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Title: Modeling gross primary production of paddy rice cropland through analyses of data from CO 2 eddy flux tower sites and MODIS images

Authors:
; ; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1419519
Grant/Contract Number:
7079856
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Remote Sensing of Environment
Additional Journal Information:
Journal Volume: 190; Journal Issue: C; Related Information: CHORUS Timestamp: 2018-02-02 20:02:52; Journal ID: ISSN 0034-4257
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

Citation Formats

Xin, Fengfei, Xiao, Xiangming, Zhao, Bin, Miyata, Akira, Baldocchi, Dennis, Knox, Sara, Kang, Minseok, Shim, Kyo-moon, Min, Sunghyun, Chen, Bangqian, Li, Xiangping, Wang, Jie, Dong, Jinwei, and Biradar, Chandrashekhar. Modeling gross primary production of paddy rice cropland through analyses of data from CO 2 eddy flux tower sites and MODIS images. United States: N. p., 2017. Web. doi:10.1016/j.rse.2016.11.025.
Xin, Fengfei, Xiao, Xiangming, Zhao, Bin, Miyata, Akira, Baldocchi, Dennis, Knox, Sara, Kang, Minseok, Shim, Kyo-moon, Min, Sunghyun, Chen, Bangqian, Li, Xiangping, Wang, Jie, Dong, Jinwei, & Biradar, Chandrashekhar. Modeling gross primary production of paddy rice cropland through analyses of data from CO 2 eddy flux tower sites and MODIS images. United States. doi:10.1016/j.rse.2016.11.025.
Xin, Fengfei, Xiao, Xiangming, Zhao, Bin, Miyata, Akira, Baldocchi, Dennis, Knox, Sara, Kang, Minseok, Shim, Kyo-moon, Min, Sunghyun, Chen, Bangqian, Li, Xiangping, Wang, Jie, Dong, Jinwei, and Biradar, Chandrashekhar. Wed . "Modeling gross primary production of paddy rice cropland through analyses of data from CO 2 eddy flux tower sites and MODIS images". United States. doi:10.1016/j.rse.2016.11.025.
@article{osti_1419519,
title = {Modeling gross primary production of paddy rice cropland through analyses of data from CO 2 eddy flux tower sites and MODIS images},
author = {Xin, Fengfei and Xiao, Xiangming and Zhao, Bin and Miyata, Akira and Baldocchi, Dennis and Knox, Sara and Kang, Minseok and Shim, Kyo-moon and Min, Sunghyun and Chen, Bangqian and Li, Xiangping and Wang, Jie and Dong, Jinwei and Biradar, Chandrashekhar},
abstractNote = {},
doi = {10.1016/j.rse.2016.11.025},
journal = {Remote Sensing of Environment},
number = C,
volume = 190,
place = {United States},
year = {Wed Mar 01 00:00:00 EST 2017},
month = {Wed Mar 01 00:00:00 EST 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.rse.2016.11.025

Citation Metrics:
Cited by: 1work
Citation information provided by
Web of Science

