scholarly journals Monitoring Drought Effects on Vegetation Productivity Using Satellite Solar-Induced Chlorophyll Fluorescence

2019 ◽  
Vol 11 (4) ◽  
pp. 378 ◽  
Author(s):  
Lifu Zhang ◽  
Na Qiao ◽  
Changping Huang ◽  
Siheng Wang
Keyword(s):  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Drought Event ◽  
Forest Land ◽  
Short Term ◽  
Broadleaf Forest ◽  
Vegetation Productivity ◽  
Drought Period ◽  
Drought Effects

Around the world, the increasing drought, which is exacerbated by climate change, has significant impacts on vegetation carbon assimilation. Identifying how short-term climate anomalies influence vegetation productivity in a timely and accurate manner at the satellite scale is crucial to monitoring drought. Satellite solar-induced chlorophyll fluorescence (SIF) has recently been reported as a direct proxy of actual vegetation photosynthesis and has more advantages than traditional vegetation indices (e.g., the Normalized Difference Vegetation Index, NDVI and the Enhanced Vegetation Index, EVI) in monitoring vegetation vitality. This study aims to evaluate the feasibility of SIF in interpreting drought effects on vegetation productivity in Victoria, Australia, where heat stress and drought are often reported. Drought-induced variations in SIF and absorbed photosynthetically active radiation (APAR) estimations based on NDVI and EVI were investigated and validated against results indicated by gross primary production (GPP). We first compared drought responses of GPP and vegetation proxies (SIF and APAR) during the 2009 drought event, considering potential biome-dependency. Results showed that SIF exhibited more consistent declines with GPP losses induced by drought than did APAR estimations during the 2009 drought period in space and time, where APAR had obvious lagged responses compared with SIF, especially in evergreen broadleaf forest land. We then estimated the sensitivities of the aforementioned variables to meteorology anomalies using the ARx model, where memory effects were considered, and compared the correlations of GPP anomaly with the anomalies of vegetation proxies during a relatively long period (2007–2013). Compared with APAR, GPP and SIF are more sensitive to temperature anomalies for the general Victoria region. For crop land, GPP and vegetation proxies showed similar sensitivities to temperature and water availability. For evergreen broadleaf forest land, SIF anomaly was explained better by meteorology anomalies than APAR anomalies. GPP anomaly showed a stronger linear relationship with SIF anomaly than with APAR anomalies, especially for evergreen broadleaf forest land. We showed that SIF might be a promising tool for effectively evaluating short-term drought impacts on vegetation productivity, especially in drought-vulnerable areas, such as Victoria.

scholarly journals Feasibility of Using MODIS Products to Simulate Sun-Induced Chlorophyll Fluorescence (SIF) in Boreal Forests

2020 ◽  
Vol 12 (4) ◽  
pp. 680 ◽  
Author(s):  
Meng Guo ◽  
Jing Li ◽  
Shubo Huang ◽  
Lixiang Wen
Keyword(s):  
Chlorophyll Fluorescence ◽  
Boreal Forest ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Mean Squared Error ◽  
Gross Primary Production ◽  
Coniferous Forest ◽  
Terrestrial Ecosystems ◽  
Linear Regression Models ◽  
Modis Evi

Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R2 = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R2 = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature.

scholarly journals Sun-induced Chlorophyll fluorescence and PRI improve remote sensing GPP estimates under varying nutrient availability in a typical Mediterranean savanna ecosystem

2015 ◽  
Vol 12 (14) ◽  
pp. 11891-11934 ◽  
Author(s):  
O. Perez-Priego ◽  
J. Guan ◽  
M. Rossini ◽  
F. Fava ◽  
T. Wutzler ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Near Infrared ◽  
Gross Primary Production ◽  
High Spectral Resolution ◽  
N Availability ◽  
P Availability ◽  
N Content ◽  
Near Surface

