Surface Soil Moisture Retrieval Using Optical/Thermal Infrared Remote Sensing Data

Abstract

Classifying the land surface according to different climate zones is often a prerequisite for global diagnostic or predictive modelling studies. Classical classifications such as the prominent K̈oppen–Geiger (KG) approach rely on heuristic decision rules. Although these heuristics may transport some process understanding, such a discretization may appear “arbitrary” from a data oriented perspective. In this contribution we compare the precision of a KG classification to an unsupervised classification (k-means clustering). Generally speaking, we revisit the problem of “climate classification” by investigating the inherent patterns in multiple data streams in a purely data driven way. One question is whether we can reproduce the KG boundaries by exploring different combinations of climate and remotely sensed vegetation variables. In this context we also investigate whether climate and vegetation variables build similar clusters. In terms of statistical performances, k-means clearly outperforms classical climate classifications. However, a subsequent stability analysis only reveals a meaningful number of clusters if both climate and vegetation data are considered in the analysis. This is a setback for the hope to explain vegetation by means of climate alone. Clearly, classification schemes like K̈oppen-Geiger will play an important role in the future. However, future developments in this area need to be assessed based on data driven approaches.

Publication
IEEE Transactions on Geoscience and Remote Sensing
Jian Peng
Jian Peng
Professor for Hydrology and Remote Sensing / Head of Remote Sensing Department (UFZ)

I am the head of the Department of Remote Sensing at the UFZ and a full professor for Hydrology and Remote Sensing at the University of Leipzig.