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Simple and Innovative Methods to Estimate Gross Primary Production and Transpiration of Crops: A Review

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Digital Ecosystem for Innovation in Agriculture

Part of the book series: Studies in Big Data ((SBD,volume 121))

Abstract

Sustainable agricultural production to support food security is one of the main targets of the 2030 agenda for global sustainable development goals (SDGs). New technology advancements and sources of information play a critical role in supporting agriculture to achieve the SDGs goals and increase production capabilities to meet rising food demands. Gross primary production (GPP) and transpiration (T) of crops are the largest carbon and water fluxes in agroecosystems. GPP data products are used to estimate crop yield, an important metric for agricultural resiliency and food security. Over land surfaces, T is the largest component of evapotranspiration and represents more than 60% of the precipitated water returned to the atmosphere. T is used to represent water use and improve irrigation in croplands, thereby helping to reduce production costs and support sustainable crop production. In this chapter, we review the state-of-the-science approaches for estimating GPP and T, including in situ and remote sensing methods, while focusing on the biophysical foundation behind the major available techniques. Furthermore, given the linkage between GPP and T through the behavior of stomates, it is possible to simplify the calculation of both variables by estimating common biophysical parameters (i.e., minimum stomatal conductance) that influence water loss and vegetation carbon uptake. We highlight innovative approaches that enhance the calculation of both variables based on this relationship and review the extent and limitations of the algorithms used in some of the most popular satellite products like Moderate Resolution Imaging Spectroradiometer (MODIS) global evapotranspiration product (MOD16) and EcoStress. Furthermore, this chapter explores and describes the potential for using solar-induced fluorescence (SIF) data to calculate agricultural GPP and T. Finally, given the importance of certain variables in agricultural and forest meteorology, this chapter also includes a section highlighting the main conversion factors useful in calculating energy, carbon, and water fluxes.

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Notes

  1. 1.

    https://github.com/AmeriFlux/ONEFlux/.

Abbreviations

APAR:

Absorbed photosynthetically active radiation

APARchl:

Absorbed photosynthetically active radiation by the chlorophyll

CO2:

Carbon dioxide

CP:

Change point detection

c p :

Specific heat

E:

Evaporation

Ebs:

Bare soil evaporation

EC:

Eddy covariance

ECOSTRESS:

The ecosystem spaceborne Thermal radiometer experiment on space station

ECT:

Eddy covariance tower

ER:

Ecosystem respiration

ET:

Evapotranspiration

EVI:

Enhanced vegetation index

Ewc:

Evaporation from wet canopy

fPAR:

Fraction of photosynthetically active radiation

FVS:

Flux variance similarity

Fwet:

Water cover fraction

G:

Ground soil heat flux

GMAO:

Global modeling and assimilation office

GOSAT:

Greenhouse gases observing satellite

GPP:

Gross primary production

H:

Sensible heat flux

HSR:

High spatial resolution

IR:

Infrared

LAI:

Leaf area index

LE:

Latente heat flux

LST:

Land surface temperature

LSWI:

Land surface water index

LUE(εg):

Light use efficiency

LUE0(ε0):

Maximum light use efficiency

MIP:

Model intercomparison project

MODIS:

Moderate resolution imaging spectroradiometer

MP:

Moving-point-transition

MSR:

Moderate spatial resolution

NBP:

Net ecosystem carbon balance

NDVI:

Normalized differentiate vegetation index

NEE:

Net ecosystem exchange

NPP:

Net primary production

NPV:

Non-photosynthetically active vegetation

OCO-2:

Orbiting carbon observatory-2

P-M:

Penman–Monteith

PAR:

Photosynthetically active radiation

PAV:

Photosynthetically active vegetation (chloroplast)

PPFD:

Photosynthetic photon flux density

QC:

Quality controls

r a :

Aerodynamic resistance

r c :

Canopy stomatal resistance

REF:

Reference

RH:

Relative humidity

Rn:

Net radiation

SE:

Standard error

SEB:

Surface energy balance

SIF:

Solar-induced chlorophyll fluorescence

SMAP:

Soil moisture active passive (SMAP)

SVAT:

Surface-vegetation-atmosphere transfer

SW:

Total incoming shortwave solar radiation

SWIR:

Shortwave infrared

T:

Transpiration

TEM:

Terrestrial ecosystem model

TIR:

Thermal infrared

Topt:

Optimal air temperature for photosynthesis

u*:

Friction velocity

VIs:

Vegetation indices

VPD:

Vapor pressure deficit

VPM:

Vegetation photosynthesis model

VTM:

Vegetation transpiration model

WUE:

Water use efficiency

γ :

Psychrometric constant

Δ :

Vapor pressure–air temperature curve

ρ :

Air density

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Acknowledgements

This literature review work was supported by research grants from NASA Geostationary Carbon Cycle Observatory mission (GeoCarb Contract #80LARC17C0001), NSF EPSCoR (IIA-1946093), and USDA NIFA (2020-67104-30935).

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Authors and Affiliations

  1. Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA

    Jorge Celis, Xiangming Xiao & Heather McCarthy

  2. Department of Meteorology, University of Oklahoma, Norman, OK, 73019, USA

    Jeffrey Basara

  3. USDA-ARS, Oklahoma and Central Plains Agricultural Research Center, El Reno, OK, 73036, USA

    Pradeep Wagle

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Correspondence to Xiangming Xiao .

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  1. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India

    Sanjay Chaudhary

  2. CIFOR-ICRAF India, Asia Continental Program, New Delhi, Delhi, India

    Chandrashekhar M. Biradar

  3. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India

    Srikrishnan Divakaran

  4. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India

    Mehul S. Raval

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Celis, J., Xiao, X., Basara, J., Wagle, P., McCarthy, H. (2023). Simple and Innovative Methods to Estimate Gross Primary Production and Transpiration of Crops: A Review. In: Chaudhary, S., Biradar, C.M., Divakaran, S., Raval, M.S. (eds) Digital Ecosystem for Innovation in Agriculture. Studies in Big Data, vol 121. Springer, Singapore. https://doi.org/10.1007/978-981-99-0577-5_7

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