Linking remote sensing parameters to CO2 assimilation rates at a leaf scale

Detecting drought stress occurrence using synergies between Sun induced fluorescence and vegetation surface temperature spatial records

Drought stress occurrence and recovery from drought can be detected using a single spatial set of simultaneous observations of SIF and canopy temperature records. Temporal and spatial responses to drought and heat stresses by plant stands of a drought-adapted diverse grassland ecosystem were studied using sun induced fluorescence (SIF,O2A and O2B bands) and further ecophysiological (canopy temperature (Tsurf), spatially modeled evapotranspiration, vegetation reflectance spectra) variables collected along spatial sampling grids while also utilizing eddy covariance measured carbon dioxide (net ecosystem exchange: NEE, gross primary production: GPP) and water flux (evapotranspiration: ET) data. The grids were of 0.5 and 5 ha spatial extents and contained 78 sampling points. Data were collected in four spatial sampling campaigns, two under drought (early summer) and another two during and after recovery (midsummer) at both spatial resolutions. Small values of spatial SIF_A averages (around 0.5 mW m-2 nm-1 sr-1) under strong early summer drought increased (to around 2 mW m-2 nm-1 sr-1) due recovery upon rain arrivals, showing high (R2: 0.8-0.88) positive temporal correlations to eddy covariance measured carbon (GPP, NEE) and water (ET) fluxes. Spatial averages of LAI, vegetation indices (NDVI, NIRv) and modeled ET followed similar temporal patterns. While SIF was depressed by drought, it showed higher values in high canopy temperature vegetation patches than in vegetation patches with lower Tsurf. The spatial pattern of higher SIF in higher Tsurf patches was persistent (2 weeks) under drought. The positive SIF_A-Tsurf spatial correlation turned into negative/not significant after recovery of the grassland from the drought, while hot summer weather persisted. It is proposed that, by using a single set of simultaneously measured spatial SIF and Tsurf data it is possible to infer whether the studied vegetation is under drought (and heat) stress while it could not be decided on the base of SIF data alone. Evaluation of the slope of the above relationship seems therefore beneficial before e.g. starting the (stress) classification procedure based on SIF.

Solar-induced chlorophyll fluorescence imperfectly tracks the temperature response of photosynthesis in winter wheat

Solar-induced fluorescence (SIF) is a promising proxy for photosynthesis, but it is unclear whether it performs well in tracking the gross primary productivity (GPP) under different environmental conditions. In this study, we investigated the dynamics of the two parameters from October 2020 to June 2021 in field-grown winter wheat (Triticum aestivum) and found that the ability of SIF to track GPP was weakened at low temperatures. Accounting for the coupling of light and temperature at a seasonal scale, we found that SIF yield showed a lower temperature sensitivity and had a lower but broader optimal temperature range compared with light-use efficiency (LUE), although both SIF yield and LUE decreased in low-temperature conditions. The discrepancy between the temperature responses of SIF yield and GPP caused an increase in the ratio of SIF/GPP in winter, which indicated the variation in the relationship between them during this period. The results of our study highlight the impact of low temperature on the relationship between SIF and GPP and show the necessity of reconsidering the dynamics of energy distribution inside plants under changing environments.

Predicting F v /F m and evaluating cotton drought tolerance using hyperspectral and 1D-CNN

The chlorophyll fluorescence parameter F_v/F_m is significant in abiotic plant stress. Current acquisition methods must deal with the dark adaptation of plants, which cannot achieve rapid, real-time, and high-throughput measurements. However, increased inputs on different genotypes based on hyperspectral model recognition verified its capabilities of handling large and variable samples. F_v/F_m is a drought tolerance index reflecting the best drought tolerant cotton genotype. Therefore, F_v/F_m hyperspectral prediction of different cotton varieties, and drought tolerance evaluation, are worth exploring. In this study, 80 cotton varieties were studied. The hyperspectral cotton data were obtained during the flowering, boll setting, and boll opening stages under normal and drought stress conditions. Next, One-dimensional convolutional neural networks (1D-CNN), Categorical Boosting (CatBoost), Light Gradient Boosting Machines (LightBGM), eXtreme Gradient Boosting (XGBoost), Decision Trees (DT), Random Forests (RF), Gradient elevation decision trees (GBDT), Adaptive Boosting (AdaBoost), Extra Trees (ET), and K-Nearest Neighbors (KNN) were modeled with F _v /F _m. The Savitzky-Golay + 1D-CNN model had the best robustness and accuracy (RMSE = 0.016, MAE = 0.009, MAPE = 0.011). In addition, the F _v /F _m prediction drought tolerance coefficient and the manually measured drought tolerance coefficient were similar. Therefore, cotton varieties with different drought tolerance degrees can be monitored using hyperspectral full band technology to establish a 1D-CNN model. This technique is non-destructive, fast and accurate in assessing the drought status of cotton, which promotes smart-scale agriculture.

