Bathymetric Mapping Of The Seafloor - A German Contribution To Completing The Map By 2030, Cruise No. MSM88/1 + MSM88/2, November 28, 2019 - January 14, 2020, Mindelo (Cabo Verde) - Mindelo (Cabo Verde) - Bridgetown (Barbados)

Despite over 100 years of acoustic seabed mapping, only around 15% of the seafloor has ever been directly mapped and little of the mapping performed has been systematic or over larger areas. The result is that our knowledge of seafloor structure is rudimentary and our understanding of the processes which form them has, in principle, advanced little since the advent of plate tectonics. Societally, the seafloor plays a vital role in humanity’s "life support system", for example providing habitat for marine organisms, stimulating mixing of ocean water as part of the overturning circulation system and increasingly being the site of industrial installations. It is scientifically and societally imperative that we bring the level of knowledge of the surface of our planet up to that of bodies like Moon and Mars that are mapped with a resolution better than 100 m per pixel. It is also essential that the data are made freely available to all to support research and conservation. The aim of this cruise was to map previously uncharted part of the tropical Atlantic using the ship’s multibeam system and to provide the data to global open databases as well as to acquire magnetic gradient data along the same tracks. Magnetic anomalies from so-called Oceanic Core Complexes challenged the conventional view that marine magnetic anomalies arose in the upper, extrusive layer of the oceanic crust, because the crust has been stripped away at these complexes. We therefore collected magnetic data simultaneously to the multibeam data in order to constrain the interpretation of the observed seabed morphology

A new approach for developing comprehensive agricultural drought index using satellite-derived biophysical parameters and factor analysis method

The accurate assessment of drought and its monitoring is highly depending on the selection of appropriate indices. Despite the availability of countless drought indices, due to variability in environmental properties, a single universally drought index has not been presented yet. In this study, a new approach for developing comprehensive agricultural drought index from satellite-derived biophysical parameters is presented. Therefore, the potential of satellite-derived biophysical parameters for improved understanding of the water status of pistachio (Pistachio vera L.) crop grown in a semiarid area is evaluated. Exploratory factor analysis with principal component extraction method is performed to select the most in?uential parameters from seven biophysical parameters including surface temperature (Ts), surface albedo (a), leaf area index (LAI), soil heat ?ux (Go), soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and net radiation (Rn). Ts and Gowere found as the most effective parameters by this method. However, Ts, LAI, a, and SAVI that accounts for 99.6 % of the total variance of seven inputs were selected to model a new biophysical water stress index (BPWSI). The values of BPWSI were stretched independently and compared with the range of actual evapotranspiration estimated through well-known METRIC (mapping evapotranspiration at high resolution with internal calibration) energy balance model. The results showed that BPWSI can be ef?ciently used for the prediction of the pistachio water status (RMSE of 0.52, 0.31, and 0.48 mm/day on three image dates of April 28, July 17, and August 2, 2010). The study con?rmed that crop water status is accounted by several satellite-based biophysical parameters rather than single parameter

Surface and sub-surface flow estimation at high temporal resolution using deep neural networks

Recent intensification in climate change have resulted in the rise of hydrological extreme events. This demands modeling of hydrological processes at high temporal resolution to better understand flow patterns in catchments. To model surface and sub-surface flows in a catchment we utilized a physically based model called Hydrological Simulated Program-FORTRAN and two deep learning-based models. One deep learning model consisted of only one long short-term memory (simple LSTM), whereas the other model simulated processes in each hydrological response unit (HRU) by defining one separate LSTM for each HRU (HRU-based LSTM). The models use environmental time-series data and two-dimensional spatial data to predict surface and sub-surface flows at 6-minute time step simultaneously. We tested our models in a tropical humid headwater catchment in northern Lao PDR and compared their performances. Our results showed that the simple LSTM model outperformed the other models on surface runoff prediction with the lowest MSE (7.4e - 5 m(3 )s(-1)), whereas HRU-based LSTM model better predicted patterns and slopes in sub-surface flow in comparison with the other models by having the smallest MSE value (3.2e - 4 m(3 )s(-1)). This study demonstrated the performance of a deep learning model when simulating hydrological cycle with high temporal resolution