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  • The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely-sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a wide rangemore » of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000-2004, and was validated using observed GPP over the period 2005-2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1 km x 1 km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production for the U.S. that is a highly constrained by eddy covariance flux data. Our study demonstrated that our empirical approach is effective for upscaling eddy flux GPP data to the continental scale and producing continuous GPP estimates across multiple biomes. With these estimates, we then examined the patterns, magnitude, and interannual variability of GPP. We estimated a gross carbon uptake between 6.91 and 7.33 Pg C yr{sup -1} for the conterminous U.S. Drought, fires, and hurricanes reduced annual GPP at regional scales and could have a significant impact on the U.S. net ecosystem carbon exchange. The sources of the interannual variability of U.S. GPP were dominated by these extreme climate events and disturbances.« less
  • The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a widemore » range of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000 2004, and was validated using observed GPP over the period 2005 2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1 km 1 km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production for the U.S. that is a highly constrained by eddy covariance flux data. Our study demonstrated that our empirical approach is effective for upscaling eddy flux GPP data to the continental scale and producing continuous GPP estimates across multiple biomes. With these estimates, we then examined the patterns, magnitude, and interannual variability of GPP. We estimated a gross carbon uptake between 6.91 and 7.33 Pg C yr 1 for the conterminous U.S. Drought, fires, and hurricanes reduced annual GPP at regional scales and could have a significant impact on the U.S. net ecosystem carbon exchange. The sources of the interannual variability of U.S. GPP were dominated by these extreme climate events and disturbances.« less
  • The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a widemore » range of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000–2004, and was validated using observed GPP over the period 2005–2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1 km×1 km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production for the U.S. that is a highly constrained by eddy covariance flux data. Our study demonstrated that our empirical approach is effective for upscaling eddy flux GPP data to the continental scale and producing continuous GPP estimates across multiple biomes. With these estimates, we then examined the patterns, magnitude, and interannual variability of GPP. We estimated a gross carbon uptake between 6.91 and 7.33 Pg C yr -1 for the conterminous U.S. Drought, fires, and hurricanes reduced annual GPP at regional scales and could have a significant impact on the U.S. net ecosystem carbon exchange. The sources of the interannual variability of U.S. GPP were dominated by these extreme climate events and disturbances.« less
  • Satellite-based Production Efficiency Models (PEMs) often require meteorological reanalysis data such as the North America Regional Reanalysis (NARR) by the National Centers for Environmental Prediction (NCEP) as model inputs to simulate Gross Primary Production (GPP) at regional and global scales. This study first evaluated the accuracies of air temperature (TNARR) and downward shortwave radiation (RNARR) of the NARR by comparing with in-situ meteorological measurements at 37 AmeriFlux non-crop eddy flux sites, then used one PEM – the Vegetation Photosynthesis Model (VPM) to simulate 8-day mean GPP (GPPVPM) at seven AmeriFlux crop sites, and investigated the uncertainties in GPPVPM from climatemore » inputs as compared with eddy covariance-based GPP (GPPEC). Results showed that TNARR agreed well with in-situ measurements; RNARR, however, was positively biased. An empirical linear correction was applied to RNARR, and significantly reduced the relative error of RNARR by ~25% for crop site-years. Overall, GPPVPM calculated from the in-situ (GPPVPM(EC)), original (GPPVPM(NARR)) and adjusted NARR (GPPVPM(adjNARR)) climate data tracked the seasonality of GPPEC well, albeit with different degrees of biases. GPPVPM(EC) showed a good match with GPPEC for maize (Zea mays L.), but was slightly underestimated for soybean (Glycine max L.). Replacing the in-situ climate data with the NARR resulted in a significant overestimation of GPPVPM(NARR) (18.4/29.6% for irrigated/rainfed maize and 12.7/12.5% for irrigated/rainfed soybean). GPPVPM(adjNARR) showed a good agreement with GPPVPM(EC) for both crops due to the reduction in the bias of RNARR. The results imply that the bias of RNARR introduced significant uncertainties into the PEM-based GPP estimates, suggesting that more accurate surface radiation datasets are needed to estimate primary production of terrestrial ecosystems at regional and global scales.« less
  • Variability in three Pacific teleconnection patterns are examined to see if net carbon exchange at a low-elevation, old-growth forest is affected by climatic changes associated with these periodicities. Examined are the Pacific Decadal Oscillation (PDO), Pacific/North American Oscillation (PNA) and El Nino-Southern Oscillation (ENSO). We use nine years of eddy covariance CO{sub 2}, H{sub 2}O and energy fluxes measured at the Wind River AmeriFlux site, Washington, USA and eight years of tower-pixel remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to address this question. We compute a new Composite Climate Index (CCI) based on the three Pacific Oscillationsmore » to divide the measurement period into positive- (2003 and 2005), negative- (1999 and 2000) and neutral-phase climate years (2001, 2002, 2004, 2006 and 2007). The forest transitioned from an annual net carbon sink (NEP = + 217 g C m{sup -2} year{sup -1}, 1999) to a source (NEP = - 100 g C m{sup -2} year{sup -1}, 2003) during two dominant teleconnection patterns. Net ecosystem productivity (NEP), water use efficiency (WUE) and light use efficiency (LUE) were significantly different (P < 0.01) during positive (NEP = -0.27 g C m{sup -2} day{sup -1}, WUE = 4.1 mg C/g H{sub 2}O, LUE = 0.94 g C MJ{sup -1}) and negative (NEP = +0.37 g C m{sup -2} day{sup -1}, WUE = 3.4 mg C/g H{sub 2}O, LUE = 0.83 g C MJ{sup -1}) climate phases. The CCI was linked to variability in the MODIS Enhanced Vegetation Index (EVI) but not to MODIS Fraction of absorbed Photosynthetically Active Radiation (FPAR). EVI was highest during negative climate phases (1999 and 2000) and was positively correlated with NEP and showed potential for using MODIS to estimate teleconnection-driven anomalies in ecosystem CO{sub 2} exchange in old-growth forests. This work suggests that any increase in the strength or frequency of ENSO coinciding with in-phase, low frequency Pacific oscillations (PDO and PNA) will likely increase CO{sub 2} uptake variability in Pacific Northwest conifer forests.« less