API

Collector

Gridded datasets

GriddingMachine.Collector.GriddedCollection — Type

struct GriddedCollection

Structure for general gridded dataset collection.

Fields

LABEL::String: Artifact label name
SUPPORTED_COMBOS::Vector{String}: Supported combinations
DEFAULT_COMBO::String: Default combination

Examples

vcmax_collection = GriddedCollection("VCMAX", ["2X_1Y_V1", "2X_1Y_V2"], "2X_1Y_V2");

source

GriddingMachine.Collector.biomass_collection — Function

biomass_collection()

Method to create a general dataset collection for biomass. Supported datasets are (click to view bibtex items)

ROOT_120X_1Y_V1 (Huang et al., 2021)
SHOOT_120X_1Y_V2 (Santoro et al., 2021)

@article{huang2021global,
    author = {Huang, Y. and Ciais, P. and Santoro, M. and Makowski, D. and Chave, J. and Schepaschenko, D. and Abramoff, R. Z. and Goll, D. S. and Yang, H. and Chen, Y. and Wei, W. and Piao, S.},
    year = {2021},
    title = {A global map of root biomass across the world's forests},
    journal = {Earth System Science Data},
    volume = {13},
    number = {9},
    pages = {4263–4274}
}
@article{santoro2021global,
    author = {Santoro, M. and Cartus, O. and Carvalhais, N. and Rozendaal, D. M. A. and Avitabile, V. and Araza, A. and de Bruin, S. and Herold, M. and Quegan, S. and Rodr{\'\i}guez-Veiga, P. and
              Balzter, H. and Carreiras, J. and Schepaschenko, D. and Korets, M. and Shimada, M. and Itoh, T. and {Moreno Mart{\'\i}nez}, {\'A}. and Cavlovic, J. and {Cazzolla Gatti}, R. and
              da Concei{\c c}\~ao Bispo, P. and Dewnath, N. and Labri{\`e}re, N. and Liang, J. and Lindsell, J. and Mitchard, E. T. A. and Morel, A. and {Pacheco Pascagaza}, A. M. and
              Ryan, C. M. and Slik, F. and {Vaglio Laurin}, G. and Verbeeck, H. and Wijaya, A. and Willcock, S.},
    year = {2021},
    title = {The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations},
    journal = {Earth System Science Data},
    volume = {13},
    number = {8},
    pages = {3927--3950}
}

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GriddingMachine.Collector.canopy_height_collection — Function

canopy_height_collection()

Method to create a general dataset collection for canopy height. Supported datasets are (click to view bibtex items)

20X_1Y_V1 (Simard et al., 2011)
2X_1Y_V2 (Boonman et al., 2020)

@article{simard2011mapping,
    author = {Simard, Marc and Pinto, Naiara and Fisher, Joshua B and Baccini, Alessandro},
    year = {2011},
    title = {Mapping forest canopy height globally with spaceborne lidar},
    journal = {Journal of Geophysical Research: Biogeosciences},
    volume = {116},
    number = {G4021}
}
@article{boonman2020assessing,
    author = {Boonman, Coline CF and Ben{\'i}tez-L{\'o}pez, Ana and Schipper, Aafke M and Thuiller, Wilfried and Anand, Madhur and Cerabolini, Bruno EL and Cornelissen, Johannes HC and
              Gonzalez-Melo, Andres and Hattingh, Wesley N and Higuchi, Pedro and others},
    year = {2020},
    title = {Assessing the reliability of predicted plant trait distributions at the global scale},
    journal = {Global Ecology and Biogeography},
    volume = {29},
    number = {6},
    pages = {1034--1051}
}

source

GriddingMachine.Collector.clumping_index_collection — Function

clumping_index_collection()

Method to create a general dataset collection for clumping index. Supported datasets are (click to view bibtex items)

240X_1Y_V1 (He et al., 2012)
2X_1Y_V1 (regridded; He et al., 2012)
2X_1Y_V2 (Braghiere et al., 2019)
20X_1M_V3 (Wei et al., 2019)
2X_1M_V3 (regridded; Wei et al., 2019)

V2 dataset are classified for different plant functional types. The indices are Broadleaf, Needleleaf, C3 grasses, C4 grasses, and shrubland.

