Validating the Carnegie-Ames-Stanford Approach for remote sensing of perennial grass net primary production

作者：Shaohui Zhang, Poul Erik Lærke, Mathias Neumann Andersen, Junxiang Peng, Esben Øster Mortensen, Johannes Wilhelmus Maria Pullens, Sheng Wang, Klaus Steenberg Larsen, Davide Cammarano, Uffe Jørgensen, Kiril Manevski · 发表于：Remote Sensing of Environment · 年份：2025 · DOI：10.1016/j.rse.2025.114857 · 被引用次数：6 · 研究领域：Remote Sensing in Agriculture、Plant Water Relations and Carbon Dynamics、Remote Sensing and LiDAR Applications

Under optimal growth conditions, net primary productivity ( NPP ) is a product of intercepted photosynthetic active radiation ( Ipar ) and maximum radiation use efficiency ( RUE max ; conversion of Ipar to biomass). The objective of this study was to improve and validate the RUE max -based Carnegie-Ames-Stanford Approach ( CASA ) for the determination of grassland NPP by canopy multispectral reflectance collected at field (handheld sensor) and airborne ( UAV ) scale considering environmental constraints. The analysis was based on multi-year field experiments on sandy loam soil in Denmark, measured shoot and estimated root biomass to calculate NPP , long-term meteorological data, and daily NPP estimated from CO 2 flux chamber measurements for deriving environmental constraints. The results derived from CO 2 flux data showed that NPP and plant respiration were higher in the middle of the season before the second harvest when temperature was also high. The daily maximum air temperature optimal for grass biomass production was 16.5 °C. The improved CASA model built in this study was accurate for modeling NPP at both daily ( nRMSE decrease of 9 %) and seasonal ( nRMSE decrease of 8–34 %) scales when considering the best environmental constraints such as maximum air temperature, vapor pressure deficit, cloudiness, and water stress, compared to no constraints. Maximum air temperature and water stress were the most important environmental constraints to the grass RUE max . Seasonal R...