TY - JOUR
T1 - Validating the Carnegie-Ames-Stanford Approach for remote sensing of perennial grass net primary production
AU - Zhang, Shaohui
AU - Lærke, Poul Erik
AU - Andersen, Mathias Neumann
AU - Peng, Junxiang
AU - Mortensen, Esben Øster
AU - Pullens, Johannes Wilhelmus Maria
AU - Wang, Sheng
AU - Larsen, Klaus Steenberg
AU - Cammarano, Davide
AU - Jørgensen, Uffe
AU - Manevski, Kiril
PY - 2025/10/1
Y1 - 2025/10/1
N2 - Under optimal growth conditions, net primary productivity (NPP) is a product of intercepted photosynthetic active radiation (Ipar) and maximum radiation use efficiency (RUEmax; conversion of Ipar to biomass). The objective of this study was to improve and validate the RUEmax-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 CO2 flux chamber measurements for deriving environmental constraints. The results derived from CO2 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 RUEmax. Seasonal RUEmax for modeling NPP after considering best environmental constraints was 1.9–2.7 g C MJ−1 for ryegrass and 1.9–2.2 g C MJ−1 for grass-legume mixture using the two remote sensors for measuring spectral reflectance. Over the whole growing season, tall fescue (3.1 g C MJ−1) and festulolium (2.9 g C MJ−1) obtained higher RUEmax than perennial ryegrass (2.3 g C MJ−1). This study highlights the practical implications of using the CASA model improved by maximum temperature and water stress functions for real-time, remote sensing-based assessments of grassland productivity.
AB - Under optimal growth conditions, net primary productivity (NPP) is a product of intercepted photosynthetic active radiation (Ipar) and maximum radiation use efficiency (RUEmax; conversion of Ipar to biomass). The objective of this study was to improve and validate the RUEmax-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 CO2 flux chamber measurements for deriving environmental constraints. The results derived from CO2 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 RUEmax. Seasonal RUEmax for modeling NPP after considering best environmental constraints was 1.9–2.7 g C MJ−1 for ryegrass and 1.9–2.2 g C MJ−1 for grass-legume mixture using the two remote sensors for measuring spectral reflectance. Over the whole growing season, tall fescue (3.1 g C MJ−1) and festulolium (2.9 g C MJ−1) obtained higher RUEmax than perennial ryegrass (2.3 g C MJ−1). This study highlights the practical implications of using the CASA model improved by maximum temperature and water stress functions for real-time, remote sensing-based assessments of grassland productivity.
KW - CASA
KW - CO flux
KW - Environmental constraints
KW - Radiation use efficiency
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=105007291958&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2025.114857
DO - 10.1016/j.rse.2025.114857
M3 - Journal article
AN - SCOPUS:105007291958
SN - 0034-4257
VL - 328
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 114857
ER -