The seasonality of sunlight and rainfall regulates net primary production in tropical forests. Previous studies have suggested that light is more limiting than water for tropical forest productivity, consistent with greening of Amazon forests during the dry season in satellite data. We evaluated four potential mechanisms for the seasonal green-up phenomenon, including increases in leaf area or leaf reflectance, using a sophisticated radiative transfer model and independent satellite observations from lidar and optical sensors. Here we show that the apparent green up of Amazon forests in optical remote sensing data resulted from seasonal changes in near-infrared reflectance, an artefact of variations in sun-sensor geometry. Correcting this bidirectional reflectance effect eliminated seasonal changes in surface reflectance, consistent with independent lidar observations and model simulations with unchanging canopy properties. The stability of Amazon forest structure and reflectance over seasonal timescales challenges the paradigm of light-limited net primary production in Amazon forests and enhanced forest growth during drought conditions. Correcting optical remote sensing data for artefacts of sun-sensor geometry is essential to isolate the response of global vegetation to seasonal and interannual climate variability.

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2)

Terrestrial plant productivity tends to increase under increasing but non-saturating photosynthetically active solar radiation when water, temperature and nutrients are not limiting. However, studies have shown that photosynthesis can also be higher under enhanced diffuse light despite a decrease in total irradiance. Clouds and atmospheric aerosols are two important variables that determine the total and proportion of diffuse light reaching the surface and thereby the rate of photosynthesis and carbon accumulation in plants. In addition to these factors, the response of plants to diffuse radiation is also dependant on plant characteristics such as functional types, leaf physiology, leaf area, leaf inclination, canopy structure and shape (i.e. clumping). Local environmental conditions (i.e. temperature, soil moisture, vapour pressure deficit, etc.) then modulate these plant responses. Changes in solar radiation as a consequence of clouds and aerosols thus can modify the carbon balance of terrestrial ecosystems. Therefore, understanding the role of solar radiation in terrestrial carbon processes has become one of the goals in terrestrial carbon cycle studies. It can help to identify the control and mechanisms of carbon processes and determines the geographical and temporal distribution of the major pools and fluxes in the global carbon cycle. Here we review the role of clouds and aerosols in partitioning solar radiation and their interactions with carbon processes of terrestrial plants. We also focus our review on vegetation characteristics that control the impact of radiation partitioning on vegetation carbon processes and the role of modelling approach to study this impact. We identify gaps in this field of research and further propose recommendations to bridge the gap.

No abstract

Plant pathogens cause significant losses to agricultural yields and increasingly threaten food security, ecosystem integrity and societies in general. Xylella fastidiosa is one of the most dangerous plant bacteria worldwide, causing several diseases with profound impacts on agriculture and the environment. Primarily occurring in the Americas, its recent discovery in Asia and Europe demonstrates that X. fastidiosa's geographic range has broadened considerably, positioning it as a reemerging global threat that has caused socioeconomic and cultural damage. X. fastidiosa can infect more than 350 plant species worldwide, and early detection is critical for its eradication. In this article, we show that changes in plant functional traits retrieved from airborne imaging spectroscopy and thermography can reveal X. fastidiosa infection in olive trees before symptoms are visible. We obtained accuracies of disease detection, confirmed by quantitative polymerase chain reaction, exceeding 80% when high-resolution fluorescence quantified by three-dimensional simulations and thermal stress indicators were coupled with photosynthetic traits sensitive to rapid pigment dynamics and degradation. Moreover, we found that the visually asymptomatic trees originally scored as affected by spectral plant-trait alterations, developed X. fastidiosa symptoms at almost double the rate of the asymptomatic trees classified as not affected by remote sensing. We demonstrate that spectral plant-trait alterations caused by X. fastidiosa infection are detectable previsually at the landscape scale, a critical requirement to help eradicate some of the most devastating plant diseases worldwide.