guidelines for optimizing plasmonic photocatalytic systems and optoelectronic devices.

Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling.

The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO) data stream requires new-generation approaches for the estimation of important biophysical crop variables (BVs). In this framework, this study evaluated a hybrid approach, combining the radiative transfer model PROSAIL-PRO and several machine learning (ML) regression algorithms, for the retrieval of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) from synthetic PRISMA data. PRISMA-like data were simulated from two images acquired by the airborne sensor HyPlant, during a campaign performed in Grosseto (Italy) in 2018. CCC and CNC estimations, assessed from the best performing ML algorithms, were used to define two relations with plant nitrogen uptake (PNU). CNC proved to be slightly more correlated to PNU than CCC (R2 = 0.82 and R2 = 0.80, respectively). The CNC-PNU model was then applied to actual PRISMA images acquired in 2020. The results showed that the estimated PNU values are within the expected ranges, and the temporal trends are compatible with plant phenology stages.

Thermally reconfigurable metalens.

Reconfigurable metalenses are compact optical components composed by arrays of meta-atoms that offer unique opportunities for advanced optical systems, from microscopy to augmented reality platforms. Although poorly explored in the context of reconfigurable metalenses, thermo-optical effects in resonant silicon nanoresonators have recently emerged as a viable strategy to realize tunable meta-atoms. In this work, we report the proof-of-concept design of an ultrathin (300 nm thick) and thermo-optically reconfigurable silicon metalens operating at a fixed, visible wavelength (632 nm). Importantly, we demonstrate continuous, linear modulation of the focal-length up to 21% (from 165 µm at 20 °C to 135 µm at 260 °C). Operating under right-circularly polarized light, our metalens exhibits an average conversion efficiency of 26%, close to mechanically modulated devices, and has a diffraction-limited performance. Overall, we envision that, combined with machine-learning algorithms for further optimization of the meta-atoms, thermally reconfigurable metalenses with improved performance will be possible. Also, the generality of this approach could offer inspiration for the realization of active metasurfaces with other emerging materials within field of thermo-nanophotonics.