Estimation and analysis of gross primary production of soybean under various management practices and drought conditions

Wagle, Pradeep; Xiao, Xiangming; Suyker, Andrew E.

doi:10.1016/j.isprsjprs.2014.10.009

Cited by 71 publications

(63 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The VI model uses EVI as a proxy for both FPAR chl and LUE. Our previous studies show that EVI is less sensitive to drought than LSWI and water-related variables (Wagle et al, , 2015. Thus, compared to LSWI-based LUE in the VPM, EVI-based LUE in the VI model is less sensitive to droughts.…”

Section: Tablementioning

confidence: 87%

“…Based on previous publications, we used the ε 0 value of 0.39 g C mol −1 PPFD at the US-Bo1 site (soybean), 0.42 g C mol −1 PPFD at the US-ARb site (tallgrass prairie), and 0.69 g C mol −1 PPFD at the US-Ne1 site (corn) (Kalfas et al, 2011;Wagle, Xiao, & Suyker, 2015;. T scalar is estimated at each time step as in the Terrestrial Ecosystem Model (Raich et al, 1991),…”

Section: The Vegetation Photosynthesis Model (Vpm)mentioning

confidence: 99%

“…In this study, we set T min , T opt , and T max values to −1°C, 30°C, and 50°C, respectively, at the US-Bo1 (soybean, Wagle et al, 2015) and US-ARb (tallgrass prairie, sites, and to 10°C, 28°C, and 48°C at the US-Ne1 (corn, Kalfas et al, 2011) site. W scalar is estimated at each time step based on the MODIS-based LSWI.…”

Section: The Vegetation Photosynthesis Model (Vpm)mentioning

confidence: 99%

“…W scalar is estimated at each time step based on the MODIS-based LSWI. A two-step W scalar function was implemented to address the effect of water stress (Wagle et al, , 2015.…”

Section: The Vegetation Photosynthesis Model (Vpm)mentioning

confidence: 99%

“…It uses parameter values from previous publications, such as maximum LUE parameters (Kalfas et al, 2011;Wagle et al, , 2015. It also uses parameter values from a standardized parameterization procedure, for example, T scalar and W scalar in the model.…”

Section: Calibration Of Tg VI and Gr Modelsmentioning

confidence: 99%

See 4 more Smart Citations

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

Dong

Xiao

Wagle

et al. 2015

Remote Sensing of Environment

Self Cite

107

View full text Add to dashboard Cite

Berrien III, "Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought" (2015). Accurate estimation of gross primary production (GPP) is critical for understanding ecosystem response to climate variability and change. Satellite-based diagnostic models, which use satellite images and/or climate data as input, are widely used to estimate GPP. Many models used the Normalized Difference Vegetation Index (NDVI) to estimate the fraction of absorbed photosynthetically active radiation (PAR) by vegetation canopy (FPAR canopy ) and GPP. Recently, the Enhanced Vegetation Index (EVI) has been increasingly used to estimate the fraction of PAR absorbed by chlorophyll (FPAR chl ) or green leaves (FPAR green ) and to provide more accurate estimates of GPP in such models as the Vegetation Photosynthesis Model (VPM), Temperature and Greenness (TG) model, Greenness and Radiation (GR) model, and Vegetation Index (VI) model. Although these EVI-based models perform well under non-drought conditions, their performances under severe droughts are unclear. In this study, we run the four EVI-based models at three AmeriFlux sites (rainfed soybean, irrigated maize, and grassland) during drought and non-drought years to examine their sensitivities to drought. As all the four models use EVI for FPAR estimate, our hypothesis is that their different sensitivities to drought are mainly attributed to the ways they handle light use efficiency (LUE), especially water stress. The predicted GPP from these four models had a good agreement with the GPP estimated from eddy flux tower in non-drought years with root mean squared errors (RMSEs) in the order of 2.17 (VPM), 2.47 (VI), 2.85 (GR) and 3.10 g C m −2 day −1 (TG). But their performances differed in drought years, the VPM model performed best, followed by the VI, GR and TG, with the RMSEs of 1.61, 2.32, 3.16 and 3.90 g C m −2 day −1 respectively. TG and GR models overestimated seasonal sum of GPP by 20% to 61% in rainfed sites in drought years and also overestimated or underestimated GPP in the irrigated site. This difference in model performance under severe drought is attributed to the fact that the VPM uses satellite-based Land Surface Water Index (LSWI) to address the effect of water stress (deficit) on LUE and GPP, while the other three models do not have such a mechanism. This study suggests that it is essential for these models to consider the effect of water stress on GPP, in addition to using EVI to estimate FPAR, if these models are applied to estimate GPP under drought conditions.

