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Physically-based reflectance models offer a robust and transferable method to assess biophysical characteristics of vegetation in remote sensing. Forests exhibit explicit structure at many scales, from shoots and branches to landscape patches, and hence present a specific challenge to vegetation reflectance modellers. To relate forest reflectance with its structure, the complexity must be parametrised leading to an increase in the number of reflectance model inputs. The parametrisations link reflectance simulations to measurable forest variables, but at the same time rely on abstractions (e.g. a geometric surface forming a tree crown) and physically-based simplifications that are difficult to quantify robustly. As high-quality data on basic forest structure (e.g. tree height and stand density) and optical properties (e.g. leaf and forest floor reflectance) are becoming increasingly available, we used the well-validated forest reflectance and transmittance model FRT to investigate the effect of the values of the “uncertain” input parameters on the accuracy of modelled forest reflectance. With the state-of-the-art structural and spectral forest information, and Sentinel-2 Multispectral Instrument imagery, we identified that the input parameters influencing the most the modelled reflectance, given that the basic forestry variables are set to their true values and leaf mass is determined from reliable allometric models, are the regularity of the tree distribution and the amount of woody elements. When these parameters were set to their new adjusted values, the model performance improved considerably, reaching in the near infrared spectral region (740–950 nm) nearly zero bias, a relative RMSE of 13% and a correlation coefficient of 0.81. In the visible part of the spectrum, the model performance was not as consistent indicating room for improvement.

Funding Information: This work was supported by the Academy of Finland under Grant [317387]. We would like to acknowledge assistance from the University of Helsinki and Ilkka Korpela for providing us with the field measured tree data from Hyyti?l?. Publisher Copyright: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. The retrieval of forest variables from optical remote sensing data using physically-based models is an ill-posed problem and does not make full use of the high spatial resolution imagery that is becoming available globally. A possible solution to this is to use prior information about the retrieved variables, which constrains the possible solutions and reduces uncertainty in forest variable estimation. Therefore, we tried to quantify physically-based parameters that could be retrieved using the second-order statistics of measured and simulated very-high-resolution (pixel size less than 1 m) images of Finnish boreal forests. These forests have a well-defined structure and are usually not closed, i.e. the reflected signal has a considerable contribution from a green forest floor. We retrieved the second-order statistics using variograms and Fourier amplitude spectra. We found, in line with previous studies, that the range of variograms correlates well (r = 0.83) with the mean crown diameter for spatially homogeneous forest patches, and it can be used to estimate crown diameters with reasonable accuracy (RMSE = 0.42 m). We present a novel approach, which uses the Fourier amplitude spectrum to study the spatial structure of a forest. The approach provided encouraging results with the measured data: despite the lower accuracy (RMSE = 0.67 m) compared with variograms, we found that it could also be used to estimate mean crown diameters for heterogeneous forest areas. The Fourier amplitude spectrum approach did not work with the simulated images. Our results highlight the possibility to obtain further information from very-high-resolution images of forests to solve the ill-posed problem of forest variable estimation from optical remote sensing data using physically-based models.

The overall objective of the Forest Flux (forestflux.eu) project was to foster the development of the Copernicus Earth Observation (EO) market and improve the profitability of forest management by implementing a world-first service of high-resolution maps of forest carbon fluxes, storage, and their development over time, using satellite imagery and associated data. The services were offered from the same process of data refinement. In this process, the outputs of the previous phase were inputs to the next phase. The earlier phase outputs were also products that were delivered to the users. Forest Flux services were implemented on the scalable Forestry TEP cloud platform that enables integration of the Forest Flux products with the business processes of end users. The services are available via an Internet connection. Computed products were mostly digital maps with supporting quantitative figures, such as statistical data on uncertainty. They were provided for nine user organizations and sites in Europe, South America, and Africa. In total, approximately 1,200 map products were delivered. Forest Flux project received funding from the European Union's Horizon 2020 Research and Innovation program under grant agreement No 821860.

The spectral properties of plant leaves relate to the state of their photosynthetic apparatus and the surrounding environment. An example is the well known photosynthetic downregulation, active on the time scale from minutes to hours, caused by reversible changes in the xanthophyll cycle pigments. These changes affect leaf spectral absorption and are frequently quantified using the photochemical reflectance index (PRI). This index can be used to remotely monitor the photosynthetic status of vegetation, and allows for a global satellite-based measurement of photosynthesis. Such earth observation satellites in near-polar orbits usually cover the same geographical location at the same local solar time at regular intervals. To facilitate the interpretation of these instantaneous remote PRI measurements and upscale them temporally, we measured the daily course of leaf PRI in two evergreen biomes—a European boreal forest and an Amazon rainforest. The daily course of PRI was different for the two locations: At the Amazonian forest, the PRI of Manilkara elata leaves was correlated with the average photosynthetic photon flux density (PPFD) (R2 = 0.59, p < 0.01) of the 40 minutes preceding the leaf measurement. In the boreal location, the variations in Pinus sylvestris needle PRI were only weakly (R2 = 0.27, p < 0.05) correlated with mean PPFD of the preceding two hours; for Betula pendula, the correlation was insignificant (p > 0.5) regardless of the averaging period. The measured daily PRI curves were specific to species and/or environmental conditions. Hence, for a proper interpretation of satellite-measured instantaneous photosynthesis, the scaling of PRI measurements should be supported with information on its correlation with PPFD.

