Remote sensing‐based estimation of annual soil respiration at two contrasting forest sites

Huang, Ni; Gu, Lianhong; Black, T. Andrew; Wang, Li; Niu, Zheng

doi:10.1002/2015jg003060

Cited by 17 publications

(17 citation statements)

References 88 publications

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“…The definition of R ref as the reference respiration is key to the design of large-scale R eco models. Many previous works have tried to simulate R ref at various spatial scales (Huang et al, 2015;Jägermeyr et al, 2014;Migliavacca et al, 2011;Yuan et al, 2011). In this study, we find that Second, the minimal annual temperature largely affects GPP, which controls R ref (Yuan et al, 2011).…”

Section: Scaling Issuesmentioning

confidence: 61%

“…The definition of R ref as the reference respiration is key to the design of large‐scale R eco models. Many previous works have tried to simulate R ref at various spatial scales (Huang et al, ; Jägermeyr et al, ; Migliavacca et al, ; Yuan et al, ). In this study, we find that R ref ( T ref = LSTnight_mean) correlated most closely with EVI at mean annual nighttime LST (EVI_LSTnight_mean) and minimum annual nighttime LST (LSTnight_min).…”

Section: Discussionmentioning

confidence: 99%

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MODIS‐Based Estimates of Global Terrestrial Ecosystem Respiration

Jia

Epstein

et al. 2018

JGR Biogeosciences

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Terrestrial ecosystem respiration (Reco) represents a large carbon source from land to atmosphere and is highly spatiotemporally heterogeneous across scales. Upscaling of field‐measured respiration data using remote sensing information is urgently needed for understanding regional and global patterns of ecosystem respiration. Using Moderate Resolution Imaging Spectroradiometer (MODIS) data with resolutions of 1 km and 8 days and flux measurements from 171 sites (total of 812 site years) across the world from 2000 to 2014, we developed a semiempirical, yet physiologically based, remote sensing model, which can simulate Reco observed across most biomes with a small margin of error (R2 = 0.55, root‐mean‐square error = 1.67 gCm−2d−1, efficiency = 0.46, and mean bias error = 0.18 gCm−2d−1). The reference respiration at the annual mean nighttime land surface temperature (LST) can be well represented by MODIS enhanced vegetation index and LST. A comprehensive comparison of six respiration‐temperature (R‐T) models shows that the more physiologically based R‐T model (extended Arrhenius model) may be most suitable for estimating the respiration rate at higher latitudes. Integrating an effect of vegetation change on Reco in different biomes effectively improves estimates of Reco in almost all of the biomes.

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Section: Scaling Issuesmentioning

confidence: 61%

Section: Discussionmentioning

confidence: 99%

MODIS‐Based Estimates of Global Terrestrial Ecosystem Respiration

Jia

Epstein

et al. 2018

JGR Biogeosciences

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“…The LST tn (nighttime LST from the Terra satellite) captured the seasonal variation of R s best among all the MODIS eight-day LST products in the study site during the nine-year period, which was consistent with the results of other studies. Huang et al [22] selected the nighttime LST observed by the Terra satellite to estimate R s at 2 contrasting forest sites, while Huang et al [23] chose the average LST of daytime and nighttime data from MOD11A2 as a driver of the R s fitting model of a deciduous broadleaf forest in the midwestern United States. Wu et al [5] reported that the nighttime LST from the Terra satellite was better correlated with the soil respiration than the daytime LST at a Canadian boreal black spruce stand and indicated that the nighttime LST could be a better estimate of the baseline temperature that regulated plant phenology.…”

Section: Seasonal Variations In R Smentioning

confidence: 99%

“…It is a low-cost and convenient method of acquiring land surface parameters indirectly, which compound the mixed effects of plants and soil. This technique has been proven to be valid for the estimation of surface parameters (land surface temperature, soil water content, and surface reflectivity), but is less applicable to underground biochemical processes such as R s [22,23]. Some researchers have recently used more accessible land surface parameters to build spatial [5] or temporal variation models of R s to predict R s based on empirical models and provide data supporting the carbon budget on a large scale.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Respiration by Its Driving Factors Based on Multi-Source Data in a Sub-Alpine Meadow in North China