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 hypothesized that light-use efficiency (LUE) models driven by remote sensing quantities (RSM) 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 proxy for LUE and NDVI or MTCI as fraction of absorbed photosynthetically active radiation (fAPAR) – with those of classical MM. Results showed significantly 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 tightly related to plant N content (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 MM is predictively inaccurate whenever no climatic variable explicitly reveals nutrient-related changes in the LUE parameter. These results put forward that RSM is a valuable means to diagnose nutrient-induced effects on the photosynthetic activity.

scholarly journals OCO-2 Solar-Induced Chlorophyll Fluorescence Variability across Ecoregions of the Amazon Basin and the Extreme Drought Effects of El Niño (2015–2016)

2020 ◽  
Vol 12 (7) ◽  
pp. 1202 ◽  
Author(s):  
Antony Oswaldo Castro ◽  
Jia Chen ◽  
Christian S. Zang ◽  
Ankit Shekhar ◽  
Juan Carlos Jimenez ◽  
...  
Keyword(s):  
Heat Stress ◽  
Chlorophyll Fluorescence ◽  
El Niño ◽  
Amazon Basin ◽  
Photosynthetic Activity ◽  
El Nino ◽  
Gross Primary Production ◽  
Extreme Drought ◽  
Drought Vulnerability ◽  
Drought Period

Amazonian ecosystems are major biodiversity hotspots and carbon sinks that may lose species to extinction and become carbon sources due to extreme dry or warm conditions. We investigated the seasonal patterns of high-resolution solar-induced chlorophyll fluorescence (SIF) measured by the satellite Orbiting Carbon Observatory-2 (OCO-2) across the Amazonian ecoregions to assess the area´s phenology and extreme drought vulnerability. SIF is an indicator of the photosynthetic activity of chlorophyll molecules and is assumed to be directly related to gross primary production (GPP). We analyzed SIF variability in the Amazon basin during the period between September 2014 and December 2018. In particular, we focused on the SIF drought response under the extreme drought period during the strong El Niño in 2015–2016, as well as the 6-month drought peak period. During the drought´s peak months, the SIF decreased and increased with different intensities across the ecoregions of the Amazonian moist broadleaf forest (MBF) biome. Under a high temperature, a high vapor pressure deficit, and extreme drought conditions, the SIF presented differences from −31.1% to +17.6%. Such chlorophyll activity variations have been observed in plant-level measurements of active fluorescence in plants undergoing physiological responses to water or heat stress. Thus, it is plausible that the SIF variations in the ecoregions’ ecosystems occurred as a result of water and heat stress, and arguably because of drought-driven vegetation mortality and collateral effects in their species composition and community structures. The SIF responses to drought at the ecoregional scale indicate that there are different levels of resilience to drought across MBF ecosystems that the currently used climate- and biome-region scales do not capture. Finally, we identified monthly SIF values of 32 ecoregions, including non-MBF biomes, which may give the first insights into the photosynthetic activity dynamics of Amazonian ecoregions.

scholarly journals Forest Resistance and Resilience to 2002 Drought in Northern China

2021 ◽  
Vol 13 (15) ◽  
pp. 2919
Author(s):  
Xiran Li ◽  
Yitong Yao ◽  
Guodong Yin ◽  
Feifei Peng ◽  
Muxing Liu
Keyword(s):  
Vegetation Index ◽  
Northern China ◽  
Severe Drought ◽  
Drought Event ◽  
Forest Types ◽  
Broadleaf Forest ◽  
Temporal Characteristics ◽  
Machine Learning Approach ◽  
Spatio Temporal ◽  
Deciduous Broadleaf Forest