@article{he2012global,
    author={He, Liming and Chen, Jing M and Pisek, Jan and Schaaf, Crystal B and Strahler, Alan H},
    year={2012},
    title={Global clumping index map derived from the MODIS BRDF product},
    journal={Remote Sensing of Environment},
    volume={119},
    pages={118--130}
}
@article{braghiere2019underestimation,
    author = {Braghiere, R{\'e}nato Kerches and Quaife, T and Black, E and He, L and Chen, JM},
    year = {2019},
    title = {Underestimation of global photosynthesis in Earth System Models due to representation of vegetation structure},
    journal = {Global Biogeochemical Cycles},
    volume = {33},
    number = {11},
    pages = {1358--1369}
}
@article{wei2019global,
    author = {Wei, Shanshan and Fang, Hongliang and Schaaf, Crystal B and He, Liming and Chen, Jing M},
    year = {2019},
    title = {Global 500 m clumping index product derived from MODIS BRDF data (2001--2017)},
    journal = {Remote Sensing of Environment},
    volume = {232},
    pages = {111296}
}

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GriddingMachine.Collector.elevation_collection — Function

elevation_collection()

Method to create a general dataset collection for surface elevation. Supported datasets are (click to view bibtex items)

2X_1Y_V1 (Yamazaki et al., 2017)

@article{yamazaki2017high,
    author = {Yamazaki, Dai and Ikeshima, Daiki and Tawatari, Ryunosuke and Yamaguchi, Tomohiro and O'Loughlin, Fiachra and Neal, Jeffery C and Sampson, Christopher C and Kanae, Shinjiro and
              Bates, Paul D},
    year = {2017},
    title = {A high-accuracy map of global terrain elevations},
    journal = {Geophysical Research Letters},
    volume = {44},
    number = {11},
    pages = {5844--5853}
}

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GriddingMachine.Collector.gpp_collection — Function

gpp_collection()

Method to create a general dataset collection for gross primary productivity. Supported datasets are (click to view bibtex items)

MPI_RS_2X_1M_YYYY_V1 (YYYY from 2001 to 2019; Tramontana et al., 2016)
MPI_RS_2X_8D_YYYY_V1 (YYYY from 2001 to 2019; Tramontana et al., 2016)
VPM_5X_8D_YYYY_V2 (YYYY from 2000 to 2019; Zhang et al., 2017)
VPM_12X_8D_YYYY_V2 (YYYY from 2000 to 2019; Zhang et al., 2017)

@article{tramontana2016predicting,
    author = {Tramontana, Gianluca and Jung, Martin and Schwalm, Christopher R and Ichii, Kazuhito and Camps-Valls, Gustau and R{\'a}duly, Botond and Reichstein, Markus and Arain, M Altaf and
              Cescatti, Alessandro and Kiely, Gerard and others},
    year = {2016},
    title = {Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms},
    journal = {Biogeosciences},
    volume = {13},
    number = {14},
    pages = {4291--4313}
}
@article{zhang2017global,
    author = {Zhang, Yao and Xiao, Xiangming and Wu, Xiaocui and Zhou, Sha and Zhang, Geli and Qin, Yuanwei and Dong, Jinwei},
    year = {2017},
    title = {A global moderate resolution dataset of gross primary production of vegetation for 2000--2016},
    journal = {Scientific data},
    volume = {4},
    pages = {170165}
}

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GriddingMachine.Collector.lai_collection — Function

lai_collection()

Method to create a general dataset collection for leaf area index. Supported datasets are (click to view bibtex items)

MODIS_2X_1M_YYYY_V1 (YYYY from 2000 to 2020; Yuan et al., 2011)
MODIS_2X_8D_YYYY_V1 (YYYY from 2000 to 2020; Yuan et al., 2011)
MODIS_10X_1M_YYYY_V1 (YYYY from 2000 to 2020; Yuan et al., 2011)
MODIS_10X_8D_YYYY_V1 (YYYY from 2000 to 2020; Yuan et al., 2011)
MODIS_20X_1M_YYYY_V1 (YYYY from 2000 to 2020; Yuan et al., 2011)
MODIS_20X_8D_YYYY_V1 (YYYY from 2000 to 2020; Yuan et al., 2011)

@article{yuan2011reprocessing,
    author = {Yuan, Hua and Dai, Yongjiu and Xiao, Zhiqiang and Ji, Duoying and Shangguan, Wei},
    year = {2011},
    title = {Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling},
    journal = {Remote Sensing of Environment},
    volume = {115},
    number = {5},
    pages = {1171--1187}
}

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GriddingMachine.Collector.land_mask_collection — Function

land_mask_collection()

Method to create a general dataset collection for land mask. Supported datasets are (click to view bibtex items)

4X_1Y_V1 [(ERA5)]

source

GriddingMachine.Collector.latent_heat_collection — Function

latent_heat_collection()

Method to create a general dataset collection for latent heat flux. Supported datasets are (click to view bibtex items)

MPI_RS_2X_8D_YYYY_V1 (YYYY from 2001 to 2015; Jung et al., 2015)
MPI_RS_2X_1M_YYYY_V1 (YYYY from 2001 to 2015; Jung et al., 2015)

