Root systems form a significant part of tree biomass and function. Yet, roots are hidden from our eyes, making it difficult to track the belowground processes. By contrast, our capacity to detect aboveground changes in trees has been continuously improving using optical methods. Here, we tested two fundamental questions: (1) To what extent can we detect aboveground responses to mechanical damage of the root system? (2) To what extent are roots redundant? We applied three different non-destructive remote sensing means: (1) optical means to derive leaf greenness, (2) infrared means to detect the changes in leaf surface temperature and (3) spectral means to derive five vegetation indices (i.e. the photochemical reflectance index (PRI), the chlorophyll photosynthesis index (CIRed-edge), the anthocyanin reflectance index 1, the structure insensitive pigment index and the normalized difference water index (NDWI)). We recorded the above metrics for hours and days and up to a month following induced root damage in three key Mediterranean tree species: Aleppo pine (Pinus halepensis Mill.), Palestine oak (Quercus calliprinos Webb.) and Carob (Ceratonia siliqua L.). To induce root damage, we removed 25, 50 and 75 percent of the root system in each species and compared it with control saplings. Tree aboveground (canopy) responses to root damage increased over time and with damage level. Leaf warming (up to 3°C) and decreased PRI were the most significant and rapid responses, with temperature differences being visible as early as 2 days following root damage. NDWI and greenness were the least sensitive, with responses detectable only at 75 percent root damage and as late as 14 or 30 days following root damage. Responses varied vastly among species, with carob being the most sensitive and pine being the least. Changes in leaf temperature and PRI indicated that leaf transpiration and photosynthesis were impaired by root damage. Although trees build roots in excess, mechanical damage will eventually decrease transpiration and photosynthesis across tree species.

The spectral-based photochemical reflectance index (PRI) and leaf surface temperature (T leaf ) derived from thermal imaging are two indicative metrics of plant functioning. The relationship of PRI with radiation-use efficiency (RUE) and T leaf with leaf transpiration could be leveraged to monitor crop photosynthesis and water use from space. Yet, it is unclear how such relationships will change under future high carbon dioxide concentrations ([CO 2 ]) and drought. Here we established an [CO 2 ] enrichment experiment in which three wheat genotypes were grown at ambient (400 ppm) and elevated (550 ppm) [CO 2 ] and exposed to well-watered and drought conditions in two glasshouse rooms in two replicates. Leaf transpiration (T r ) and latent heat flux (LE) were derived to assess evaporative cooling, and RUE was calculated from assimilation and radiation measurements on several dates along the season. Simultaneous hyperspectral and thermal images were taken at ~1.5 m from the plants to derive PRI and the temperature difference between the leaf and its surrounding air (∆T leaf−air ). We found significant PRI and RUE and ∆T leaf−air and T r correlations, with no significant differences among the genotypes. A PRI-RUE decoupling was observed under drought at ambient [CO 2 ] but not at elevated [CO 2 ], likely due to changes in photorespiration. For a LE range of 350 W m -2 , the ΔT leaf−air range was ~10°C at ambient [CO 2 ] and only ~4°C at elevated [CO 2 ]. Thicker leaves in plants grown at elevated [CO 2 ] suggest higher leaf water content and consequently more efficient thermoregulation at high [CO 2 ] conditions. In general, T leaf was maintained closer to the ambient temperature at elevated [CO 2 ], even under drought. PRI, RUE, ΔT leaf−air , and T r decreased linearly with canopy depth, displaying a single PRI-RUE and ΔT leaf−air T r model through the canopy layers. Our study shows the utility of these sensing metrics in detecting wheat responses to future environmental changes.

