Estimating Carbon Flux Phenology with Satellite-Derived Land Surface Phenology and Climate Drivers for Different Biomes: A Synthesis of AmeriFlux Observations

<div><p>Carbon Flux Phenology (CFP) can affect the interannual variation in Net Ecosystem Exchange (NEE) of carbon between terrestrial ecosystems and the atmosphere. In this study, we proposed a methodology to estimate CFP metrics with satellite-derived Land Surface Phenology (LSP) metrics and climate drivers for 4 biomes (i.e., deciduous broadleaf forest, evergreen needleleaf forest, grasslands and croplands), using 159 site-years of NEE and climate data from 32 AmeriFlux sites and MODIS vegetation index time-series data. LSP metrics combined with optimal climate drivers can explain the variability in Start of Carbon Uptake (SCU) by more than 70% and End of Carbon Uptake (ECU) by more than 60%. The Root Mean Square Error (RMSE) of the estimations was within 8.5 days for both SCU and ECU. The estimation performance for this methodology was primarily dependent on the optimal combination of the LSP retrieval methods, the explanatory climate drivers, the biome types, and the specific CFP metric. This methodology has a potential for allowing extrapolation of CFP metrics for biomes with a distinct and detectable seasonal cycle over large areas, based on synoptic multi-temporal optical satellite data and climate data.</p></div

Relationships between observed and estimated carbon flux phenology dates for different biomes.

<p>A) and B) Deciduous broadleaf forest, C) and D) Evergreen needleleaf forest, E) and F) Grasslands, and G) and H) Croplands. The left panel (i.e., A, C, E and G) indicates the relationships between observed Start/End of Carbon Uptake (SCU/ECU) in Julian Day of Year (DOY) and estimated with the best performing explanatory variables given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084990#pone-0084990-g004" target="_blank">Figure 4</a>, and the right panel (i.e., B, D, F and H) indicates the relationships between observed SCU/ECU and estimated with the best performing explanatory variables based on the leave-one-out cross-validation approach.</p

A schematic representation of the different impact periods for climate drivers.

<p>The different impact periods of climate drivers on carbon flux phenology dates were determined in terms of the distance (in days) from satellite-derived Start/End of Season (SOS/EOS), 10-day after SOS/EOS and 20-day after SOS/EOS with a step of 10 days. There were totally 18 candidate impact periods for each climate driver. Negative values indicate the days before SOS/EOS and positive values indicates the days after SOS/EOS.</p

The coefficient of determination (<i>R</i>²), Root Mean Square Error (RMSE) and Bias between Net Ecosystem Exchange (NEE)-derived carbon flux phenology dates and Normalized Difference Vegetation Index (NDVI)-derived land surface phenology dates based on the best performing retrieval method (i.e., the local mean midpoint threshold method) for different biomes.

<p>SOS = Start of Season derived from satellite data, SCU = Start of Carbon Uptake derived from carbon flux data, EOS = End of Season derived from satellite data, ECU = End of Carbon Uptake derived from carbon flux data.</p><p>Statistically significant at the 0.05 level.</p

The relationships between carbon flux phenology dates and climate drivers in different impact periods.

<p>A) The coefficient of determination (<i>R</i>²) between Net Ecosystem Exchange (NEE)-derived Start of Carbon Uptake (SCU) and the cumulative daily air temperature (above 0°C) for different periods around Normalized Difference Vegetation Index (NDVI)-derived Start of Season (SOS). B) The <i>R</i>² between NEE-derived SCU and the total precipitation for different periods around NDVI-derived SOS. C) The <i>R</i>² between NEE-derived End of Carbon Uptake (ECU) and the cumulative daily air temperature (above 0°C) for different periods around NDVI-derived End of Season (EOS). D) The <i>R</i>² between NEE-derived ECU and the total precipitation for different periods around NDVI-derived EOS. Red colored line: Deciduous broadleaf forest; green: evergreen needleleaf forest; blue: grassland; orange: cropland. Stars indicate the locations with the highest <i>R</i>² for each biome and with a statistical significance at the 0.05 level. Solid circles indicate statistically significant <i>R</i>² at the 0.05 level, and hollow circles indicate statistically non-significant <i>R</i>².</p

Distribution of eddy flux towers and their corresponding biome types.