et al. 2019

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Soil respiration (Rs) in high-altitude areas are normally sensitive to varying climatic conditions. The objective of this research was mainly to explore temporal variations in Rs rates and the corresponding controlling factors for the establishment of appropriate fitting models in a sub-alpine meadow of north China. The data was obtained through field measuring and extraction of the Moderate Resolution Imaging Spectroradiometer (MODIS) in the geographical unit of the study site over the period of 2007 to 2015. The main results were as follows: (1) seasonal variations in Rs rates, soil temperature (Ts), land surface temperature (LST), and normalized difference vegetation index (NDVI) all produced symmetrical bell type patterns, while soil moisture (Ms) showed a fluctuating pattern, (2) a Ts-exponential model could greatly capture seasonal variations of Rs rates in the study site, reflecting the role of temperature as a dominant driving factor in determining Rs temporal variations in alpine meadow areas, (3) there was no significant difference between the performing indicators evaluating the proposed Ts-exponential model and the LST-exponential model. This indicated great potential for applying remote sensing products to estimate seasonal Rs rates and 4) seasonal variations in Rs rates towards temperature sensitivity (Q10) showed a concave curve and dramatically decreased as the temperature increased from −1 to 11 °C. Overall, the results indicated that attention to significant effects of climatic conditions on Rs, particularly in areas of low temperature, should be warranted. Also, applicability of remote sensing products for estimating Rs was reflected and demonstrated.

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“…Vegetation indices and parameters, such as normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI), and gross primary production (GPP), indicate the strength of photosynthesis activity related to terrestrial CO 2 uptake [21][22][23], while land surface temperature (LST) has an impact on the respiration rate and evaporation of terrestrial biosphere [24]. For a better understanding of where and how strong the biosphere's seasonal activities influence the seasonal cycle pattern of XCO 2 , this study proposes a data-driven approach to assess the impacts from terrestrial biosphere-atmosphere interactions on the seasonal cycle pattern of XCO 2 using satellite retrievals of XCO 2 observed by GOSAT and parameters related to terrestrial biosphere activities observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) from June 2009 to May 2014.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Assessment of Biosphere-Atmosphere Interaction Impact on Seasonal Cycle Patterns of XCO2 Using GOSAT and MODIS Observations

Baker

Lei

et al. 2017

Remote Sensing

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Using measurements of the column-averaged CO 2 dry air mole fraction (XCO 2 ) from GOSAT and biosphere parameters, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI), gross primary production (GPP), and land surface temperature (LST) from MODIS, this study proposes a data-driven approach to assess the impacts of terrestrial biosphere activities on the seasonal cycle pattern of XCO 2 . A unique global land mapping dataset of XCO 2 with a resolution of 1 • by 1 • in space, and three days in time, from June 2009 to May 2014, which facilitates the assessment at a fine scale, is first produced from GOSAT XCO 2 retrievals. We then conduct a statistical fitting method to obtain the global map of seasonal cycle amplitudes (SCA) of XCO 2 and NDVI, and implement correlation analyses of seasonal variation between XCO 2 and the vegetation parameters. As a result, the spatial distribution of XCO 2 SCA decreases globally with latitude from north to south, which is in good agreement with that of simulated XCO 2 from CarbonTracker. The spatial pattern of XCO 2 SCA corresponds well to the vegetation seasonal activity revealed by NDVI, with a strong correlation coefficient of 0.74 in the northern hemisphere (NH). Some hotspots in the subtropical areas, including Northern India (with SCA of 8.68 ± 0.49 ppm on average) and Central Africa (with SCA of 8.33 ± 0.25 ppm on average), shown by satellite measurements, but missed by model simulations, demonstrate the advantage of satellites in observing the biosphere-atmosphere interactions at local scales. Results from correlation analyses between XCO 2 and NDVI, EVI, LAI, or GPP show a consistent spatial distribution, and NDVI and EVI have stronger negative correlations over all latitudes. This may suggest that NDVI and EVI can be better vegetation parameters in characterizing the seasonal variations of XCO 2 and its driving terrestrial biosphere activities. We, furthermore, present the global distribution of phase lags of XCO 2 compared to NDVI in seasonal variation, which, to our knowledge, is the first such map derived from a completely data-driven approach using satellite observations. The impact of retrieval error of GOSAT data on the mapping data, especially over high-latitude areas, is further discussed. Results from this study provide reference for better understanding the distribution of the strength of carbon sink by terrestrial ecosystems and utilizing remote sensing data in assessing the impact of biosphere-atmosphere interactions on the seasonal cycle pattern of atmospheric CO 2 columns.

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Remote sensing‐based estimation of annual soil respiration at two contrasting forest sites

Cited by 17 publications

References 88 publications

MODIS‐Based Estimates of Global Terrestrial Ecosystem Respiration

MODIS‐Based Estimates of Global Terrestrial Ecosystem Respiration

Estimation of Soil Respiration by Its Driving Factors Based on Multi-Source Data in a Sub-Alpine Meadow in North China

A Data-Driven Assessment of Biosphere-Atmosphere Interaction Impact on Seasonal Cycle Patterns of XCO2 Using GOSAT and MODIS Observations

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