Drought can weaken forest activity and even lead to forest mortality, and the response of different forest types to drought can be diverse. Deciduous broadleaf forest (DBF) and deciduous needleleaf forest (DNF) are two of the majority forest types in northern China. In this region, a severe drought event happened in 2002. However, due to the lack of data, the spatio-temporal characteristics of the ecosystem stability of different forest types here remain unclear. In this study, we used a machine learning approach (model tree ensemble, MTE) to drive fluxsite gross primary productivity (GPP), combined with remote sensing-based GPP and a vegetation index data (EVI), to analyze the spatial and temporal characteristics of resistance and resilience of DNF and DBF in northern China during and after the 2002 drought. The results showed that the DBFs were more acclimatized to the drought, while the resistance and resilience of DNF and DBF were diverse under different consecutive drought events. These results could be suggestive for forest conservation and vegetation modeling.

scholarly journals Can vegetation index track the interannual variation in gross primary production of temperate deciduous forests?

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Fan Liu ◽  
Chuankuan Wang ◽  
Xingchang Wang
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Gross Primary Production ◽  
Growing Season ◽  
Temperate Deciduous Forest ◽  
Near Infrared Reflectance ◽  
Modis Ndvi ◽  
Temperate Deciduous

Abstract Background Vegetation indices (VIs) by remote sensing are widely used as simple proxies of the gross primary production (GPP) of vegetation, but their performances in capturing the inter-annual variation (IAV) in GPP remain uncertain. Methods We evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China. The VIs assessed included the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the near-infrared reflectance of vegetation (NIRv) obtained from tower-radiometers (broadband) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. Results We found that 25%–35% amplitude of the broadband EVI tracked the start of growing season derived by GPP (R2: 0.56–0.60, bias < 4 d), while 45% (or 50%) amplitudes of broadband (or MODIS) NDVI represented the end of growing season estimated by GPP (R2: 0.58–0.67, bias < 3 d). However, all the VIs failed to characterize the summer peaks of GPP. The growing-season integrals but not averaged values of the broadband NDVI, MODIS NIRv and EVI were robust surrogates of the IAV in GPP (R2: 0.40–0.67). Conclusion These findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak, while the integral VIs have the potential to mirror the IAV in GPP.

scholarly journals Land surface model photosynthesis and parameter calibration for boreal sites with adaptive population importance sampler

2019 ◽  
Author(s):  
Jarmo Mäkelä ◽  
Jürgen Knauer ◽  
Mika Aurela ◽  
Andrew Black ◽  
Martin Heimann ◽  
...  
Keyword(s):  
Stomatal Conductance ◽  
Land Surface ◽  
Carbon Fixation ◽  
Gross Primary Production ◽  
Effect Of Temperature ◽  
Coefficient Of Determination ◽  
Surface Model ◽  
Drought Event ◽  
Forest Site ◽  
Delayed Effect

Abstract. We calibrated the JSBACH model with six different stomatal conductance formulations using measurements from 10 FLUXNET coniferous evergreen sites in the Boreal zone. The parameter posterior distributions were generated by adaptive population importance sampler and the optimal values by a simple stochastic optimisation algorithm. The observations used to constrain the model are evapotranspiration (ET) and gross primary production (GPP). We identified the key parameters in the calibration process. These parameters control the soil moisture stress function and the overall rate of carbon fixation. We were able to improve the coefficient of determination and the model bias with all stomatal conductance formulations. There was no clear candidate for the best stomatal conductance model, although certain versions produced better estimates depending on the examined variable (ET, GPP) and the used metric. We were also able to significantly enhance the model behaviour during a drought event in a Finnish Scots pine forest site. The JSBACH model was also modified to use a delayed effect of temperature for photosynthetic activity. This modification enabled the model to correctly time and replicate the springtime increase in GPP (and ET) for conifers throughout the measurements sites used in this study.

Early stress detection in grapevine – can remotely-sensed sun-induced chlorophyll fluorescence do the job?

