Solar-induced chlorophyll fluorescence as an indicator for determining the end date of the vegetation growing season. (February 2020)

• Record Type: Journal Article
• Authors: Wang, Fei; Chen, Baozhang; Lin, Xiaofeng; Zhang, Huifang
• Is Part Of: Ecological indicators. Volume 109(2020)
• Publication Date: 2020-02
• Subjects: End date of growing season -- Solar-induced chlorophyll fluorescence -- Normalized difference vegetation index -- Light use efficiency model -- Environmental factors; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2019.105755
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: SIF performs better than NDVI in identifying the end date of growing season (EGS) in both mixed forest and cropland ecosystems. The incorporation of SIF into light use efficiency model can yield good estimation of GPP and further improve the results of EGS determination. GPP and SIF are mainly constrained by sunlight; while NDVI is controlled by air temperature in the autumn. Abstract: In this study, the performance of solar-induced chlorophyll fluorescence (SIF) in detecting the end date of the vegetation growing season (EGS) is evaluated at the canopy level. The experiment was conducted at two ecological stations in the Olympic Park mixed forest and Yucheng cropland in China. Validated against the gross primary productivity (GPP) measured by the eddy covariance technique (GPPEC ), the SIF was first compared with the normalized difference vegetation index (NDVI). The results showed that at the Olympic Park station (mixed forest), the EGS of SIF was 4 days earlier than that of the GPPEC, and the EGS of the NDVI showed 2 days of hysteresis. However, in the Yucheng cropland, the EGS of the NDVI was 26 days later than that of the GPPEC, while SIF lagged by 18 days. Considering the total biases against the EGS of GPPEC, the SIF was comprehensively superior to the NDVI for determining the end date of the vegetation growing season in these two ecosystems. SIF was further incorporated into a light use efficiency model to estimate the GPP, which was also used to extract the EGS. Two sets of inputs were employed, including the photochemical reflectance index and SIF, apparent fluorescence yield and SIF. The results showed that this SIF-based light use efficiency model can yield good estimations of GPPs, with correlation coefficients of all GPP values being above 0.8 for both ecosystems. Additionally, the estimated end dates of the vegetation growing season were better than those from the single SIF proxy. In particular, the combination of the two fluorescence variables used in the light use efficiency model contributed the best performance in that the EGS was 2 days earlier than that of the GPPEC at Olympic Park station and 13 days later than that of the GPPEC at Yucheng station. The EGS was improved by 2 days and 5 days for the mixed forest site and cropland site, respectively, compared to the EGSs determined by SIF. The influences of air temperature and sunlight availability on the temporal patterns of the GPP, SIF and NDVI was investigated. Light was the main factor controlling the GPP and SIF, while the NDVI was primarily controlled by the air temperature in both ecosystems. These results explained the different mechanisms of the three surrogates, and therefore, the distinct performances in detecting the EGSs of plants was explained. Our findings suggest that SIF is suitable for determining the end date of the vegetation growing season, regardless of whether it is used alone or in combination with other photosynthetic indices in mixed forest and cropland ecosystems. The errors in the estimated EGS can be limited to twenty days. Based on the results of this study, SIF can be more widely employed either in the ecological field to investigate vegetation dynamics and carbon cycling or in the agronomic field to predict the harvest period and adjust agricultural management strategies.