The article "Global Net Primary Production: Combining Ecology and Remote Sensing" by Christopher B. Field, James T. Randerson, and Carolyn M. Malmström explores the factors influencing terrestrial net primary production (NPP) and proposes a model that integrates ecological principles with satellite data to estimate NPP at a global scale. The authors discuss the sensitivity of NPP to various controls, including climate, topography, soils, plant and microbial characteristics, disturbance, and anthropogenic impacts. They highlight the challenges in modeling NPP due to the complexity of these controls and the limited availability of data, especially for historical and future conditions. The CASA (Carnegie, Stanford, Ames Approach) model, introduced in this article, calculates NPP as the product of absorbed photosynthetically active radiation (APAR) and an efficiency of radiation use, ε. The model uses satellite data to estimate APAR and surface data to determine ε, providing monthly estimates of NPP. The authors detail the components of the CASA model, including the calculation of APAR from AVHRR NDVI and the determination of ε based on a globally uniform maximum value and scalars representing water availability and temperature suitability. The article also discusses the calibration process of the CASA model, which involves fitting the model to observed NPP data from a suite of surface sites. The authors compare the CASA estimates of light use efficiencies with those from other models and literature values, showing that CASA's estimates are consistent with observed patterns in different vegetation types. Overall, the CASA model provides a robust framework for estimating global NPP, integrating ecological principles with remote sensing data to capture the complex interactions among various controls on NPP.The article "Global Net Primary Production: Combining Ecology and Remote Sensing" by Christopher B. Field, James T. Randerson, and Carolyn M. Malmström explores the factors influencing terrestrial net primary production (NPP) and proposes a model that integrates ecological principles with satellite data to estimate NPP at a global scale. The authors discuss the sensitivity of NPP to various controls, including climate, topography, soils, plant and microbial characteristics, disturbance, and anthropogenic impacts. They highlight the challenges in modeling NPP due to the complexity of these controls and the limited availability of data, especially for historical and future conditions. The CASA (Carnegie, Stanford, Ames Approach) model, introduced in this article, calculates NPP as the product of absorbed photosynthetically active radiation (APAR) and an efficiency of radiation use, ε. The model uses satellite data to estimate APAR and surface data to determine ε, providing monthly estimates of NPP. The authors detail the components of the CASA model, including the calculation of APAR from AVHRR NDVI and the determination of ε based on a globally uniform maximum value and scalars representing water availability and temperature suitability. The article also discusses the calibration process of the CASA model, which involves fitting the model to observed NPP data from a suite of surface sites. The authors compare the CASA estimates of light use efficiencies with those from other models and literature values, showing that CASA's estimates are consistent with observed patterns in different vegetation types. Overall, the CASA model provides a robust framework for estimating global NPP, integrating ecological principles with remote sensing data to capture the complex interactions among various controls on NPP.