Identifying high-quality (ie, relatively clear) measurements of surface conditions is a near-
universal first step in working with optical satellite imagery. Many cloud masking algorithms …

In this study, the emphasis is on assessing how satellite-derived vegetation indices respond
to drought stress characterized by meteorological observations. This study aimed to …

Big optical Earth observation (EO) data analytics usually start from numerical, sub-symbolic
reflectance values that lack inherent semantic information (meaning) and require …

Accurately characterizing clouds and their shadows is a long-standing problem in the Earth
Observation community. Recent works showcase the necessity to improve cloud detection …

Land use and land cover (LULC) are fundamental units of human activities. Therefore, it is of
significance to accurately and in a timely manner obtain the LULC maps where dramatic …

Aiming at the convergence between Earth observation (EO) Big Data and Artificial General
Intelligence (AGI), this two-part paper identifies an innovative, but realistic EO optical …

Lakes play an important role in global hydrologically and provide substantial water
resources in hyperarid regions, and they are becoming even more significant as climate …

Accurate delineation of spring phenology (eg, start of growing season, SOS) of deciduous
forests is essential for understanding its responses to environmental changes. To date, SOS …

Aiming at the convergence between Earth observation (EO) Big Data and Artificial General
Intelligence (AGI), this paper consists of two parts. In the previous Part 1, existing EO optical …

Precision mapping towards tropical forest cover data is critical to address the global climate
crisis, such as land‐based carbon measurement and potential conservation areas …