@article{jung2019fluxcom,
    author = {Jung, Martin and Koirala, Sujan and Weber, Ulrich and Ichii, Kazuhito and Gans, Fabian and Camps-Valls, Gustau and Papale, Dario and Schwalm, Christopher and Tramontana, Gianluca and
              Reichstein, Markus},
    year = {2019},
    title = {The {FLUXCOM} ensemble of global land-atmosphere energy fluxes},
    journal = {Scientific Data},
    volume = {6},
    number = {1},
    pages = {74}
}

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GriddingMachine.Collector.leaf_chlorophyll_collection — Function

leaf_chlorophyll_collection()

Method to create a general dataset collection for leaf chlorophyll content. Supported datasets are (click to view bibtex items)

2X_7D_V1 (Croft et al., 2017)

@article{croft2020global,
    author = {Croft, H and Chen, JM and Wang, R and Mo, G and Luo, S and Luo, X and He, L and Gonsamo, A and Arabian, J and Zhang, Y and others},
    year = {2020},
    title = {The global distribution of leaf chlorophyll content},
    journal = {Remote Sensing of Environment},
    volume = {236},
    pages = {111479},
}

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GriddingMachine.Collector.leaf_drymass_collection — Function

leaf_drymass_collection()

Method to create a general dataset collection for leaf dry mass content. Supported datasets are (click to view bibtex items)

12X_1Y_V1 (Moreno-Martinez et al., 2018)
36X_1Y_V1 (Moreno-Martinez et al., 2018)

@article{moreno2018methodology,
    author = {Moreno-Mart{\'i}nez, {\'A}lvaro and Camps-Valls, Gustau and Kattge, Jens and Robinson, Nathaniel and Reichstein, Markus and van Bodegom, Peter and Kramer, Koen and
              Cornelissen, J Hans C and Reich, Peter and Bahn, Michael and others},
    year = {2018},
    title = {A methodology to derive global maps of leaf traits using remote sensing and climate data},
    journal = {Remote sensing of environment},
    volume = {218},
    pages = {69--88}
}

source

GriddingMachine.Collector.leaf_nitrogen_collection — Function

leaf_nitrogen_collection()

Method to create a general dataset collection for leaf nitrogen content. Supported datasets are (click to view bibtex items)

2X_1Y_V1 (Butler et al., 2017)
2X_1Y_V2 (Boonman et al., 2020)
12X_1Y_V3 (Moreno-Martinez et al., 2018)
36X_1Y_V3 (Moreno-Martinez et al., 2018)

@article{butler2017mapping,
    author = {Butler, Ethan E and Datta, Abhirup and Flores-Moreno, Habacuc and Chen, Ming and Wythers, Kirk R and Fazayeli, Farideh and Banerjee, Arindam and Atkin, Owen K and Kattge, Jens and
              Amiaud, Bernard and others},
    year = {2017},
    title = {Mapping local and global variability in plant trait distributions},
    journal = {Proceedings of the National Academy of Sciences},
    volume = {114},
    number = {51},
    pages = {E10937--E10946}
}
@article{boonman2020assessing,
    author = {Boonman, Coline CF and Ben{\'i}tez-L{\'o}pez, Ana and Schipper, Aafke M and Thuiller, Wilfried and Anand, Madhur and Cerabolini, Bruno EL and Cornelissen, Johannes HC and
              Gonzalez-Melo, Andres and Hattingh, Wesley N and Higuchi, Pedro and others},
    year = {2020},
    title = {Assessing the reliability of predicted plant trait distributions at the global scale},
    journal = {Global Ecology and Biogeography},
    volume = {29},
    number = {6},
    pages = {1034--1051}
}
@article{moreno2018methodology,
    author = {Moreno-Mart{\'i}nez, {\'A}lvaro and Camps-Valls, Gustau and Kattge, Jens and Robinson, Nathaniel and Reichstein, Markus and van Bodegom, Peter and Kramer, Koen and
              Cornelissen, J Hans C and Reich, Peter and Bahn, Michael and others},
    year = {2018},
    title = {A methodology to derive global maps of leaf traits using remote sensing and climate data},
    journal = {Remote sensing of environment},
    volume = {218},
    pages = {69--88}
}

source

GriddingMachine.Collector.leaf_phosphorus_collection — Function

leaf_phosphorus_collection()

Method to create a general dataset collection for leaf phosphorus content. Supported datasets are (click to view bibtex items)

2X_1Y_V1 (Butler et al., 2017)
12X_1Y_V2 (Moreno-Martinez et al., 2018)
36X_1Y_V2 (Moreno-Martinez et al., 2018)