Chlorophylls: A Personal Snapshot

Chlorophylls provide the basis for photosynthesis and thereby most life on Earth. Besides their involvement in primary charge separation in the reaction center, they serve as light-harvesting and light-sensing pigments, they also have additional functions, e.g., in inter-system electron transfer. Chlorophylls also have a wealth of applications in basic science, medicine, as colorants and, possibly, in optoelectronics. Considering that there has been more than 200 years of chlorophyll research, one would think that all has been said on these pigments. However, the opposite is true: ongoing research evidenced in this Special Issue brings together current work on chlorophylls and on their carotenoid counterparts. These introductory notes give a very brief and in part personal account of the history of chlorophyll research and applications, before concluding with a snapshot of this year's publications.

Area-ratio Fraunhofer line depth (aFLD) method approach to estimate solar-induced chlorophyll fluorescence in low spectral resolution spectra in a cool-temperate deciduous broadleaf forest

Solar-induced chlorophyll fluorescence (SIF) emissions were estimated by the "area-ratio Fraunhofer line depth (aFLD) method", a new retrieval methodology in spectra from a low spectral resolution (SR) spectroradiometer (MS-700: full width half maximum (FWHM) of 10 nm and spectral sampling interval of 3.3 nm), assisted with a scaling to reference SIF detected from high SR spectrum. The sparse pixels of a spectrum of low SR misses detecting the minimum of the O₂A absorption band around at 760 nm, which makes the SIF detection by conventional FLD methods lose accuracy considerably. To overcome this, the aFLD method uses the definite integral of spectra over a wide interval between 750 and 780 nm. The integration of the spectrum is insusceptible to the change in shape of the depression curve, leading to higher accuracy of the aFLD method. Daily SIF, calculated by the aFLD method using the spectra obtained with MS-700, was scaled to reference daily SIF calculated by the spectral fitting method using the spectra obtained from August to December 2019 with an ultrafine SR spectroradiometer (QE Pro, FWHM = 0.24 nm). As a result, SIF calculated from MS-700 spectra by aFLD method was strongly correlated with the reference SIF from QE Pro spectra (r² = 0.81) and was successfully scaled. Then, the scaled 11-year SIF from MS-700 at a deciduous broadleaf forest showed the correlation with GPP at multiple time steps: daily, monthly, and yearly, consistently during 2008-2018. The comparison of aFLD-derived SIF with the global Orbiting Carbon Observatory-2 (OCO-2) SIF data set (GOSIF) showed high correlation on monthly values during 2008-2017 (r² = 0.85). The combining approach of the aFLD method with a scaling to reference SIF successfully detected long-term canopy SIF emissions, which has great potential to provide essential information on ecosystem-level photosynthesis.

Photosynthesis: a multiscopic view

A recurring analogy for photosynthesis research is the fable of the blind men and the elephant. Photosynthesis has many complex working parts, which has driven the need to study each of them individually, with an inherent understanding that a more complete picture will require systematic integration of these views. However, unlike the blind men, who are limited to using their hands, researchers have developed over the past decades a repertoire of methods for studying these components, many of which capitalize on unique features intrinsic to each. More recent concerns about food security and clean, renewable energy have increased support for applied photosynthesis research, with the idea of either improving photosynthetic performance as a desired trait in select species or using photosynthetic measurements as a phenotyping tool in breeding efforts or for high precision crop management. In this review, we spotlight the migration of approaches for studying photosynthesis from the laboratory into field environments, highlight some recent advances and speculate on areas where further development would be fruitful, with an eye towards how applied photosynthesis research can have impacts at local and global scales.