Sensitivity of Estimated Total Canopy SIF Emission to Remotely Sensed LAI and BRDF Products

Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides new possibilities to estimate terrestrial gross primary production (GPP). To mitigate the angular and canopy structural effects on original SIF observed by sensors (SIFobs), it is recommended to derive total canopy SIF emission (SIFtotal) of leaves within a canopy using canopy interception (i0) and reflectance of vegetation (RV). However, the effects of the uncertainties in i0 and RV on the estimation of SIFtotal have not been well understood. Here, we evaluated such effects on the estimation of GPP using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model. The SCOPE simulations showed that the R2 between GPP and SIFtotal was clearly higher than that between GPP and SIFobs and the differences in R2 (ΔR2) tend to decrease with the increasing levels of uncertainties in i0 and RV. The resultant ΔR2 decreased to zero when the uncertainty level in i0 and RV was ~30% for red band SIF (RSIF, 683 nm) and ~20% for far-red band SIF (FRSIF, 740 nm). In addition, as compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIFobs at both red and far-red bands, SIFtotal derived using any combination of i0 (from MCD15, VNP15, and CGLS LAI products) and RV (from MCD34, MCD19, and VNP43 BRDF products) showed comparable improvements in estimating GPP. With this study, we suggest a way to advance our understanding in the estimation of a more physiological relevant SIF datasets (SIFtotal) using current satellite products.

Download Full-text

The Links between Canopy Solar-Induced Chlorophyll Fluorescence and Gross Primary Production Responses to Meteorological Factors in the Growing Season in Deciduous Broadleaf Forest

Remote Sensing ◽

10.3390/rs13122363 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2363

Author(s):

Xiangfen Cheng ◽

Yu Zhou ◽

Meijun Hu ◽

Feng Wang ◽

Hui Huang ◽

...

Keyword(s):

Remote Sensing ◽

Chlorophyll Fluorescence ◽

Primary Production ◽

Forest Canopy ◽

Meteorological Factors ◽

Gross Primary Production ◽

Terrestrial Ecosystems ◽

Growing Season ◽

Near Surface ◽

The Relationship

Solar-induced chlorophyll fluorescence (SIF) is a hopeful indicator, which along with remote sensing, is used to measure the photosynthetic efficiency and gross primary production (GPP) of vegetation in regional terrestrial ecosystems. Studies have found a significant linear correlation between SIF and GPP in a variety of ecosystems. However, this relationship has mainly been established using SIF and GPP data derived from satellite remote sensing and continuous ground-based observations, respectively, which are difficult to accurately match. To overcome this, some studies have begun to use tower-based automatic observation instruments to study the changes of near-surface SIF and GPP. This study conducts continuous simultaneous observation of SIF, carbon flux, and meteorological factors on the forest canopy of a cork oak plantation during the growing season to explore how meteorological factors impact on canopy SIF and its relationship with GPP. This research found that the canopy SIF has obvious diurnal and day-to-day variations during the growing season but overall is relatively stable. Furthermore, SIF is greatly affected by incident radiation in different weather conditions and can change daily. Meteorological factors have a major role in the relationship between SIF and GPP; overall, the relationship shows a significant linear regression on the 30 min scale, but weakens when aggregating to the diurnal scale. Photosynthetically active radiation (PAR) drives SIF on a daily basis and changes the relationship between SIF and GPP on a seasonal timescale. As PAR increases, the daily slopes of the linear regressions between SIF and GPP decrease. On the 30 min timescale, both SIF and GPP increase with PAR until it reaches 1250 μmol·m−2·s−1; subsequently, SIF continues to increase while GPP decreases and they show opposite trends. Soil moisture and vapor pressure deficit influence SIF and GPP, respectively. Our findings demonstrate that meteorological factors affect the relationship between SIF and GPP, thereby enhancing the understanding of the mechanistic link between chlorophyll fluorescence and photosynthesis.

Download Full-text

Model-based analysis of the relationship between sun-induced chlorophyll fluorescence and gross primary production for remote sensing applications

Remote Sensing of Environment ◽

10.1016/j.rse.2016.10.016 ◽

2016 ◽

Vol 187 ◽

pp. 145-155 ◽

Cited By ~ 96

Author(s):

Yongguang Zhang ◽

Luis Guanter ◽

Joseph A. Berry ◽

Christiaan van der Tol ◽

Xi Yang ◽

...