@article{butler2017mapping,
    author = {Butler, Ethan E and Datta, Abhirup and Flores-Moreno, Habacuc and Chen, Ming and Wythers, Kirk R and Fazayeli, Farideh and Banerjee, Arindam and Atkin, Owen K and Kattge, Jens and
              Amiaud, Bernard and others},
    year = {2017},
    title = {Mapping local and global variability in plant trait distributions},
    journal = {Proceedings of the National Academy of Sciences},
    volume = {114},
    number = {51},
    pages = {E10937--E10946}
}
@article{moreno2018methodology,
    author = {Moreno-Mart{\'i}nez, {\'A}lvaro and Camps-Valls, Gustau and Kattge, Jens and Robinson, Nathaniel and Reichstein, Markus and van Bodegom, Peter and Kramer, Koen and
              Cornelissen, J Hans C and Reich, Peter and Bahn, Michael and others},
    year = {2018},
    title = {A methodology to derive global maps of leaf traits using remote sensing and climate data},
    journal = {Remote sensing of environment},
    volume = {218},
    pages = {69--88}
}

source

GriddingMachine.Collector.pft_collection — Function

pft_collection()

Method to create a general dataset collection for plant function type ratio. Supported datasets are (click to view bibtex items)

2X_1Y_V1 (Lawrence and Chase, 2007)

@article{lawrence2007representing,
    author = {Lawrence, Peter J and Chase, Thomas N},
    year = {2007},
    title = {Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0)},
    journal = {Journal of Geophysical Research: Biogeosciences},
    volume = {112},
    pages = {G01023}
}

source

GriddingMachine.Collector.sif_collection — Function

sif_collection()

Method to create a general dataset collection for solar-induced chlorophyll fluorescence. Supported datasets are (click to view bibtex items)

TROPOMI_683_nX_1M_YYYY_V2 (YYYY from 2018 to 2020; Köhler et al., 2020) n = [1,2,4,5,12]
TROPOMI_683_nX_8D_YYYY_V2 (YYYY from 2018 to 2020; Köhler et al., 2020) n = [1,2,4,5,12]
TROPOMI_683DC_nX_1M_YYYY_V2 (YYYY from 2018 to 2020; Köhler et al., 2020) n = [1,2,4,5,12]
TROPOMI_683DC_nX_8D_YYYY_V2 (YYYY from 2018 to 2020; Köhler et al., 2020) n = [1,2,4,5,12]
TROPOMI_740_nX_1M_YYYY_V1 (YYYY from 2018 to 2022; Köhler et al., 2018) n = [1,2,4,5,12]
TROPOMI_740_nX_8D_YYYY_V1 (YYYY from 2018 to 2022; Köhler et al., 2018)) n = [1,2,4,5,12]
TROPOMI_740DC_nX_1M_YYYY_V1 (YYYY from 2018 to 2022; Köhler et al., 2018) n = [1,2,4,5,12]
TROPOMI_740DC_nX_8D_YYYY_V1 (YYYY from 2018 to 2022; Köhler et al., 2018) n = [1,2,4,5,12]
OCO2_757_5X_1M_YYYY_V3 (YYYY from 2014 to 2020; Sun et al., 2017)
OCO2_757DC_5X_1M_YYYY_V3 (YYYY from 2014 to 2020; Sun et al., 2017)
OCO2_771_5X_1M_YYYY_V3 (YYYY from 2014 to 2020; Sun et al., 2017)
OCO2_771DC_5X_1M_YYYY_V3 (YYYY from 2014 to 2020; Sun et al., 2017)

@article{kohler2018global,
    author = {K{\"o}hler, Philipp and Frankenberg, Christian and Magney, Troy S and Guanter, Luis and Joiner, Joanna and Landgraf, Jochen},
    year = {2018},
    title = {Global retrievals of solar-induced chlorophyll fluorescence with {TROPOMI}: {F}irst results and intersensor comparison to {OCO-2}},
    journal = {Geophysical Research Letters},
    volume = {45},
    number = {19},
    pages = {10,456--10,463}
}
@article{kohler2020global,
    author = {K{\"o}hler, Philipp and Behrenfeld, Michael J and Landgraf, Jochen and Joiner, Joanna and Magney, Troy S and Frankenberg, Christian},
    year = {2020},
    title = {Global retrievals of solar-induced chlorophyll fluorescence at red wavelengths with {TROPOMI}},
    journal = {Geophysical Research Letters},
    volume = {47},
    number = {15},
    pages = {e2020GL087541}
}
@article{sun2017oco,
    author = {Sun, Ying and Frankenberg, Christian and Wood, Jeffery D and Schimel, DS and Jung, Martin and Guanter, Luis and Drewry, DT and Verma, Manish and Porcar-Castell, Albert and Griffis, Timothy J and others},
    year = {2017},
    title = {OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence},
    journal = {Science},
    volume = {358},
    number = {6360}
}

source

GriddingMachine.Collector.sil_collection — Function

sil_collection()

