Improvement of satellite-based estimation of gross primary production through optimization of meteorological parameters and high resolution land cover information at regional scale over East Asia

2019 ◽  
Vol 271 ◽  
pp. 180-192
Author(s):  
Haemi Park ◽  
Jungho Im ◽  
Miae Kim
PLoS ONE ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. e0192041 ◽  
Author(s):  
Heather L. Kimball ◽  
Paul C. Selmants ◽  
Alvaro Moreno ◽  
Steve W. Running ◽  
Christian P. Giardina

2020 ◽  
Author(s):  
Rodolfo Nóbrega ◽  
David Sandoval ◽  
Colin Prentice

<p>Root zone storage capacity (R<sub>z</sub>) is a parameter widely used in terrestrial ecosystem models that estimate the amount of soil moisture available for transpiration. However, R<sub>z</sub> is subject to large uncertainty, due to the lack of data on the distribution of soil properties and the depth of plant roots that actively take up water. Our study makes use of a mass-balance approach to investigate R<sub>z</sub> in different ecosystems, and changes in water fluxes caused by land-cover change. The method needs no land-cover or soil information, and uses precipitation (P) and evapotranspiration (ET) time series to estimate the seasonal water deficit. To account for some of the uncertainty in ET, we use different methods for ET estimation, including methods based on satellite estimates, and modelling approaches that back-calculate ET from other ecosystem fluxes. We show that reduced ET due to land-cover change reduces R<sub>z</sub>, which in turn increases baseflow in regions with a strong rainfall seasonality. This finding allows us to analyse the trade-off between gross primary production and hydrological fluxes at river basin scales. We also consider some ideas on how to use mass-balance R<sub>z</sub> in water-stress functions as incorporated in existing terrestrial ecosystem models.</p>


2017 ◽  
Vol 72 ◽  
pp. 153-164 ◽  
Author(s):  
Hao Shi ◽  
Longhui Li ◽  
Derek Eamus ◽  
Alfredo Huete ◽  
James Cleverly ◽  
...  

PLoS ONE ◽  
2017 ◽  
Vol 12 (9) ◽  
pp. e0184466 ◽  
Author(s):  
Heather L. Kimball ◽  
Paul C. Selmants ◽  
Alvaro Moreno ◽  
Steve W. Running ◽  
Christian P. Giardina

2021 ◽  
Vol 308-309 ◽  
pp. 108609
Author(s):  
Yulong Zhang ◽  
Conghe Song ◽  
Taehee Hwang ◽  
Kimberly Novick ◽  
John W. Coulston ◽  
...  

2019 ◽  
Vol 11 (9) ◽  
pp. 1135 ◽  
Author(s):  
Marta Gómez Giménez ◽  
Rogier de Jong ◽  
Armin Keller ◽  
Beat Rihm ◽  
Michael E. Schaepman

Climate, soil type, and management practices have been reported as primary limiting factors of gross primary production (GPP). However, the extent to which these factors predict GPP response varies according to scales and land cover classes. Nitrogen (N) deposition has been highlighted as an important driver of primary production in N-limited ecosystems that also have an impact on biodiversity in alpine grasslands. However, the effect of N deposition on GPP response in alpine grasslands hasn’t been studied much at a large scale. These remote areas are characterized by complex topography and extensive management practices with high species richness. Remotely sensed GPP products, weather datasets, and available N deposition maps bring along the opportunity of analyzing how those factors predict GPP in alpine grasslands and compare these results with those obtained in other land cover classes with intensive and mixed management practices. This study aims at (i) analyzing the impact of N deposition and climatic variables (precipitation, sunshine, and temperature) on carbon (C) fixation response in alpine grasslands and (ii) comparing the results obtained in alpine grasslands with those from other land cover classes with different management practices. We stratified the analysis using three land cover classes: Grasslands, croplands, and croplands/natural vegetation mosaic and built multiple linear regression models. In addition, we analyzed the soil characteristics, such as aptitude for croplands, stone content, and water and nutrient storage capacity for each class to interpret the results. In alpine grasslands, explanatory variables explained up to 80% of the GPP response. However, the explanatory performance of the covariates decreased to maximums of 47% in croplands and 19% in croplands/natural vegetation mosaic. Further information will improve our understanding of how N deposition affects GPP response in ecosystems with high and mixed intensity of use management practices, and high species richness. Nevertheless, this study helps to characterize large patterns of GPP response in regions affected by local climatic conditions and different land management patterns. Finally, we highlight the importance of including N deposition in C budget models, while accounting for N dynamics.


2021 ◽  
Vol 13 (13) ◽  
pp. 2448
Author(s):  
Tao Yu ◽  
Qiang Zhang ◽  
Rui Sun

Eddy covariance observation is an applicable way to obtain accurate and continuous carbon flux at flux tower sites, while remote sensing technology could estimate carbon exchange and carbon storage at regional and global scales effectively. However, it is still challenging to up-scale the field-observed carbon flux to a regional scale, due to the heterogeneity and the unstable air conditions at the land surface. In this paper, gross primary production (GPP) from ground eddy covariance systems were up-scaled to a regional scale by using five machine learning methods (Cubist regression tree, random forest, support vector machine, artificial neural network, and deep belief network). Then, the up-scaled GPP were validated using GPP at flux tower sites, weighted GPP in the footprint, and MODIS GPP products. At last, the sensitivity of the input data (normalized difference vegetation index, fractional vegetation cover, shortwave radiation, relative humidity and air temperature) to the precision of up-scaled GPP was analyzed, and the uncertainty of the machine learning methods was discussed. The results of this paper indicated that machine learning methods had a great potential in up-scaling GPP at flux tower sites. The validation of up-scaled GPP, using five machine learning methods, demonstrated that up-scaled GPP using random forest obtained the highest accuracy.


Author(s):  
A.R. As-syakur ◽  
T. Osawa ◽  
IW.S. Adnyana

Remote sensing data with high spatial resolution is very useful to provideinformation about Gross Primary Production (GPP) especially over spatial coverage in theurban area. Most models of ecosystem carbon exchange based on remote sensing data usedlight use efficiency (LUE) model. The aim of this research was to analyze the distributionof annual GPP urban area of Denpasar. Two main satellite data used in this study wereALOS/AVNIR-2 and Aster satellite data. Result showed that annual value of GPP usingALOS/AVNIR-2 varied from 0.130 gC m-2 yr-1 to 2586.181 gC m-2 yr-1. Meanwhile, usingAster the value varied from 0.144 gC m-2 yr-1 to 2595.264 gC m-2 yr-1. The annual value ofGPP ALOS was lower than the value of Aster, because ALOS have high spatial resolutionand smaller interval of spectral resolution compared to Aster. Different land use couldeffect the value of GPP, because the different land use has different vegetation type,distribution, and different photosynthetic pathway type. The high spatial resolution of theremote sensing data is crucial to discriminate different land cover types in urban region.With heterogeneous land cover surface, maximum value of GPP using ALOS/AVNIR-2was smaller than that of Aster, however, the annual mean of GPP value usingALOS/AVNIR-2 was higher than that of Aster.


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