Keyword(s):

Remote Sensing ◽

Chlorophyll Fluorescence ◽

Primary Production ◽

Gross Primary Production ◽

Model Based ◽

Sensing Applications ◽

Remote Sensing Applications ◽

The Relationship ◽

Model Based Analysis

Download Full-text

Sun-induced chlorophyll fluorescence and photochemical reflectance index improve remote-sensing gross primary production estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem

Biogeosciences ◽

10.5194/bg-12-6351-2015 ◽

2015 ◽

Vol 12 (21) ◽

pp. 6351-6367 ◽

Cited By ~ 41

Author(s):

O. Perez-Priego ◽

J. Guan ◽

M. Rossini ◽

F. Fava ◽

T. Wutzler ◽

...

Keyword(s):

Remote Sensing ◽

Chlorophyll Fluorescence ◽

Primary Production ◽

Gross Primary Production ◽

High Spectral Resolution ◽

N Availability ◽

P Availability ◽

N Content ◽

Near Surface ◽

Photochemical Reflectance Index

Abstract. This study investigates the performances of different optical indices to estimate gross primary production (GPP) of herbaceous stratum in a Mediterranean savanna with different nitrogen (N) and phosphorous (P) availability. Sun-induced chlorophyll fluorescence yield computed at 760 nm (Fy760), scaled photochemical reflectance index (sPRI), MERIS terrestrial-chlorophyll index (MTCI) and normalized difference vegetation index (NDVI) were computed from near-surface field spectroscopy measurements collected using high spectral resolution spectrometers covering the visible near-infrared regions. GPP was measured using canopy chambers on the same locations sampled by the spectrometers. We tested whether light-use efficiency (LUE) models driven by remote-sensing quantities (RSMs) can better track changes in GPP caused by nutrient supplies compared to those driven exclusively by meteorological data (MM). Particularly, we compared the performances of different RSM formulations – relying on the use of Fy760 or sPRI as a proxy for LUE and NDVI or MTCI as a fraction of absorbed photosynthetically active radiation (fAPAR) – with those of classical MM. Results showed higher GPP in the N-fertilized experimental plots during the growing period. These differences in GPP disappeared in the drying period when senescence effects masked out potential differences due to plant N content. Consequently, although MTCI was closely related to the mean of plant N content across treatments (r2 = 0.86, p < 0.01), it was poorly related to GPP (r2 = 0.45, p < 0.05). On the contrary sPRI and Fy760 correlated well with GPP during the whole measurement period. Results revealed that the relationship between GPP and Fy760 is not unique across treatments, but it is affected by N availability. Results from a cross-validation analysis showed that MM (AICcv = 127, MEcv = 0.879) outperformed RSM (AICcv =140, MEcv = 0.8737) when soil moisture was used to constrain the seasonal dynamic of LUE. However, residual analyses demonstrated that GPP predictions with MM are inaccurate whenever no climatic variable explicitly reveals nutrient-related changes in the LUE parameter. These results suggest that RSM is a valuable means to diagnose nutrient-induced effects on the photosynthetic activity.

Download Full-text

The spatial heterogeneity of the relationship between gross primary production and sun-induced chlorophyll fluorescence regulated by climate conditions during 2007-2018

Global Ecology and Conservation ◽

10.1016/j.gecco.2021.e01721 ◽

2021 ◽

pp. e01721

Author(s):

Yonglin Wang ◽

Lei Zhou ◽

Jie Zhuang ◽

Leigang Sun ◽

Yonggang Chi

Keyword(s):

Chlorophyll Fluorescence ◽

Primary Production ◽

Spatial Heterogeneity ◽

Gross Primary Production ◽

Climate Conditions ◽

The Relationship

Download Full-text

Gross primary production simulation in a coniferous forest using a daily gas exchange model with seasonal change of leaf physiological parameters derived from remote sensing data

International Journal of Remote Sensing ◽

10.1080/01431160802558691 ◽

2009 ◽

Vol 30 (12) ◽

pp. 3013-3025 ◽

Cited By ~ 8

Author(s):

Quan Wang ◽

John Tenhunen ◽

Timo Vesala

Keyword(s):

Remote Sensing ◽

Gas Exchange ◽

Primary Production ◽

Seasonal Change ◽

Gross Primary Production ◽

Coniferous Forest ◽

Remote Sensing Data ◽

Exchange Model ◽

Sensing Data ◽

Gas Exchange Model

Download Full-text

SIFSpec: Measuring Solar-Induced Chlorophyll Fluorescence Observations for Remote Sensing of Photosynthesis

Sensors ◽

10.3390/s19133009 ◽

2019 ◽

Vol 19 (13) ◽

pp. 3009 ◽

Cited By ~ 11

Author(s):

Shanshan Du ◽

Liangyun Liu ◽

Xinjie Liu ◽

Jian Guo ◽

Jiaochan Hu ◽

...

