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Items: 1 to 20 of 83

1.

Modeling gross primary production of agro-forestry ecosystems by assimilation of satellite-derived information in a process-based model.

Migliavacca M, Meroni M, Busetto L, Colombo R, Zenone T, Matteucci G, Manca G, Seufert G.

Sensors (Basel). 2009;9(2):922-42. doi: 10.3390/s90200922. Epub 2009 Feb 13.

2.

An improved approach for remotely sensing water stress impacts on forest C uptake.

Sims DA, Brzostek ER, Rahman AF, Dragoni D, Phillips RP.

Glob Chang Biol. 2014 Sep;20(9):2856-66. doi: 10.1111/gcb.12537. Epub 2014 Apr 12.

PMID:
24464936
3.

Estimating of gross primary production in an Amazon-Cerrado transitional forest using MODIS and Landsat imagery.

Danelichen VH, Biudes MS, Velasque MC, Machado NG, Gomes RS, Vourlitis GL, Nogueira JS.

An Acad Bras Cienc. 2015 Sep;87(3):1545-64. doi: 10.1590/0001-3765201520140457. Epub 2015 Jul 28.

4.

Development of the BIOME-BGC model for the simulation of managed Moso bamboo forest ecosystems.

Mao F, Li P, Zhou G, Du H, Xu X, Shi Y, Mo L, Zhou Y, Tu G.

J Environ Manage. 2016 May 1;172:29-39. doi: 10.1016/j.jenvman.2015.12.013. Epub 2016 Feb 23.

PMID:
26921563
5.

Validating MODIS and Sentinel-2 NDVI Products at a Temperate Deciduous Forest Site Using Two Independent Ground-Based Sensors.

Lange M, Dechant B, Rebmann C, Vohland M, Cuntz M, Doktor D.

Sensors (Basel). 2017 Aug 11;17(8). pii: E1855. doi: 10.3390/s17081855.

6.

Landscape variation in canopy nitrogen and carbon assimilation in a temperate mixed forest.

Zhou Z, Ollinger SV, Lepine L.

Oecologia. 2018 Oct;188(2):595-606. doi: 10.1007/s00442-018-4223-2. Epub 2018 Jul 12.

PMID:
30003370
7.

Progress in Remote Sensing of Photosynthetic Activity over the Amazon Basin.

de Sousa CHR, Hilker T, Waring R, de Moura YM, Lyapustin A.

Remote Sens (Basel). 2017;9(1):48. doi: 10.3390/rs9010048. Epub 2017 Jan 7.

8.

A model of gross primary productivity based on satellite data suggests formerly afforested peatlands undergoing restoration regain full photosynthesis capacity after five to ten years.

Lees KJ, Quaife T, Artz RRE, Khomik M, Sottocornola M, Kiely G, Hambley G, Hill T, Saunders M, Cowie NR, Ritson J, Clark JM.

J Environ Manage. 2019 Sep 15;246:594-604. doi: 10.1016/j.jenvman.2019.03.040. Epub 2019 Jun 13.

PMID:
31202827
9.

[Temporal and spatial heterogeneity analysis of optimal value of sensitive parameters in ecological process model: The BIOME-BGC model as an example.]

Li YZ, Zhang TL, Liu QY, Li Y.

Ying Yong Sheng Tai Xue Bao. 2018 Jan;29(1):84-92. doi: 10.13287/j.1001-9332.201801.016. Chinese.

PMID:
29692016
10.

Satellite microwave detection of boreal forest recovery from the extreme 2004 wildfires in Alaska and Canada.

Jones MO, Kimball JS, Jones LA.

Glob Chang Biol. 2013 Oct;19(10):3111-22. doi: 10.1111/gcb.12288. Epub 2013 Aug 18.

PMID:
23749682
11.

Combining remote sensing and eddy covariance data to monitor the gross primary production of an estuarine wetland ecosystem in East China.

Wu M, Muhammad S, Chen F, Niu Z, Wang C.

Environ Sci Process Impacts. 2015 Apr;17(4):753-62. doi: 10.1039/c5em00061k.

PMID:
25797359
12.

Terrestrial gross primary production: Using NIRV to scale from site to globe.

Badgley G, Anderegg LDL, Berry JA, Field CB.

Glob Chang Biol. 2019 Jun 14. doi: 10.1111/gcb.14729. [Epub ahead of print]

PMID:
31199543
13.

[Comparison of GIMMS and MODIS normalized vegetation index composite data for Qing-Hai-Tibet Plateau].

Du JQ, Shu JM, Wang YH, Li YC, Zhang LB, Guo Y.

Ying Yong Sheng Tai Xue Bao. 2014 Feb;25(2):533-44. Chinese.

PMID:
24830255
14.

[Simulation of water and carbon fluxes in harvard forest area based on data assimilation method].

Zhang TL, Sun R, Zhang RH, Zhang L.

Ying Yong Sheng Tai Xue Bao. 2013 Oct;24(10):2746-54. Chinese.

PMID:
24483066
15.

Contributions of climate, leaf area index and leaf physiology to variation in gross primary production of six coniferous forests across Europe: a model-based analysis.

Duursma RA, Kolari P, Perämäki M, Pulkkinen M, Mäkelä A, Nikinmaa E, Hari P, Aurela M, Berbigier P, Bernhofer CH, Grünwald T, Loustau D, Mölder M, Verbeeck H, Vesala T.

Tree Physiol. 2009 May;29(5):621-39. doi: 10.1093/treephys/tpp010. Epub 2009 Feb 19.

PMID:
19324698
16.

Chlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest.

Yang H, Yang X, Zhang Y, Heskel MA, Lu X, Munger JW, Sun S, Tang J.

Glob Chang Biol. 2017 Jul;23(7):2874-2886. doi: 10.1111/gcb.13590. Epub 2017 Jan 3.

PMID:
27976474
17.

Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States.

Paciorek CJ, Liu Y; HEI Health Review Committee.

Res Rep Health Eff Inst. 2012 May;(167):5-83; discussion 85-91.

PMID:
22838153
18.

Terrestrial gross primary production inferred from satellite fluorescence and vegetation models.

Parazoo NC, Bowman K, Fisher JB, Frankenberg C, Jones DB, Cescatti A, Pérez-Priego O, Wohlfahrt G, Montagnani L.

Glob Chang Biol. 2014 Oct;20(10):3103-21. doi: 10.1111/gcb.12652. Epub 2014 Jul 21.

PMID:
24909755
19.

Using spatio-temporal fusion of Landsat-8 and MODIS data to derive phenology, biomass and yield estimates for corn and soybean.

Liao C, Wang J, Dong T, Shang J, Liu J, Song Y.

Sci Total Environ. 2019 Feb 10;650(Pt 2):1707-1721. doi: 10.1016/j.scitotenv.2018.09.308. Epub 2018 Sep 26.

PMID:
30273730
20.

Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models.

Xie X, Li A, Jin H, Tan J, Wang C, Lei G, Zhang Z, Bian J, Nan X.

Sci Total Environ. 2019 Nov 10;690:1120-1130. doi: 10.1016/j.scitotenv.2019.06.516. Epub 2019 Jul 2.

PMID:
31470475

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