Regression

SUSI: Supervised Self-Organizing Maps for Regression and Classification in Python

In many research fields, the sizes of the existing datasets vary widely. Hence, there is a need for machine learning techniques which are well-suited for these different datasets. One possible technique is the self-organizing map (SOM), a type of …

Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data

In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with IR data. Measurements from a multi-sensor field campaign represent the benchmark dataset which contains measured …

Fusion of hyperspectral and ground penetrating radar data to estimate soil moisture

In this contribution, we investigate the potential of hyperspectral data combined with either simulated ground penetrating radar (GPR) or simulated (sensor-like) soil-moisture data to estimate soil moisture. We propose two simulation approaches to …

Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae, and Turbidity

Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters …

Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data

In this paper, we introduce a framework to solve regression problems based on high-dimensional and small datasets. This framework involves two self-organizing maps (SOM) and combines unsupervised with supervised learning. We investigate the impacts …

Modeling Subsurface Soil Moisture Based on Hyperspectral Data: First Results of a Multilateral Field Campaign

Soil moisture dynamics and its spatial distribution represent important data of a landscape. Until today, a precise and spatially continuous measurement of these is difficult. Current remote sensing approaches focus on an upscaling of measured …