Abstract
Keywords
1 Introduction
Alpine meadow ecosystems on the Qinghai-Tibetan Plateau, one of the largest soil carbon pools with 11.3 Pg C in China, has a soil carbon density of 18.2 Kg m-2 (
Plant-driven photosynthesis, usually quantified as gross primary production (GPP), is the basis of all the terrestrial biospheric functions (
Remote sensing data can overcome the space and time limitations that hinder the obtaining of field measurements (
In this study, we selected a typical alpine steppe―a
2 Materials and methods
2.1 Site description
The study site is a typical alpine steppe—
2.2 Ground meteorological and plant measurements
Ground meteorological measurements include air temperature at the height of 2 m (Ta), precipitation (PPT), relative humidity, vapor pressure deficit (VPD), soil temperature at a depth of 5 cm (Ts), and soil water content (SWC). Tower-based radiation measurements include observations of standard radiation observations and sub-spectra radiation (photosynthetically active radiation, with wavelengths of 380-710 nm). Standard radiation observations include four radiative components: solar shortwave down-welling radiation (
The sub-spectra radiation observations consist of incident photosynthetically active radiation above the canopy (
In this study, the units of all radiation components were converted to MJ m-2 in order to make pairwise comparisons (
2.3 Satellite-based measurements
Using the study site position as the center pixel, we extracted MODIS 8-day surface reflectance composite data (MOD09A1) and MODIS LAI/FPAR (LAI_MOD/FPAR_ MOD) data for the years 2009 to 2011 from the Land Processes DAAC (http://daac.ornl.gov/MODIS). Based on MOD09A1, we calculated two vegetation indices (NDVI and EVI) using Eq. 4 and Eq. 5, and a Land Surface Water Index (LSWI) using Eq.6 (
where ${{\rho }_{\text{nir}}}$(841-876 nm), ${{\rho }_{\text{red}}}$(620-670 nm), ${{\rho }_{\text{blue}}}$(459- 479 nm), and ${{\rho }_{\text{swir}}}$(459-479 nm) are the four surface reflectance values from different spectral bands.
2.4 Satellite-based FPAR estimations
Except for ground tower-based FPARg, and MODIS FPAR products (FPAR_MOD), FPAR can also be derived from LAIg observations based on the Beer-Lambert law (
where
Using the relationships between
2.5 Statistical analysis
In this study, we reconciled the ground measurements to the 8-day time step as MODIS products were, because we assumed the plants conditions in each 8-day interval were the same (
where
3 Results
3.1 Tower-based radiation measurements
Seasonal variations of all radiation components, including total radiation (Rt), net radiation (Rn), PAR and APAR, showed consistent and single peak patterns in each year from 2009 to 2011 (
The peak value of Rt (DOY 150-160) and PAR (DOY 150-160) occurred earlier than those of Rn (DOY 210-230) and APAR (DOY 250-260) (
Figure 1.
3.2 Comparison of FPAR estimations
The seasonal dynamics of satellite-based FPAR estimations, FPAR_MOD and FAPR_LAI, corresponded well with the observed FPARg from 2009 to 2011 (
Figure 2.
regressions are extremely significant (
Method | Daily average FPAR estimations ( | Mean FPAR | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Mean of SD | RMSE | RPE (%) | ||||||||||
2009 | 2010 | 2011 | 2009 | 2010 | 2011 | Mean | 2009 | 2010 | 2011 | Mean | Mean (SD) | |
FPAR_MOD | 0.19 (0.08) | 0.21 (0.11) | 0.20 (0.09) | 0.16 | 0.19 | 0.16 | 0.17 | 43.1 | 43.0 | 41.9 | 42.7 | 0.20 (0.09) a |
FPAR_LAI | 0.26 (0.16) | 0.30 (0.21) | 0.30 (0.20) | 0.13 | 0.20 | 0.16 | 0.17 | 23.4 | 19.1 | 15.1 | 19.2 | 0.29 (0.19) b |
FPAR_NDVI | 0.34 (0.05) | 0.36 (0.05) | 0.36 (0.06) | 0.07 | 0.06 | 0.03 | 0.05 | -4.2 | 3.8 | -1.3 | -0.5 | 0.36 (0.05) c |
FPAR_EVI | 0.35 (0.05) | 0.36 (0.06) | 0.36 (0.05) | 0.07 | 0.06 | 0.03 | 0.05 | -4.1 | 4.5 | 1.2 | -0.3 | 0.35 (0.05) c |
FPARg | 0.33 (0.08) | 0.37 (0.10) | 0.35 (0.09) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.35 (0.08) c |
Table 1.