Method to create a general dataset collection for solar-induced luminescence. Supported datasets are (click to view bibtex items)

SIL_20X_1Y_V1 (Köhler et al., 2021)

@article{kohler2021mineral,
    author = {K{\"o}hler, Philipp and Fischer, Woodward W and Rossman, George R and Grotzinger, John P and Doughty, Russell and Wang, Yujie and Yin, Yi and Frankenberg, Christian},
    year = {2021},
    title = {Mineral luminescence observed from space},
    journal = {Geophysical Research Letters},
    volume = {48},
    number = {19},
    pages = {e2021GL095227}
}

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GriddingMachine.Collector.sla_collection — Function

sla_collection()

Method to create a general dataset collection for SLA (specific leaf area). Supported datasets are (click to view bibtex items)

2X_1Y_V1 (Butler et al., 2017)
2X_1Y_V2 (Boonman et al., 2020)
12X_1Y_V3 (Moreno-Martinez et al., 2018)
36X_1Y_V3 (Moreno-Martinez et al., 2018)

@article{butler2017mapping,
    author = {Butler, Ethan E and Datta, Abhirup and Flores-Moreno, Habacuc and Chen, Ming and Wythers, Kirk R and Fazayeli, Farideh and Banerjee, Arindam and Atkin, Owen K and Kattge, Jens and
              Amiaud, Bernard and others},
    year = {2017},
    title = {Mapping local and global variability in plant trait distributions},
    journal = {Proceedings of the National Academy of Sciences},
    volume = {114},
    number = {51},
    pages = {E10937--E10946}
}
@article{boonman2020assessing,
    author = {Boonman, Coline CF and Ben{\'i}tez-L{\'o}pez, Ana and Schipper, Aafke M and Thuiller, Wilfried and Anand, Madhur and Cerabolini, Bruno EL and Cornelissen, Johannes HC and
              Gonzalez-Melo, Andres and Hattingh, Wesley N and Higuchi, Pedro and others},
    year = {2020},
    title = {Assessing the reliability of predicted plant trait distributions at the global scale},
    journal = {Global Ecology and Biogeography},
    volume = {29},
    number = {6},
    pages = {1034--1051}
}
@article{moreno2018methodology,
    author = {Moreno-Mart{\'i}nez, {\'A}lvaro and Camps-Valls, Gustau and Kattge, Jens and Robinson, Nathaniel and Reichstein, Markus and van Bodegom, Peter and Kramer, Koen and
              Cornelissen, J Hans C and Reich, Peter and Bahn, Michael and others},
    year = {2018},
    title = {A methodology to derive global maps of leaf traits using remote sensing and climate data},
    journal = {Remote sensing of environment},
    volume = {218},
    pages = {69--88}
}

source

GriddingMachine.Collector.soil_color_collection — Function

soil_color_collection()

Method to create a general dataset collection for soil color class to use with soil albedo. Supported datasets are (click to view bibtex items)

2X_1Y_V1 (Lawrence and Chase, 2007)

@article{lawrence2007representing,
    author = {Lawrence, Peter J and Chase, Thomas N},
    year = {2007},
    title = {Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0)},
    journal = {Journal of Geophysical Research: Biogeosciences},
    volume = {112},
    pages = {G01023}
}

source

GriddingMachine.Collector.soil_hydraulics_collection — Function

soil_hydraulics_collection()

Method to create a general dataset collection for soil hydraulic parameters (saturated hydraulic conductance - KSAT, residual soil water content - SWCR, saturated soil water content - SWCS, van Genuchten α - VGA, van Genuchten n - VGN). Supported datasets are (click to view bibtex items)

SWCR_120X_1Y_V1 (Dai et al., 2019)
SWCR_12X_1Y_V1 (regridded; Dai et al., 2019)
SWCS_120X_1Y_V1 (Dai et al., 2019)
SWCS_12X_1Y_V1 (regridded; Dai et al., 2019)
VGA_120X_1Y_V1 (Dai et al., 2019)
VGA_12X_1Y_V1 (regridded; Dai et al., 2019)
VGN_120X_1Y_V1 (Dai et al., 2019)
VGN_12X_1Y_V1 (regridded; Dai et al., 2019)
KSAT_100X_1Y_V2 (Gupta et al., 2021)