2021 ◽  
Author(s):  
Georg Wohlfahrt ◽  
Albin Hammerle ◽  
Barbara Rainer ◽  
Florian Haas
Keyword(s):  
Chlorophyll Fluorescence ◽  
Vegetation Index ◽  
Near Infrared ◽  
Census Data ◽  
Meteorological Data ◽  
Vegetation Indices ◽  
Wavelength Region ◽  
Remote Sensing Data ◽  
Warning Signs ◽  
Mitigation Measures

&lt;p&gt;Ongoing changes in climate (both in the means and the extremes) are increasingly challenging grapevine production in the province of South Tyrol (Italy). Here we ask the question whether sun-induced chlorophyll fluorescence (SIF) observed remotely from space can detect early warning signs of stress in grapevine and thus help guide mitigation measures.&lt;/p&gt;&lt;p&gt;Chlorophyll fluorescence refers to light absorbed by chlorophyll molecules that is re-emitted in the red to far-red wavelength region. Previous research at leaf and canopy scale indicated that SIF correlates with the plant photosynthetic uptake of carbon dioxide as it competes for the same energy pool.&lt;/p&gt;&lt;p&gt;To address this question, we use time series of two down-scaled SIF products (GOME-2 and OCO-2, 2007/14-2018) as well as the original OCO-2 data (2014-2019). As a benchmark, we use several vegetation indices related to canopy greenness, as well as a novel near-infrared radiation-based vegetation index (2000-2019). Meteorological data fields are used to explore possible weather-related causes for observed deviations in remote sensing data. Regional DOC grapevine census data (2000-2019) are used as a reference for the analyses.&lt;/p&gt;

scholarly journals Modelling spatial and temporal dynamics of gross primary production in the Sahel from earth-observation-based photosynthetic capacity and quantum efficiency

2017 ◽  
Vol 14 (5) ◽  
pp. 1333-1348 ◽  
Author(s):  
Torbern Tagesson ◽  
Jonas Ardö ◽  
Bernard Cappelaere ◽  
Laurent Kergoat ◽  
Abdulhakim Abdi ◽  
...  
Keyword(s):  
Primary Production ◽  
Quantum Efficiency ◽  
Vegetation Index ◽  
Photosynthetic Capacity ◽  
Gross Primary Production ◽  
Earth Observation ◽  
Arid Ecosystems ◽  
Spatially Explicit ◽  
Equivalent Water Thickness ◽  
Semi Arid

Abstract. It has been shown that vegetation growth in semi-arid regions is important to the global terrestrial CO2 sink, which indicates the strong need for improved understanding and spatially explicit estimates of CO2 uptake (gross primary production; GPP) in semi-arid ecosystems. This study has three aims: (1) to evaluate the MOD17A2H GPP (collection 6) product against GPP based on eddy covariance (EC) for six sites across the Sahel; (2) to characterize relationships between spatial and temporal variability in EC-based photosynthetic capacity (Fopt) and quantum efficiency (α) and vegetation indices based on earth observation (EO) (normalized difference vegetation index (NDVI), renormalized difference vegetation index (RDVI), enhanced vegetation index (EVI) and shortwave infrared water stress index (SIWSI)); and (3) to study the applicability of EO upscaled Fopt and α for GPP modelling purposes. MOD17A2H GPP (collection 6) drastically underestimated GPP, most likely because maximum light use efficiency is set too low for semi-arid ecosystems in the MODIS algorithm. Intra-annual dynamics in Fopt were closely related to SIWSI being sensitive to equivalent water thickness, whereas α was closely related to RDVI being affected by chlorophyll abundance. Spatial and inter-annual dynamics in Fopt and α were closely coupled to NDVI and RDVI, respectively. Modelled GPP based on Fopt and α upscaled using EO-based indices reproduced in situ GPP well for all except a cropped site that was strongly impacted by anthropogenic land use. Upscaled GPP for the Sahel 2001–2014 was 736 ± 39 g C m−2 yr−1. This study indicates the strong applicability of EO as a tool for spatially explicit estimates of GPP, Fopt and α; incorporating EO-based Fopt and α in dynamic global vegetation models could improve estimates of vegetation production and simulations of ecosystem processes and hydro-biochemical cycles.

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

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