Satellite-based FPAR estimations and comparisons with tower-based FPAR observations during the growing seasons from 2009 to 2011.
However, FPAR_MOD and FAPR_LAI showed significant underestimations (
4 Discussion
4.1 Comparison of FPAR estimations
The seasonal dynamics of FPARg measurements from 2009 to 2011were captured by FPAR_MOD in the alpine meadow study area. During the past two decades, most of previous studies assessed the accuracies of MODIS FPAR/LAI and showed that the seasonal patterns of FPARg were in line with the FPAR_MOD (
4.2 Improvements of satellite-based FPAR estimations
Tower-based FPAR_LAI estimations derived from the Beer-Lambert law had a significant underestimation of FPARg by approximately 20% from 2009 to 2011 in the alpine meadow. This may be attributed to the constant
4.3 Satellite-based LAI estimations
LAI_MOD substantially undervalued LAIg measurements rendering values of less than 30% of LAIg, although the values for seasonal dynamics matched well (
where
Figure 3.
Figure 4.
5 Conclusions
This study used tower-based meteorological and plant measurements to estimate the key photosynthetic parameters, FPARg,
References
[1] Baldocchi DD, Matt DR, Hutchison BA et al. Solar radiation within an oak—hickory forest: An evaluation of the extinction coefficients for several radiation components during fully-leafed and leafless periods. Agricultural and Forest Meteorology, 32, 307-322(1984).
[2] Behera SK, SrivastavaP, Pathre UV et al. An indirect method of estimating leaf area index in
[3] CaoX, ZhouZ, ChenX et al. Improving leaf area index simulation of IBIS model and its effect on water carbon and energy—A case study in Changbai Mountain broadleaved forest of China. Ecological Modelling, 303, 97-104(2015).
[4] Chen BX, Zhang XZ, TaoJ et al. The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai-Tibet Plateau. Agricultural and Forest Meteorology, 189-190, 11-18(2014).
[7] Chen JM, Blanken PD, Black TA et al. Radiation regime and canopy architecture in a boreal aspen forest. Agricultural and Forest Meteorology, 86, 107-125(1997).
[8] de Almeida CT, Delgado RC, Galv?o LS et al. Improvements of the MODIS Gross Primary Productivity model based on a comprehensive uncertainty assessment over the Brazilian Amazonia. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 268-283(2018).
[9] DongJ, XiaoX, WagleP et al. Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought. Remote Sensing of Environment, 162, 154-168(2015).
[10] DuM, LiY, ZhangF et al. Recent changes of climate and livestock productions on the Tibetan Plateau and in situ observations of NEE. Journal of Arid Land Studies, 28, 139-142(2018).
[11] FensholtR, SandholtI, Rasmussen M S. Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements. Remote Sensing of Environment, 91, 490-507(2004).
[12] FuG, Shen ZX, Zhang XZ et al. Calibration of MODIS-based gross primary production over an alpine meadow on the Tibetan Plateau. Canadian Journal of Remote Sensing, 38, 157-168(2012).
[13] FuG, Wu J S. Validation of MODIS collection 6 FPAR/LAI in the alpine grassland of the Northern Tibetan Plateau. Remote Sensing Letters, 8, 831-838(2017).
[16] Gitelson AA, Vi?aA, Verma SB et al. Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity. Journal of Geophysical Research: Atmospheres, 111, 1-13(2006).
[17] Hanan NP, BurbaG, Verma SB et al. Inversion of net ecosystem CO2 flux measurements for estimation of canopy PAR absorption. Global Change Biology, 8, 563-574(2002).
[18] Heinsch FA, ReevesM, VotavaP et al. GPP and NPP (MOD17A2/ A3) Products NASA MODIS Land Algorithm. MOD17 User's Guide:, 1-57(2003).
[21] LiangT, YangS, FengQ et al. Multi-factor modeling of above- ground biomass in alpine grassland: A case study in the Three-River Headwaters Region, China. Remote Sensing of Environment, 186, 164-172(2016).
