SCASA: A Spark-Based Parallel Approach for Net Primary Productivity Calculation with CASA Model

Abstract

Net primary productivity (NPP) research is an essential aspect of carbon cycle research. Remote sensing combined with CASA (Carnegie–Ames–Stanford Approach) model is a mainstream method to calculate the NPP, which is generally based on the ENVI or ArcGIS in the standalone mode. However, with the enhancement of spatial and temporal resolution of remote sensing images, the volume of data has also grown rapidly, which makes the centralized methods not well adapted to the NPP calculation. In this paper, we propose SCASA, a Spark-based parallel approach for NPP calculation with CASA model. Based on the distributed storage characteristics of HDFS, we propose a storage strategy specifically for CASA model. Based on the characteristics of CASA model and Spark’s parallel computing rules, we redesign the computation process to maximize parallelism to further improve the calculation speed. Extensive comparative experiments based on ArcGIS and Spark demonstrate the proposed approach outperform the existing CASA model.