We know that ecosystems vary greatly from local to global scales. Simulation models use equations that are written into a program that acts as a copy of a real system. This is called net primary productivity, and is related to the growth rates of green vegetation. Understanding the exchange of carbon dioxide between the biosphere and the atmosphere is critical to predicting global climate change. Each image is based on the output of a different computer model, each of which used current climate conditions and carbon dioxide concentrations to calculate plant productivity.
Computer models can fill in the blanks left by satellite remote sensing and in-situ measurements.
The second type of model uses observed measurements that scientists have collected across the globe to generalize real processes and properties seen in nature. For example, Levine uses satellite images of land surface overlain on soil data and, together with the GIS software and a neural network, relates soil properties with the spectral signatures from the satellite image. When the model predicts something interesting or unexpected, Levine says, then it is time to go make more measurements. Data-driven models combine many types of observations to derive information that is difficult or impossible to directly measure. In this case, Dr. Levine used topography, vegetation index, and in-situ soil sample data from selected sites to estimate soil carbon content throughout Brazil's Amazon Basin.