<p>Distribution of eddy flux towers and their corresponding biome types.</p

A schematic demonstration of the retrieval method for carbon flux phenology dates.

<p>A) The original and smoothed 15-day mean Net Ecosystem Exchange (NEE) of carbon, and B) the two selected transition periods for spring source-sink and autumn sink-source for identifying linear regressions between NEE and the Julian Day of Year (DOY). Start/End of Carbon Uptake (SCU/ECU) is estimated at the zero intersection.</p

A correction technique for false topographic perception of remote-sensing images based on an inverse topographic correction technique

<p>The false topographic perception phenomenon (FTPP) refers to the visual misperception in remote-sensing images that certain types of terrains are visually interpreted as other types in rugged lands, for example, valleys as ridges and troughs as peaks. For this reason, the FTPP can influence the visualization and interpretation of images to a great extent. To scrutinize this problem, the paper firstly reviews and tests the existing FTPP-correction techniques and identifies the inverse slope-matching technique as an effective approach to visually enhance remote-sensing images and retain the colour information. The paper then proposes an improved FTPP-correction procedure that incorporates other image-processing techniques (e.g. linear stretch, histogram matching, and flat-area replacement) to enhance the performance of this technique. A further evaluation of the proposed technique is conducted by applying the technique to various study areas and using different types of remote-sensing images. The result indicates the method is relatively robust and will be a significant extension to geovisual analytics in digital earth research.</p

Change in the Green-Up Dates for <i>Quercus mongolica</i> in Northeast China and Its Climate-Driven Mechanism from 1962 to 2012

<div><p>The currently available studies on the green-up date were mainly based on ground observations and/or satellite data, and few model simulations integrated with wide coverage satellite data have been reported at large scale over a long time period (i.e., > 30 years). In this study, we combined phenology mechanism model, long-term climate data and synoptic scale remote sensing data to investigate the change in the green-up dates for <i>Quercus mongolica</i> over 33 weather stations in Northeast China and its climate-driven mechanism during 1962-2012. The results indicated that the unified phenology model can be well parameterized with the satellite derived green-up dates. The optimal daily mean temperature for chilling effect was between -27°C and 1°C for <i>Q</i>. <i>mongolica</i> in Northeast China, while the optimal daily mean temperature for forcing effect was above -3°C. The green-up dates for <i>Q</i>. <i>mongolica</i> across Northeast China showed a delayed latitudinal gradient of 2.699 days degree^-1, with the earliest date on the Julian day 93 (i.e., 3^th April) in the south and the latest date on the Julian day 129 (i.e., 9^th May) in the north. The green-up date for <i>Q</i>. <i>mongolica</i> in Northeast China has advanced 6.6 days (1.3 days decade^-1) from 1962 to 2012. With the prevailing warming in autumn, winter and spring in Northeast China during the past 51 years, the chilling effect for <i>Q</i>. <i>mongolica</i> has been weakened, while the forcing effect has been enhanced. The advancing trend in the green-up dates for <i>Q</i>. <i>mongolica</i> implied that the enhanced forcing effect to accelerate green-up was stronger than the weakened chilling effect to hold back green-up while the changes of both effects were caused by the warming climate.</p></div

The temporal changes in the green-up dates for <i>Q</i>. <i>mongolica</i> in Northeast China from 1962 to 2012.

<p>(A) The temporal changes in the green-up dates over 33 weather stations; (B) the frequency distribution of the temporal changes in green-up dates; and (C) the interannual variations in green-up dates (DOY) for the 33 stations and the change trend (days decade^-1). Note: the numbers in (A) indicate the advanced (minus number) or delayed (plus number) days at each weather station.</p