The combination of a future rise in atmospheric carbon dioxide concentration ([CO2]) and drought will significantly impact wheat production and quality. Genotype phenology is likely to play an essential role in such an effect. Yet, its response to elevated [CO2] and drought has not been studied before. Here we conducted a temperature‐controlled glasshouse [CO2] enrichment experiment in which two wheat cultivars with differing maturity timings and life cycle lengths were grown under ambient (aCO2 approximately 400 μmol mol–1) and elevated (eCO2 approximately 550 μmol mol–1) [CO2]. The two cultivars, bred under dry and warm Mediterranean conditions, were well‐watered or exposed to drought at 40% pot holding capacity. We aimed to explore water × [CO2] × genotype interaction in terms of phenology, physiology, and agronomic trait response. Our results show that eCO2 had a significant effect on plants grown under drought. eCO2 boosted the booting stage of the late‐maturing genotype (cv. Ruta), thereby prolonging its booting‐to‐anthesis period by approximately 3 days (p < 0.05) while unaffecting the phenological timing of the early‐maturing genotype (cv. Zahir). The prolonged period resulted in a much higher carbon assimilation rate, particularly during pre‐anthesis (+87% for Ruta vs. +22% for Zahir under eCO2). Surprisingly, there was no eCO2 effect on transpiration rate and grain protein content in both cultivars and under both water conditions. The higher photosynthesis (and transpiration efficiency) of Ruta was not translated into higher aboveground biomass or grain yield, whereas both cultivars showed a similar increase of approximately 20% in these two traits at eCO2 under drought. Overall, Zahir, the cultivar that responded the least to eCO2, had a more efficient source‐to‐sink balance with a lower sink limitation than Ruta. The complex water × [CO2] × genotype interaction found in this study implies that future projections should account for multifactor interactive effects in modeling wheat response to future climate.

Vertical green living walls (VGWs)—growing plants on vertical walls inside or outside buildings—have been suggested as a nature-based solution to improve air quality and comfort in modern cities. However, as with other greenery systems (e.g., agriculture), managing VGW systems requires adequate temporal and spatial monitoring of the plants as well as the surrounding environment. Remote sensing cameras and small, low-cost sensors have become increasingly valuable for conventional vegetation monitoring; nevertheless, they have rarely been used in VGWs. In this descriptive paper, we present a first-of-its-kind remote sensing high-throughput monitoring system in a VGW workplace. The system includes low- and high-cost sensors, thermal and hyperspectral remote sensing cameras, and in situ gas-exchange measurements. In addition, air temperature, relative humidity, and carbon dioxide concentrations are constantly monitored in the operating workplace room (scientific computer lab) where the VGW is established, while data are continuously streamed online to an analytical and visualization web application. Artificial Intelligence is used to automatically monitor changes across the living wall. Preliminary results of our unique monitoring system are presented under actual working room conditions while discussing future directions and potential applications of such a high-throughput remote sensing VGW system.

To meet the ever-growing global population necessities, integrating climate-change-relevant plant traits into breeding programs is required. Developing new tools for fast and accurate estimation of chlorophyll parameters, chlorophyll a (Chl-a) content, chlorophyll b (Chl-b) content, and their ratio (Chl-a/b), can promote breeding programs of wheat with enhanced climate adaptability. Spectral reflectance of leaves is affected by changes in pigment concentration and can be used to estimate chlorophyll parameters. The current study identified and validated the top known spectral indices and developed new vegetation indices (VIs) for Chl-a and Chl-b content estimation and used them to non-destructively estimate Chl-a/b values and compare them to hyperspectral estimations. Three wild emmer introgression lines, with contrasting drought stress responsiveness dynamics, were selected. Well-watered and water-limited irrigation regimes were applied. The wheat leaves were spectrally measured with a handheld spectrometer to acquire their reflectance in the 330 to 790 nm range. Regression models based on calculated VIs as well as all hyperspectral curves were calibrated and validated against chlorophyll extracted values. The developed normalized difference spectral indices (NDSIs) resulted in high accuracy of Chl-a (NDSI415,614) and Chl-b (NDSI406,525) estimation, allowing for indirect non-destructive estimation of Chl-a/b with root mean square error (RMSE) values that could fit 6 to 10 times in the range of the measured values. They also performed similarly to the hyperspectral models. Altogether, we present here a new tool for a non-destructive estimation of Chl-a/b, which can serve as a basis for future breeding efforts of climate-resilient wheat as well as other crops.