@article{dai2019global,
    author = {Dai, Yongjiu and Xin, Qinchuan and Wei, Nan and Zhang, Yonggen and Shangguan, Wei and Yuan, Hua and Zhang, Shupeng and Liu, Shaofeng and Lu, Xingjie},
    year = {2019},
    title = {A global high-resolution data set of soil hydraulic and thermal properties for land surface modeling},
    journal = {Journal of Advances in Modeling Earth Systems},
    volume = {11},
    number = {9},
    pages = {2996--3023}
}
@article{gupta2021global,
    author = {Gupta, Surya and Lehmann, Peter and Bonetti, Sara and Papritz, Andreas and Or, Dani},
    year = {2021},
    title = {Global Prediction of Soil Saturated Hydraulic Conductivity Using Random Forest in a Covariate-Based GeoTransfer Function (CoGTF) Framework},
    journal = {Journal of Advances in Modeling Earth Systems},
    volume = {13},
    number = {4},
    pages = {e2020MS002242}
}

source

GriddingMachine.Collector.surface_area_collection — Function

surface_area_collection()

Method to create a general dataset collection for earth surface area. Supported datasets are (click to view bibtex items)

2X_1Y_V1 (Lawrence and Chase, 2007)
1X_1Y_V1 (regridded; Lawrence and Chase, 2007)

@article{lawrence2007representing,
    author = {Lawrence, Peter J and Chase, Thomas N},
    year = {2007},
    title = {Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0)},
    journal = {Journal of Geophysical Research: Biogeosciences},
    volume = {112},
    pages = {G01023}
}

source

GriddingMachine.Collector.tree_density_collection — Function

tree_density_collection()

Method to create a general dataset collection for tree density (number of trees per area). Supported datasets are (click to view bibtex items)

120X_1Y_V1 (Crowther et al., 2017)
2X_1Y_V1 (regridded; Crowther et al., 2020)

@article{crowther2015mapping,
    author = {Crowther, Thomas W and Glick, Henry B and Covey, Kristofer R and Bettigole, Charlie and Maynard, Daniel S and Thomas, Stephen M and Smith, Jeffrey R and Hintler, Gregor and
              Duguid, Marlyse C and Amatulli, Giuseppe and others},
    year = {2015},
    title = {Mapping tree density at a global scale},
    journal = {Nature},
    volume = {525},
    number = {7568},
    pages = {201--205}
}

source

GriddingMachine.Collector.vcmax_collection — Function

vcmax_collection()

Method to create a general dataset collection for Vcmax. Supported datasets are (click to view bibtex items)

2X_1Y_V1 (Smith et al., 2019)
2X_1Y_V2 (Luo et al., 2019)

@article{smith2019global,
    author = {Smith, Nicholas G. and Keenan, Trevor F. and Prentice, I. Colin and Wang, Han and Wright, Ian J. and Niinemets, Ülo and Crous, Kristine Y. and Domingues, Tomas F. and
              Guerrieri, Rossella and {Yoko Ishida}, F. and Zhou, Shuangxi},
    year = {2019},
    title = {Global photosynthetic capacity is optimized to the environment},
    journal = {Ecology Letters},
    volume = {22},
    number = {3},
    pages = {506–517}
}
@article{luo2021global,
    author = {Luo, Xiangzhong and Keenan, Trevor F. and Chen, Jing M. and Croft, Holly and {Colin Prentice}, I. and Smith, Nicholas G. and Walker, Anthony P. and Wang, Han and Wang, Rong and
              Xu, Chonggang and Zhang, Yao},
    year = {2021},
    title = {Global variation in the fraction of leaf nitrogen allocated to photosynthesis},
    journal = {Nature Communications},
    volume = {12},
    number = {1},
    pages = {4866}
}

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GriddingMachine.Collector.vegetation_cover_fraction — Function

vegetation_cover_fraction()

Method to create a general dataset collection for vegetation cover fraction. Supported datasets are (click to view bibtex items)

MODIS_MOD44B_2X_1Y_V1 (DiMiceli et al., 2022)

@article{dimiceli2022modismod44b,
    author = {DiMiceli, C. and Sohlberg, R. and Townshend, J.},
    doi = {10.5067/MODIS/MOD44B.061},
    year = {2022},
    title = {MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V061},
    journal = {NASA EOSDIS Land Processes DAAC}
}

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GriddingMachine.Collector.wood_density_collection — Function

wood_density_collection()

Method to create a general dataset collection for wood density. Supported datasets are (click to view bibtex items)

2X_1Y_V1 (Boonman et al., 2020)

@article{boonman2020assessing,
    author = {Boonman, Coline CF and Ben{\'i}tez-L{\'o}pez, Ana and Schipper, Aafke M and Thuiller, Wilfried and Anand, Madhur and Cerabolini, Bruno EL and Cornelissen, Johannes HC and
              Gonzalez-Melo, Andres and Hattingh, Wesley N and Higuchi, Pedro and others},
    year = {2020},
    title = {Assessing the reliability of predicted plant trait distributions at the global scale},
    journal = {Global Ecology and Biogeography},
    volume = {29},
    number = {6},
    pages = {1034--1051}
}

