Granular computing with data granulation as the basic is a frontier direction in the field of big data processing, which simulates human thinking and solves large-scale complex problems. It helps improve the accuracy and efficiency of pattern mining and knowledge discovery by means of structure and association. Therefore, incorporating this data analysis method into the process of mining information and discovering knowledge from remote sensing big data needs to be considered.In order to better implement intelligent processing and interpretation analysis of multi-source and multimodal remote sensing big data, and obtain spatiotemporal information that can serve precise applications, this study draws on the data processing thinking of granular computing, and builts a research path that follows the evolution route from visual understanding of external scene to relationship perspective of internal generation mechanism (spectrum analysis). The paper analyzes the granular structure of remote sensing big data and its multi-level and multi-granularity characteristics from three dimensions of space, time, and attribute. We further determine the corresponding granulation strategy based on the characteristics of remote sensing data. In addition, we build a methodology of remote sensing granular computing based on geo-parcels, which integrates the basic models of zonal-stratified perception, spatiotemporal collaborative inversion, and multi-granularity decision making. These models integrate geographical analysis methods, remote sensing mechanism models, and artificial intelligence algorithms. They also mine geographic information or knowledge including morphology, type, index, state, development trend, and mechanism of land geo-parcels.This study focuses on practical research guided by the application needs of precision agriculture. The case study shows that granular computing meets the requirements of intelligent computing of remote sensing big data from multiple perspectives. It is verified that the theory and method proposed in this study can systematically deconstruct and methodically address the multi-level complex problems of agricultural remote sensing. The case study also demonstrates its potential ability to support precise domain applications.This study develops a methodology of remote sensing intelligent computing under the guidance of granular computing. The corresponding problems and solutions in the aspects of space, time, and attribute are also analyzed. Based on the abovementioned work, we are confident that the proposed methodology of intelligent interpretation of remote sensing based on granular computing can effectively address and resolve complex surface cognitive problems in Earth observation through remote sensing.

remote sensing big data;granular structure/granular computing;geo-parcel;zonal-stratified perception;spatiotemporal collaborative inversion;multi-granularity decision making;precision agriculture application