[23] NiJ. Carbon storage in grasslands of China. Journal of Arid Environments, 50, 205-218(2002).
[27] NiuB, ZhangX, HeY et al. Satellite-based estimation of gross primary production in an alpine swamp meadow on the Tibetan Plateau: A multi-model comparison. Journal of Resources and Ecology, 8, 57-66(2017).
[28] OlofssonP, EklundhL. Estimation of absorbed PAR across Scandinavia from satellite measurements. Part II: Modeling and evaluating the fractional absorption. Remote Sensing of Environment, 110, 240-251(2007).
[29] PiaoS, ZhangX, WangT et al. Responses and feedback of the Tibetan Plateau’s alpine ecosystem to climate change. Chinese Science Bulletin, 64, 2842-2855(2019).
[31] RuimyA, KergoatL, BondeauA et al. Comparing global models of terrestrial net primary productivity (NPP): Analysis of differences in light absorption and light-use efficiency. Global Change Biology, 5, 56-64(1999).
[32] Running SW, Thornton PE, NemaniR et al. Global terrestrial gross and net primary productivity from the earth observing system, In: Sala O E, Jackson R B, Mooney H A, et al. (ed.), 44-57(2000).
[33] SaitoM, KatoT, Tang Y. Temperature controls ecosystem CO2 exchange of an alpine meadow on the northeastern Tibetan Plateau. Global Change Biology, 15, 221-228(2009).
[34] Saitoh TM, NagaiS, Noda HM et al. Examination of the extinction coefficient in the Beer-Lambert law for an accurate estimation of the forest canopy leaf area index. Forest Science and Technology, 8, 67-76(2012).
[35] Shi PL, Sun XM, Xu LL et al. Net ecosystem CO2 exchange and controlling factors in a steppe -
[36] TangX, LiH, HuangN et al. A comprehensive assessment of MODIS-derived GPP for forest ecosystems using the site-level FLUXNET database. Environmental Earth Sciences, 74, 5907-5918(2015).
[37] Turner DP, Ritts WD, Cohen WB et al. Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring. Global Change Biology, 11, 666-684(2005).
[39] Varlet-GrancherC, BonhommeR, JacobC et al. Caracterisation et evolution de la structure d'un couvert vegetal de canne a sucre, Annales agronomiques(1980).
[40] VermaM, Friedl MA, Law BE et al. Improving the performance of remote sensing models for capturing intra- and inter-annual variations in daily GPP: An analysis using global FLUXNET tower data. Agricultural and Forest Meteorology, 214-, 215, 416-429(2015).
[50] YangX, TangJ, Mustard JF et al. Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophysical Research Letters, 42, 2977-2987(2015).
[51] YinG, LiA, JinH et al. Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO. Agricultural and Forest Meteorology, 233, 209-221(2017).
[52] ZhangB, ShiP, HeY et al. The climate feature of Damxung alpine meadow carbon flux research station on the Tibetan Plateau. Journal of Mountain Science, 27, 88-95(2009).
[54] ZhangX, YangY, PiaoS et al. Ecological change on the Tibetan Plateau. Chinese Science Bulletin, 60, 3048-3056(2015).
[55] ZhangY, YuQ, Jiang J IE et al. Calibration of Terra/MODIS gross primary production over an irrigated cropland on the North China Plain and an alpine meadow on the Tibetan Plateau. Global Change Biology, 14, 757-767(2008).
[56] ZhaoL, LiY, XuS et al. Diurnal, seasonal and annual variation in net ecosystem CO2 exchange of an alpine shrubland on Qinghai-Tibetan Plateau. Global Change Biology, 12, 1940-1953(2006).
[57] ZhengD, ZhangQ, WuS. Mountain geoecology and sustainable development of the Tibetan Plateau. Dordrecht:. Springer.(2000).
[58] ZhengY, ZhangL, XiaoJ et al. Sources of uncertainty in gross primary productivity simulated by light use efficiency models: Model structure, parameters, input data, and spatial resolution. Agricultural and Forest Meteorology, 263, 242-257(2018).
[61] ZhuZ, PiaoS, LianX et al. Attribution of seasonal leaf area index trends in the northern latitudes with “optimally” integrated ecosystem models. Global Change Biology, 23, 4798-4813(2017).
Set citation alerts for the article
Please enter your email address