This letter presents a modification to the established Fraunhofer line discrimination (FLD) method for improving the accuracy of the solar-induced chlorophyll fluorescence (ChF) retrieval over terrestrial vegetation. The FLD method relies on the decoupling of reflected and ChF emitted radiation by the evaluation of measurements inside and outside the absorption bands. The improved FLD method introduces two correction coefficients that relate the values of the fluorescence and the reflectance inside and outside the absorption band. The new method uses the full spectral information around the absorption band to derive these coefficients. A sensitivity analysis has been performed to evaluate the impact of the correction coefficients on the accuracy of the ChF estimation. The new formulation has been tested for the O₂ A-band on synthetic data obtaining lower errors in comparison to the standard FLD and has been successfully applied to real measurements at canopy level.

The exploitation of Earth observation satellite images acquired by optical instruments requires an automatic and accurate cloud detection. Multitemporal approaches to cloud detection are usually more powerful than their single scene counterparts since the presence of clouds varies greatly from one acquisition to another whereas surface can be assumed stationary in a broad sense. However, two practical limitations usually hamper their operational use: the access to the complete satellite image archive and the required computational power. This work presents a cloud detection and removal methodology implemented in the Google Earth Engine (GEE) cloud computing platform in order to meet these requirements. The proposed methodology is tested for the Landsat-8 mission over a large collection of manually labeled cloud masks from the Biome dataset. The quantitative results show state-of-the-art performance compared with mono-temporal standard approaches, such as FMask and ACCA algorithms, yielding improvements between 4–5% in classification accuracy and 3–10% in commission errors. The algorithm implementation within the Google Earth Engine and the generated cloud masks for all test images are released for interested readers.

In order to estimate vegetation photosynthesis from remote sensing observations; some critical parameters need to be quantified. From all absorbed light; the plant needs to release any excess that is not used for photosynthesis; by non-photochemical quenching; by fluorescence emission and unregulated thermal dissipation. Non-photochemical quenching (NPQ) processes are controlled photoprotective mechanisms which; once activated; strongly control the dynamics of photochemical efficiency. With illumination conditions increasing and decreasing during a diurnal cycle; photoprotection mechanisms needs to change accordingly. The goal of this work is to quantify dynamic NPQ; measured from active fluorescence measurements; based on passive proximal sensing leaf measurements. During a 22-day controlled light and water stress experiment on a tobacco (Nicotiana tabacum L.) leaf we measured the diurnal dynamics of passive fluorescence (Chl F); the Photochemical Reflectance Index (PRI); the Absorbed Photosynthetically Active Radiation (APAR) and leaf temperature in combination with the actively retrieved non-photochemical quenching (NPQ) parameter. Based on a bi-linear combination of diurnal APAR and PRI (plane fit model) we succeeded to estimate NPQ with a RMSE of 0.08. The simple plane fit model estimation represents well the diurnal NPQ dynamics; except for the high light stress phase; when additional reversible photoinhibition processes took place. The present works presents a way of determining NPQ from passive remote sensing measurements; as a necessary step towards estimating photosynthetic rate.