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TVDI-based water stress coefficient to estimate net primary productivity in soybean areas

Author

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  • Rodigheri, Grazieli
  • Fontana, Denise Cybis
  • da Luz, Luana Becker
  • Dalmago, Genei Antonio
  • Schirmbeck, Lucimara Wolfarth
  • Schirmbeck, Juliano
  • de Gouvêa, Jorge Alberto
  • da Cunha, Gilberto Rocca

Abstract

Net Primary Productivity (NPP) is a major parameter to assess carbon (C) increments by crops. Many models based on Light Use Efficiency (LUE) have been developed to estimate NPP in different regions. LUE in different ecosystems is reduced by environmental stressors, such as those caused by water restrictions. However, few studies have used remote sensing data to estimate water stress coefficients. Thus, our goal was to evaluate the performance of the Carnegie-Ames-Stanford-Approach (CASA) model using Temperature-Vegetation Dryness Index (TVDI) as a water stress coefficient to quantify NPP dynamics in agricultural ecosystems in northwestern Rio Grande do Sul state, in Brazil. Weather data from the ERA5 and surface weather stations were used to estimate NPP. Surface temperature (LST), Normalized Difference Vegetation Index (NDVI), estimated (EET) and potential (PET) evapotranspiration data were retrieved from Landsat/OLI and Terra/MODIS and used as input in the model. The CASA model was evaluated using ground-based data and then applied to the agricultural region of study. NPP data obtained using the CASA model and remote sensing data were in accordance with the observed data (RMSE less than 30 gC m−2 month−1 and r higher than 0,97), highlighting the model's efficiency in representing the temporal variations of NPP in the experimental area. The high correlation between simulated and observed data indicates that the TVDI is suitable for use as a water stress index. This work may serve as a baseline for future studies, which could explore the use of other indices and enhance NPP estimates.

Suggested Citation

  • Rodigheri, Grazieli & Fontana, Denise Cybis & da Luz, Luana Becker & Dalmago, Genei Antonio & Schirmbeck, Lucimara Wolfarth & Schirmbeck, Juliano & de Gouvêa, Jorge Alberto & da Cunha, Gilberto Rocca, 2024. "TVDI-based water stress coefficient to estimate net primary productivity in soybean areas," Ecological Modelling, Elsevier, vol. 490(C).
    • Handle: RePEc:eee:ecomod:v:490:y:2024:i:c:s0304380024000255
    DOI: 10.1016/j.ecolmodel.2024.110636
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    References listed on IDEAS

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    1. Wan, Wei & Liu, Zhong & Li, Kejiang & Wang, Guiman & Wu, Hanqing & Wang, Qingyun, 2021. "Drought monitoring of the maize planting areas in Northeast and North China Plain," Agricultural Water Management, Elsevier, vol. 245(C).
    2. Christopher Potter & Steven Klooster & Vanessa Genovese, 2012. "Net primary production of terrestrial ecosystems from 2000 to 2009," Climatic Change, Springer, vol. 115(2), pages 365-378, November.
    3. Qiang Zhu & Jianjun Zhao & Zhenhua Zhu & Hongyan Zhang & Zhengxiang Zhang & Xiaoyi Guo & Yunzhi Bi & Li Sun, 2017. "Remotely Sensed Estimation of Net Primary Productivity (NPP) and Its Spatial and Temporal Variations in the Greater Khingan Mountain Region, China," Sustainability, MDPI, vol. 9(7), pages 1-16, July.
    4. Jun Chen & Liguo Cao, 2022. "Spatiotemporal Variability in Water-Use Efficiency in Tianshan Mountains (Xinjiang, China) and the Influencing Factors," Sustainability, MDPI, vol. 14(13), pages 1-14, July.
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