Abstract
In field measurement programmes, stratified sampling can optimize sampling efficiency, but stratification is often undertaken subjectively, and is frequently based on a priori classification schemes such as those used for vegetation maps. In order to avoid the problems associated with a priori subjective schemes, we explore here an objective procedure, Regression Tree Analysis (RTA). RTA has previously been used in local‐scale studies, but here we apply it to a very large study domain, namely the entire humid tropical zone of South America. The aim of the study was to develop an optimal sampling design in preparation for the Large Scale Biosphere‐Atmosphere Experiment in Amazonia (LBA). Co‐registered spatially continuous fields of rainfall, temperature, photosynthetically active radiation (PAR), the normalized difference index (NDVI), an index of surface moisture, and other independent variables were used to predict three dependent variables, annual net radiation (Rn), latent heat (LE) and net primary production (NPP). Rather than simply dividing the study area based on differing levels of the three dependent variables, empirical models were developed using RTA to indicate how the relationships between these and possible forcing variables vary across the study area. For each variable long‐term seasonal indices such as annual average, monthly minimum and amplitude were used to exclude effects of temporal phase differences between the hemispheres. The resulting hierarchical models revealed variations in the interdependence of the forcing variables throughout the study area and therefore provided a basis for a stratified sampling and identifying the most important variables to be collected in LBA for the Amazon basin as a whole as well as optimizing the sampling scheme for scaling up findings from the field scale to larger areas.