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Query gridded datasets

GriddingMachine.Collector.query_collection — Function

query_collection(ds::GriddedCollection)
query_collection(ds::GriddedCollection, version::String)
query_collection(artname::String)

This method queries the local data path from collection for the default data, given

ds GriddedCollection type collection
version Queried dataset version (must be in ds.SUPPORTED_COMBOS)
artname Artifact name

Examples

dat_file = query_collection(canopy_height_collection());
dat_file = query_collection(canopy_height_collection(), "20X_1Y_V1");

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Clean up collections

GriddingMachine.Collector.clean_collections! — Function

clean_collections!(selection::String="old")
clean_collections!(selection::Vector{String})
clean_collections!(selection::GriddedCollection)

This method cleans up all selected artifacts of GriddingMachine.jl (through identify the GRIDDINGMACHINE file in the artifacts), given

selection
- A string indicating which artifacts to clean up
  - old Artifacts from an old version of GriddingMachine.jl (default)
  - all All Artifacts from GriddingMachine.jl
- A vector of artifact names
- A GriddedCollection type collection

Examples

clean_collections!();
clean_collections!("old");
clean_collections!("all");
clean_collections!(["PFT_2X_1Y_V1"]);
clean_collections!(pft_collection());

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Sync collections

GriddingMachine.Collector.sync_collections! — Function

sync_collections!()
sync_collections!(gcs::GriddedCollection)

Sync collection datasets to local drive, given

gc GriddedCollection type collection

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Indexer

GriddingMachine.Indexer.lat_ind — Function

lat_ind(lat::Number; res::Number=1)

Round the latitude and return the index in a matrix, given

lat Latitude
res Resolution in latitude

Examples

ilat = lat_ind(0.3);
ilat = lat_ind(0.3; res=0.5);

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GriddingMachine.Indexer.lon_ind — Function

lon_ind(lon::Number; res::Number=1)

Round the longitude and return the index in a matrix, given

lon Longitude
res Resolution in longitude

Examples

ilon = lon_ind(90.3);
ilon = lon_ind(90.3; res=0.5);

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GriddingMachine.Indexer.read_LUT — Function

This function reads look-up-table (LUT) for whole dataset or selected pieces. Supported methods are

read_LUT(fn)
read_LUT(fn, FT)

defined at /home/runner/work/GriddingMachine.jl/GriddingMachine.jl/src/Indexer.jl:74.

read_LUT(fn, cyc)
read_LUT(fn, cyc, FT)

defined at /home/runner/work/GriddingMachine.jl/GriddingMachine.jl/src/Indexer.jl:96.

read_LUT(fn, lat, lon, res)
read_LUT(fn, lat, lon, res, FT; interpolation)

defined at /home/runner/work/GriddingMachine.jl/GriddingMachine.jl/src/Indexer.jl:123.

read_LUT(fn, lat, lon)
read_LUT(fn, lat, lon, FT; interpolation)

defined at /home/runner/work/GriddingMachine.jl/GriddingMachine.jl/src/Indexer.jl:216.

read_LUT(fn, lat, lon, cyc, res)
read_LUT(fn, lat, lon, cyc, res, FT; interpolation)

defined at /home/runner/work/GriddingMachine.jl/GriddingMachine.jl/src/Indexer.jl:246.

read_LUT(fn, lat, lon, cyc)
read_LUT(fn, lat, lon, cyc, FT; interpolation)

defined at /home/runner/work/GriddingMachine.jl/GriddingMachine.jl/src/Indexer.jl:340.

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GriddingMachine.Indexer.read_LUT — Method

read_LUT(fn::String, FT::DataType = Float32)

Read the entire look-up-table from collection, given

fn Path to the target file
FT Float number type, default is Float32

Examples

read_LUT(query_collection(vcmax_collection()));
read_LUT(query_collection(vcmax_collection()), Float64);

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GriddingMachine.Indexer.read_LUT — Method

read_LUT(fn::String, cyc::Int, FT::DataType = Float32)

Read the entire look-up-table from collection, given

fn Path to the target file
cyc Cycle number, such as 8 for data in Augest in 1 1M resolution dataset
FT Float number type, default is Float32

Examples

read_LUT(query_collection(gpp_collection()), 8);
read_LUT(query_collection(gpp_collection()), 8, Float64);

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GriddingMachine.Indexer.read_LUT — Method

read_LUT(fn::String, lat::Number, lon::Number, res::Number, FT::DataType = Float32; interpolation::Bool = false)

Read the selected part of a look-up-table from collection, given

fn Path to the target file
lat Latitude in °
lon Longitude in °
res Spatial resolution in °
FT Float number type, default is Float32
interpolation If true, interpolate the dataset

