Complex crop growth models (CGM) require a large number of input parameters, which can cause large errors if they are uncertain. Furthermore, they often lack spatial information. The coupling of a CGM with a radiative transfer model offers the possibility to assimilate remote sensing data while taking into account uncertainties in input parameters. A particle filter was used to assimilate satel...

The coastal shallow water zone can be a challenging and costly environment in which to acquire bathymetry and other oceanographic data using traditional survey methods. Much of the coastal shallow water zone worldwide remains unmapped using recent techniques and is, therefore, poorly understood. Optical satellite imagery is proving to be a useful tool in predicting water depth in coastal zones,...

The cortical transition from the slow-wave pattern of sleep (SWS) to the rapideye-movement (REM) pattern is a dramatic feature of the somnogram. Indeed, the change in the electrocorticogram (ECoG) is so abrupt that the moment of transition usually can be identified with a time-resolution of about one second [8, 37]. Although the neuromodulatory environment and electroencephalographic patterns r...

Automatic landslide mapping is crucial for a fast response in disaster scenario and improving susceptibility models. Recent studies highlighted the potential of deep learning methods automatic segmentation. However, only few works discuss generalization capacity these models to segment landslides areas that differ from ones used train In this study, we evaluated three different locations assess...

Ecosystem water-use efficiency (WUE) has been central in revealing the variability terrestrial carbon and water cycles. Short-rotation plantations such as Eucalyptus can simultaneously impact net primary production (NPP) actual evapotranspiration (ETa), components of WUE, resulting changes However, there are few detailed studies on WUE at catchment scale with high spatial remote sensing imagery...

The classification and recognition of agricultural crop types is an important application of remote sensing. New machine learning algorithms have emerged in the last years, but so far, few studies only have compared their performance and usability. Therefore, we compared three different state-of-the-art machine learning classifiers, namely Support Vector Machine (SVM), Artificial Neural Network...

Earth Observation has become a progressively important source of information for land use and land cover services over the past decades. At the same time, an increasing number of reconnaissance satellites have been set in orbit with ever increasing spatial, temporal, spectral, and radiometric resolutions. The available bulk of data, fostered by open access policies adopted by several agencies, ...

This study presents an approach to quantify thermokarst lake change and lake object structure change in spatial very high resolution remote sensing data as part of ESAs "Data User Element Permafrost". Lake Center points are used and multi temporal data is radiometrically normalized using a water mean rationing. A set of specific lake object characteristics (object shape, direction, lake object ...

The goal of this study is to develop an automatic supervised classification strategy and to find the necessary data sources to perform automatic tree species classification on single tree level for a large area. The derived forest map is used in a virtual forest testbed to estimate additional forest parameters like diameter at breast height and volume at single tree and stand level. A virtual f...