Remote sensing of terrestrial gross primary productivity: a review of advances in theoretical foundation, key parameters and methods

Figures & data

Figure 1. The theoretical foundation of remotely sensed GPP estimation. Photosynthesis process at leaf scale was referenced from Porcar-Castell et al. (2014) and Patel et al. (2018), and remote sensing monitoring was referenced from Thenkabail et al. (2011). LAI: leaf area index; CI: clumping index; FVC: fractional vegetation cover; Ta: air temperature; VPD: vapor pressure deficit; LST: land surface temperature; SM: soil moisture; PAR: photosynthetically active radiation; APAR: absorbed photosynthetically active radiation; LUE: light use efficiency; SIF: solar-induced chlorophyll fluorescence; [N]: nitrogen content; [cab]: chlorophyll concentration; NADPH: nicotinamide adenine dinucleotide phosphate; ATP: Adenosine triphosphate; H⁺: electrons generated by water splitting; J_max: maximum electron transport rate (μmol m⁻² s⁻¹); V_cmax: maximum carboxylation rate (μmol m⁻² s⁻¹); GPP: gross primary production. See more symbols and acronyms in Appendix A.

Figure 2. Five key parameters closely linked to the photosynthetic process. The size and color of the arrows depict the degree of correlation between these parameters and remotely sensed GPP estimation. See more symbols and acronyms in Appendix A.

Figure 3. The development of remotely sensed GPP estimation. f() is linear or nonlinear equation, g() and k() are conceptual functions that represent different groups of models. The arrows (e.g. ANN→SVM→RF) only show the chronological order of application, without further meaning in model performance.

Figure 4. The development of each core parameter in the LUE model.

Table 1. The remote sensing data used in machine learning approaches for GPP estimation.

Methods	RS data	References
ANN	NOAA-AVHRR NDVI, landcover	Papale and Valentini (2003)
SVM	MODIS LST, EVI/FPAR, SWR and landcover	Yang et al. (2007)
SVR	MODIS NDVI, EVI, GR, LAI, and daytime LST	Ueyama et al. (2013)
PRM	SPOT VEGETATION NDVI and NDVI-based phenometrics	Wylie et al. (2007) and Zhang et al. (2007)
	MOD09A1, MODIS LST, EVI and LAI/FPAR	Xiao et al. (2008)
MTE	AVHRR, SeaWiFS and MERIS FPAR and FPAR-based indictors	Jung et al. (2011)
	FPAR (Z. Zhu et al. 2013) based indictors	Liang et al. (2017)
RF	SeaWiFS-FPAR, MODIS EVI and landcover	Wei et al. (2017)
	MODIS NDVI, EVI, NDWI (B.-C. Gao 1996), FPAR and LST	Bodesheim et al. (2018)
	GOSIF, NIRV (MCD43C4) and landcover	Bai et al. (2021)

Non-remotely sensed parameters are not listed. ANN: artificial neural networks; SVM: support vector machines; SVR: support vector regression; PRM: piecewise regression models; MTE: model tree ensembles; RF: random forest

Figure 5. Schematic of the comprehensive observation system.

Table

Index	Equation	References
EVI	$2.5\times \frac{{\mathrm{R}}_{\mathrm{NIR}}-{\mathrm{R}}_{\mathrm{Red}}}{{\mathrm{R}}_{\mathrm{NIR}}+6\times {\mathrm{R}}_{\mathrm{Red}}-7.5\times {\mathrm{R}}_{\mathrm{Blue}}+1}$	(Huete et al. 2002)
MTCI	$\frac{{\mathrm{R}}_{753.75}-{\mathrm{R}}_{708.75}}{{\mathrm{R}}_{708.75}+{\mathrm{R}}_{681.25}}$ where R753.75, R708.75, R681.25 are reflectance in the center wavelengths of band 8, 9 and 10 in the MERIS standard band setting	(Dash and Curran 2007)
NDVI	$\frac{{\mathrm{R}}_{\mathrm{NIR}}-{\mathrm{R}}_{\mathrm{Red}}}{{\mathrm{R}}_{\mathrm{NIR}}+{\mathrm{R}}_{\mathrm{Red}}}$	(Goward, Tucker, and Dye 1985)
NIRV	NDVI × RNIR	(Badgley, Field Christopher, and Berry Joseph 2017)
NIRVP	NIRV × PAR	(Dechant et al. 2022)
PRI	$\frac{{\mathrm{R}}_{531}-{\mathrm{R}}_{570}}{{\mathrm{R}}_{531}+{\mathrm{R}}_{570}}$	(Garbulsky et al. 2011)

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Data availability statement

Data will be made available on request.