Response of Mangrove Carbon Fluxes to Drought Stress Detected by Photochemical Reflectance Index

Abstract

The photochemical reflectance index (PRI) has been often used as a physiology-based remote sensing indicator of ecosystem carbon fluxes. However, the assessments of PRI in tracking long-term carbon fluxes with climatic anomalies in mangroves are very limited. In this study, four-year (2017–2020) continuous time series measurements from tower-based eddy covariance and spectral systems in a subtropical mangrove were used to explore the ability of PRI in tracking the response of mangrove carbon fluxes to climate fluctuations and drought stress. The results showed that the temporal dynamics of daily PRI and carbon fluxes shared similar variation patterns over the study period, experiencing simultaneously decreasing trends under drought stress. Compared with the first three years, annual mean values of NEE in 2020 decreased by 10.7% and PRI decreased by 29.0%, correspondingly. PRI and carbon fluxes were significantly correlated across diurnal, seasonal, and annual time scales with better fitness under drought stress. Dark-state PRI (PRI0), the constitutive component of PRI variation due to seasonally changing pigment pool size, showed similar temporal variation as PRI in response to drought stress, while delta PRI (ΔPRI), the facultative component of PRI variation due to diurnal xanthophyll cycle, showed no response to drought stress. This study confirms the ability of PRI to track temporal dynamics of mangrove carbon fluxes on both short-term and long-term scales, with the temporal variation of PRI largely affected by the long-term constitutive pigment pool size. This study highlights the potential of PRI to serve as an early and readily detectable indicator to track the response of the mangrove carbon cycle to climatic anomalies such as drought stress.

Response Mechanism of Leaf Area Index and Main Nutrient Content in Mangrove Supported by Hyperspectral Data

“…By converting the original reflectivity, a series of reflectivity independent variables can be formed, which can amplify or reduce the reflectivity value of the characteristic peak and improve the probability of spectral recognition [24]. When establishing the regression model with the physical and chemical composition analysis data, the matching relationship between the spectral data and the laboratory data is analyzed through the comprehensive validation of various methods [33]. The hyperspectral data of these 20 sampling points were synchronously measur the field using the PSR-1100 portable ground object spectrometer [14].…”

“…Covariance can reflect the change trend among multiple inversion results of mangrove (Table 4) [33]. If the distribution law of a nutrient element and LAI is consistent, the difference of the covariance is positive; if the distribution law is opposite, the covariance difference is negative [49].…”

“…The remote sensing monitoring data in the past 40 years show that the mangrove area changes significantly from year to year in the Guangdong province. Natural factors such as temperature, sea level rise, extreme weather events, and coastline length have a macro impact on the distribution of mangroves [32,33]. The whole process of mangrove death caused by road construction was studied based on the analysis of optics, synthetic aperture radar, UAV image, and topographic survey data [34,35].…”

Optical and Thermal Remote Sensing for Monitoring Agricultural Drought

“…There are other wavelength selections 1997 [58] Bolivia [107], Spain [108], China [109,110] Simple Ratio Water Index (SRWI) ρ 860 ρ 1240…”