show abstract

Section: Tablementioning

confidence: 87%

Section: The Vegetation Photosynthesis Model (Vpm)mentioning

confidence: 99%

Section: The Vegetation Photosynthesis Model (Vpm)mentioning

confidence: 99%

“…W scalar is estimated at each time step based on the MODIS-based LSWI. A two-step W scalar function was implemented to address the effect of water stress (Wagle et al, , 2015.…”

Section: The Vegetation Photosynthesis Model (Vpm)mentioning

confidence: 99%

Section: Calibration Of Tg VI and Gr Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

Dong

Xiao

Wagle

et al. 2015

Remote Sensing of Environment

Self Cite

107

View full text Add to dashboard Cite

show abstract

CO₂ flux in a wheat‐soybean succession in subtropical Brazil: A carbon sink

et al. 2022

View full text Add to dashboard Cite

The subtropical region of Brazil is home to 33% of the soybean [Glycine max (L.)Merr.] growing area and 90% of the wheat (Tritucum aestivum L.) growing area of this country. A soybean-wheat succession with fallow between crops is used in about 11% of the cultivated area. No study has quantified CO 2 fluxes in annual soybeanwheat succession in this region. Hence, this study analyzed the seasonality of CO 2 exchange (net ecosystem exchange [NEE]) in a 2015/2016 wheat-soybean succession in a commercial farm located in Carazinho, Rio Grande do Sul State, Brazil. The eddy covariance method was used to estimate the annual C balance of this system.The NEE was partitioned between gross primary productivity and ecosystem respiration to understand the dynamics of these fluxes during a year of wheat-soybean succession. Considering the net ecosystem balance between photosynthesis and respiration during the growing season, both soybean and wheat absorbed CO 2 from the atmosphere (NEE wheat: -347 ± 4 g C m -2 ; NEE soybean: -242 ± 3 g C m -2 ). The fallow periods between growing seasons, however, acted as a source of 156 ± 2 g C m -2 , reducing the C absorbed by the crops by 27%. For 1 yr, the net biome productivity was -50 g C m -2 yr -1 . The results obtained here demonstrate that the wheat-soybean succession was a net C sink under these specific climatic conditions and field management practices and that the long fallow period between crops limited the agroecosystem from becoming a more efficient CO 2 sink.Abbreviations: EC, eddy covariance; FSW, fallow period between soybean and wheat; FWS, fallow period between wheat and soybean; GPP, gross primary production; LAI, leaf area index; LAs, specific leaf area; LDMc, leaf dry matter of the canopy; NBP, net biome production; NEE, net ecosystem exchange; RDMc, the rest (no leaves) dry mass of canopy; RH, relative humidity; TDMc, total dry matter of the canopy; VPD, vapor pressure deficit.

show abstract

Dynamics of CO2 fluxes and controlling environmental factors in sugarcane (C4)–wheat (C3) ecosystem of dry sub-humid region in India

et al. 2021

View full text Add to dashboard Cite

Estimation and analysis of gross primary production of soybean under various management practices and drought conditions

Cited by 71 publications

References 47 publications

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

CO₂ flux in a wheat‐soybean succession in subtropical Brazil: A carbon sink

Dynamics of CO2 fluxes and controlling environmental factors in sugarcane (C4)–wheat (C3) ecosystem of dry sub-humid region in India

Contact Info

Product

Resources

About

Estimation and analysis of gross primary production of soybean under various management practices and drought conditions

Cited by 71 publications

References 47 publications

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

CO2 flux in a wheat‐soybean succession in subtropical Brazil: A carbon sink

Dynamics of CO2 fluxes and controlling environmental factors in sugarcane (C4)–wheat (C3) ecosystem of dry sub-humid region in India

Contact Info

Product

Resources

About

CO₂ flux in a wheat‐soybean succession in subtropical Brazil: A carbon sink