Potential Of Solar-induced Chlorophyll Fluorescence Research Articles

Potential of solar-induced chlorophyll fluorescence (SIF) to access long-term dynamics of soil salinity using OCO-2 satellite data and machine learning method

The accumulation of soil salt becomes a worldwide widespread phenomenon, being a major threat to global production. As an environmental stress, soil salinity can reduce the vegetation photosynthetic activity. Solar-induced chlorophyll fluorescence (SIF) is an electromagnetic signal actively released by vegetation during photosynthesis. SIF not only can capture lower vegetation photosynthetic activity due to environmental stress promptly, but also is less affected by atmosphere and soil background. However, the ability of SIF observation to detect soil salinity remains unclear. Here, we use standardizedsolar-induced chlorophyll iluorescence index (SIFI) from SIF observation time series (2000 ∼ 2020) of global OCO-2 based SIF product (GOSIF) to develop soil salinity model. The results show that: SIF observation can identify salt affected soil (EC ≥ 2 ∼ 4 dS m−1) and salinity class a global scale. The SIFI calculated from SIF observation at May ∼ August (hereafter SIFI5-8) is the optimal sensitivity indices for rainfed cropland, herbaceous cover, irrigated cropland, shrubland, grassland. SIFI10-11 is the optimal sensitivity indices for forest and sparse vegetation; (2) By comparison, the ovrerall classification accuracy of predicted salinity class is above 70 %. The order of classification accuracy is rainfed cropland > irrigated cropland > bare area > forest > grassland > shrubland > herbaceous cover > sparse vegetation; (3) During at least three-quarters of the period from 2000 to 2020, the area of salt affected soil (EC ≥ 2 ∼ 4 dS m−1) was 4.9 Mkm2; (4) The annual change rate of global soil salt content is generally between −0.05 and 0.05 dS m−1 yr−1. Soil salinity in South Africa and West Asia increased greatly with an annual change rate of 0.02 ∼ 0.03 dS m−1 yr−1. These results demonstrate the ability of SIF observation to estimate soil salinity, providing a new perspective for explaining and evaluating soil salinity variation.

Assessing the Response of Satellite Solar-Induced Chlorophyll Fluorescence and NDVI to Impacts of Heat Waves on Winter Wheat in the North China Plain

Global warming has increased the chance of concurrent extreme climate events (weather or climate events that are rare within their statistical reference distributions in a particular place, such as heat waves, floods, and droughts). Crops grow best within specific temperature intervals, and excessive heat is detrimental to the physiological processes of crops and eventually affects yield levels. Analysing historical changes in concurrent extreme high temperatures is critical to preparing for and mitigating the negative effects of climatic change. The North China Plain (NCP) is the most important wheat production area in China. In this study, the spatiotemporal variations in temperature and heat wave trends in the NCP were analysed. Furthermore, we examined the potential of solar-induced chlorophyll fluorescence (SIF) to capture the influence of heat wave impacts on wheat crops in the NCP by comparing satellite remote sensing data of SIF and normalized difference vegetation index (NDVI) and validated ground-based yield data. The results indicate that temperatures and the number of heat wave days in the study region all show increasing trends, especially daily minimum temperature, which has increased by 0.38°C per decade for the past 30 years. Spatially, the southern NCP has suffered greater increasing-temperature trends and more heat wave days than the northern region. Regarding the response of SIF and NDVI to heat waves, SIF can better capture wheat yield decline due to heat waves compared to NDVI; thus, the SIF result indicated more sensitivity to heat waves compared to NDVI.

Potential of solar-induced chlorophyll fluorescence to estimate transpiration in a temperate forest

By utilizing continuous measurements of water fluxes and solar-induced chlorophyll fluorescence (SIF) over the entire growing season, we exploit the potential of broadband SIF in predicting plant transpiration (T) in a temperate forest. After reconstructing the full SIF spectrum from the selected absorption lines and simulations from the SCOPE (Soil Canopy Observation Photochemistry and Energy fluxes) model, linear regression (LR) and Gaussian processes regression (GPR) models are used to analyze the relation between T and combinations of different SIF bands. We find that SIF emissions in the near-infrared spectrum (at 720 nm, 740 nm and 760 nm) are more sensitive to T than SIF emissions in the red spectrum (at 685 nm and 687 nm). While conditions such as light and heat stress decouple the relationship between single-band SIF and T, the combination of different SIF bands allows the retrieval of reliable T estimates even in these conditions. Overall, we find that the use of SIF as a proxy for T yields estimates that are at least as accurate as those from traditional transpiration models such as the Penman-Monteith equation, which are input demanding and complex to apply to in situ and satellite data. Specifically, we find that (1) the SIF-T relationship deteriorates when Photosynthetically Active Radiation (PAR), vapor pressure deficit and air temperature exceed biological optimal thresholds; (2) a high leaf area index exerts a negative impact on the SIF-T correlation due to increasing scattering and (re)absorption of the SIF signal; (3) the SIF-T relationship does not change depending on the observation time during the day; and (4) temporal aggregation to days further enhanced the SIF-T correlations. Altogether, our results provide the first ground-based evidence that SIF emission has potential to be a close predictor of plant transpiration, especially when a combination of different SIF bands is considered.