Keyword(s):

Remote Sensing ◽

Chlorophyll Fluorescence ◽

Seasonal Variations ◽

Photosynthetic Activity ◽

Gross Primary Production ◽

Atmospheric Correction ◽

Gansu Province ◽

Optical Measurements ◽

Calibration Data ◽

Flux Measurements

Solar-induced chlorophyll fluorescence (SIF) is regarded as a proxy for photosynthesis in terrestrial vegetation. Tower-based long-term observations of SIF are very important for gaining further insight into the ecosystem-specific seasonal dynamics of photosynthetic activity, including gross primary production (GPP). Here, we present the design and operation of the tower-based automated SIF measurement (SIFSpec) system. This system was developed with the aim of obtaining synchronous SIF observations and flux measurements across different terrestrial ecosystems, as well as to validate the increasing number of satellite SIF products using in situ measurements. Details of the system components, instrument installation, calibration, data collection, and processing are introduced. Atmospheric correction is also included in the data processing chain, which is important, but usually ignored for tower-based SIF measurements. Continuous measurements made across two growing cycles over maize at a Daman (DM) flux site (in Gansu province, China) demonstrate the reliable performance of SIF as an indicator for tracking the diurnal variations in photosynthetically active radiation (PAR) and seasonal variations in GPP. For the O2–A band in particular, a high correlation coefficient value of 0.81 is found between the SIF and seasonal variations of GPP. It is thus concluded that, in coordination with continuous eddy covariance (EC) flux measurements, automated and continuous SIF observations can provide a reliable approach for understanding the photosynthetic activity of the terrestrial ecosystem, and are also able to bridge the link between ground-based optical measurements and airborne or satellite remote sensing data.

Download Full-text

Gross Primary Production and False Spring: a spatio-temporal analysis

10.5194/egusphere-egu2020-17837 ◽

2020 ◽

Author(s):

Emma Izquierdo-Verdiguier ◽

Raúl Zurita-Milla ◽

Álvaro Moreno-Martinez ◽

Gustau Camps-Valls ◽

Anja Klisch ◽

...

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Primary Production ◽

Gross Primary Production ◽

Damage Index ◽

Temporal Analysis ◽

Environmental Stressors ◽

Impact Of Climate Change ◽

False Spring ◽

Spatial Temporal Analysis

Phenological information can be obtained from different sources of data. For instance, from remote sensing data or products and from models driven by weather variables. The former typically allows analyzing land surface phenology whereas the latter provide plant phenological information. Analyzing relationships between both sources of data allows us to understand the impact of climate change on vegetation over space and time. For example, the onset of spring is advanced or delayed by changes in the climate. These alterations affect plant productivity and animal migrations.Spring onset monitoring is supported by the Extended Spring Index (SI-x), which are a suite of regression-based models for key indicator plant species. These models (Schwartz et al. in 2013) are based on daily maximum and minimum temperature from the first day of the year (January 1st). The primary products of these models are the timing of first leaf and first bloom, but they also provide derivative products such as the timing of last freeze day and the risk of frost damage day (damage index) for each year. This information helps to understand if vegetation could have suffered from environmental stressors such as droughts or a late frost events. The effects of environmental stressors in vegetation could be captured by the false spring index, which relates the first leaf day and the last freeze day. Moreover, this information could be used to understand plant productivity as well as to evaluate the economic impact of climate change.Previous works studied the relationship between remote sensing and plant level products by means of spatial-temporal analysis between Gross Primary Production (GPP) and a spring onset index. However, they did not consider the possible impact of false spring effect in these relationships. Here, we present a spatial-temporal analysis between GPP and the damage index to better understand the effect of false springs (in annual gross photosynthesis data). The analysis is done for the period 2000 to 2015 over the contiguous US and at spatial resolution of 1 km. We used the MODIS annual sum of GPP and the damage and false spring indices derived from the SI-x models.

Download Full-text