Timely and accurate information on forest above-ground biomass (AGB) is required for understanding carbon balance, future climate, the sustainability of current politics and the emerging bioeconomy. Optical Earth Observation (EO) in the visible to short infrared spectral region can contribute to this challenge. This is supported by the more frequent and higher quality EO data, mainly contributed by the new Sentinel-series satellites developed and launched by the European Space Agency (ESA). The new satellites deliver data with better spatial and spectral resolutions in a rapidly increasing volume, calling for new processing algorithms. Current algorithms fall short in making a full use of the higher information content of the new data, and have trouble handling the data volume produced by current and forthcoming satellite instruments. Artificial intelligence (AI) has shown promise to overcome many of the shortcomings in existing empirical methods for retrieving forest characteristics, such as its structure and biomass, from EO data. It can also handle efficiently and rapidly large data volumes. However, despite optimistic case studies, no large-scale applications using optical EO for biomass retrieval have emerged. Up to now, AI has been applied to simple canopies with little structure and no woody biomass. Recent developments in deep learning have made it feasible to utilize it for analyzing complex structured vegetation with significant AGB storage such as the boreal forest. We hypothesize that with a carefully selected approach, deep learning approaches can retrieve the forest AGB which is indirectly, but physically, related to the structural parameters affecting forest reflectance, e.g., crown volume and shape, or tree density. We propose to use a well-validated physically-based forest reflectance model to simulate the spectral reflectance factors of all possible European boreal forest canopies under different illumination conditions. The model will be used to simulate the reflectance signatures of a wide range of forests with structural parameters taken from forestry databases in Sweden, Finland, Estonia and Russia. We will use the simulated reflectance signatures, corresponding to satellite-measured signals, to train a combination of deep learning algorithms with several desired properties such as : - Recurrent Neural Networks or Long Short-Term Memory (LSTM) networks for regression - Fully Convolutional Networks (FCN) or Convolutional Neural Networks (CNN) for their ability to extract hierarchical visual features from multispectral images - semi-supervised approaches to deal with the limited availability of labelled training data - deep probabilistic approaches to provide confidence intervals on the estimated outputs Once trained, we will apply these algorithms to optical EO data from the European boreal zone, and hyperspectral data from Finland. The AI retrieval results will be compared against forestry data from test sites in each of these regions. We will also investigate the potential of Generative Adversarial Networks (GANs) to augment the available reference forest data and generate more diverse and realistic images than from simulations based solely on physical models. AIROBEST will provide a new look on our forests on a continental scale. It will deepen the apprehension of AI methods in Earth Observation and our understanding of the changes in environment. The algorithms produced in the project will be integrated with other EO-based forest monitoring tools developed at VTT, creating a capacity for comprehensive forest information retrieval for research, governmental bodies, and industry. This study is part of the AIROBEST project on Artificial Intelligence for Retrieval of Forest Biomass & Structure, funded by the Finnish Academy (grant 317387) as part of the programme on Novel Applications of Artificial Intelligence in Physical Sciences and Engineering Research (AIPSE).

The photochemical reflectance index (PRI) is calculated from vegetation narrowband reflectance in two bands in the visible part of the spectrum. Variations in PRI are associated with changes in the xanthophyll cycle pigments which regulate the light use efficiency of vegetation. Correlations have been found between remotely-sensed PRI and various photosynthetic productivity parameters at the scales from leaves to landscapes. Environmental satellites can provide only an instantaneous value of this index at the time of overpass. The diurnal course of needle (leaf) PRI needs to be known in order to link the instantaneous values robustly with photosynthetic parameters at time scales exceeding one day. This information is not currently available in the scientific literature. Here we present the daily cycle of Scots pine needle and canopy PRI in a southern boreal forest zone in the presence of direct solar radiation during the peak growing season of two consecutive years. We found the PRI of the needles which are exposed to direct radiation to have a distinct diurnal cycle with constant or slightly increasing values before noon and a daily minimum in the afternoon. The cycle in needle PRI was not correlated with that in the incident photosynthetic photon flux density (PPFD). However, when PPFD was above 1000 &mu, mol m&minus, 2 s&minus, 1, approximately between 8 a.m. and 5 p.m., needle PRI was correlated with the light use efficiency (LUE), measured with shoot chambers. The timing of the minimum needle PRI coincided with the minimum canopy value, as measured by an independent sensor above the canopy, but the correlation between the two variables was not significant. Our field results corroborate the applicability of needle PRI in monitoring the daily variation in LUE. However, to apply this to remote sensing of seasonal photosynthetic productivity, the daily cycle of leaf PRI needs to be known for the specific vegetation type.