Examples

read_LUT(query_collection(vcmax_collection()), 30, 116, 0.5);
read_LUT(query_collection(vcmax_collection()), 30, 116, 0.5, Float64);
read_LUT(query_collection(vcmax_collection()), 30, 116, 0.5, Float64; interpolation=true);

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GriddingMachine.Indexer.read_LUT — Method

read_LUT(fn::String, lat::Number, lon::Number, FT::DataType = Float32; interpolation::Bool = false)

Read the selected part of a look-up-table from collection, given

fn Path to the target file
lat Latitude in °
lon Longitude in °
FT Float number type, default is Float32
interpolation If true, interpolate the dataset

Examples

read_LUT(query_collection(vcmax_collection()), 30, 116);
read_LUT(query_collection(vcmax_collection()), 30, 116, Float64);
read_LUT(query_collection(vcmax_collection()), 30, 116, Float64; interpolation=true);

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GriddingMachine.Indexer.read_LUT — Method

read_LUT(fn::String, lat::Number, lon::Number, cyc::Int, res::Number, FT::DataType = Float32; interpolation::Bool = false)

Read the selected part of a look-up-table from collection, given

fn Path to the target file
lat Latitude in °
lon Longitude in °
cyc Cycle number, such as 8 for data in Augest in 1 1M resolution dataset
res Spatial resolution in °
FT Float number type, default is Float32
interpolation If true, interpolate the dataset

Examples

read_LUT(query_collection(gpp_collection()), 30, 116, 8, 0.5);
read_LUT(query_collection(gpp_collection()), 30, 116, 8, 0.5, Float64);
read_LUT(query_collection(gpp_collection()), 30, 116, 8, 0.5, Float64; interpolation=true);

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GriddingMachine.Indexer.read_LUT — Method

read_LUT(fn::String, lat::Number, lon::Number, res::Number, FT::DataType = Float32; interpolation::Bool = false)

Read the selected part of a look-up-table from collection, given

fn Path to the target file
lat Latitude in °
lon Longitude in °
res Spatial resolution in °
FT Float number type, default is Float32
interpolation If true, interpolate the dataset

Examples

read_LUT(query_collection(vcmax_collection()), 30, 116, 0.5);
read_LUT(query_collection(vcmax_collection()), 30, 116, 0.5, Float64);
read_LUT(query_collection(vcmax_collection()), 30, 116, 0.5, Float64; interpolation=true);

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read_LUT(fn::String, lat::Number, lon::Number, cyc::Int, FT::DataType = Float32; interpolation::Bool = false)

Read the selected part of a look-up-table from collection, given

fn Path to the target file
lat Latitude in °
lon Longitude in °
cyc Cycle number, such as 8 for data in Augest in 1 1M resolution dataset
FT Float number type, default is Float32
interpolation If true, interpolate the dataset

Examples

read_LUT(query_collection(gpp_collection()), 30, 116, 8);
read_LUT(query_collection(gpp_collection()), 30, 116, 8, Float64);
read_LUT(query_collection(gpp_collection()), 30, 116, 8, Float64; interpolation=true);

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Requestor

GriddingMachine.Requestor.request_LUT — Function

This function requests data from our server for supported datasets (only latest datasets supported). Supported methods are

request_LUT(artname, lat, lon)
request_LUT(
    artname,
    lat,
    lon,
    cyc;
    user,
    interpolation,
    server,
    port
)

defined at /home/runner/work/GriddingMachine.jl/GriddingMachine.jl/src/Requestor.jl:41.

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GriddingMachine.Requestor.request_LUT — Method

request_LUT(artname::String, lat::Number, lon::Number, cyc::Int = 0; user::String="Anonymous", interpolation::Bool = false, server::String = "https://tropo.gps.caltech.edu", port::Int = 44301)

Request data from the server, given

artname Artifact full name such as LAI_MODIS_2X_8D_2017_V1
lat Latitude
lon Longitude
cyc Cycle index, default is 0 (read entire time series)
user User name (non-registered users need to wait for 5 seconds before the server processes the request)
interpolation If true, interpolate the data linearly
server Server address such as https://tropo.gps.caltech.edu
port Port number for the GriddingMachine server

Examples

request_LUT("LAI_MODIS_2X_8D_2017_V1", 30.5, 115.5);
request_LUT("LAI_MODIS_2X_8D_2017_V1", 30.5, 115.5; interpolation=true);
request_LUT("LAI_MODIS_2X_8D_2017_V1", 30.5, 115.5, 8);
request_LUT("LAI_MODIS_2X_8D_2017_V1", 30.5, 115.5, 8; interpolation=true);

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