A5001726575- Remote Sensing in Agriculture
- Land Use and Ecosystem Services
- Remote Sensing and LiDAR Applications
- Soil Moisture and Remote Sensing
- Soil Geostatistics and Mapping
- Calibration and Measurement Techniques
- Geochemistry and Geologic Mapping
- Atmospheric and Environmental Gas Dynamics
- Remote-Sensing Image Classification
- Remote Sensing and Land Use
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Soil Carbon and Nitrogen Dynamics
- Geophysics and Gravity Measurements
- Image and Signal Denoising Methods
- Crop Yield and Soil Fertility
- Plant Water Relations and Carbon Dynamics
- Greenhouse Technology and Climate Control
- Peatlands and Wetlands Ecology
- Infrared Target Detection Methodologies
- Leaf Properties and Growth Measurement
- Meteorological Phenomena and Simulations
- Potato Plant Research
- Planetary Science and Exploration
- Climate change impacts on agriculture
- Sustainability and Climate Change Governance
GFZ Helmholtz Centre for Geosciences
2014-2024
Universidade Federal de Mato Grosso do Sul
2024
Delft University of Technology
2024
Vanderbilt University
1986
The status, changes, and disturbances in geomorphological regimes can be regarded as controlling regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, global scales is not only necessary to conserve geodiversity, but also preserve biodiversity, well improve biodiversity conservation ecosystem management. Numerous remote sensing (RS) approaches platforms have been used the past enable a cost-effective, increasingly freely available, comprehensive,...
The knowledge about heterogeneity on agricultural fields is essential for a sustainable and effective field management. This study investigates the performance of Synthetic Aperture Radar (SAR) data Sentinel-1 satellites to detect variability between within in two test sites Germany. For this purpose, temporal profiles SAR backscatter VH VV polarization as well their ratio VH/VV multiple wheat barley are illustrated interpreted considering differences acquisition settings, years, crop types...
In this article, we propose a deep learning-based algorithm for the classification of crop types from Sentinel-1 and Sentinel-2 time series data which is based on celebrated transformer architecture. Crucially, enable our to do early classification, i.e., predict at arbitrary points in year with single trained model (progressive intra-season classification). Such season predictions are practical relevance instance yield forecasts or modeling agricultural water balances, therefore being...
In light of the increasing demand for food production, climate change challenges agriculture, and economic pressure, precision farming is an ever-growing market. The development distribution remote sensing applications also growing. availability extensive spatial temporal data—enhanced by satellite open-source policies—provides attractive opportunity to collect, analyze use agricultural data at farm scale beyond. division individual fields into zones differing yield potential (management...
Local parameters for climate modelling are highly dependent on crop types and their phenological growth stage. The land cover change of agricultural areas during the growing season provides important information to distinguish types. presented progressive classification algorithm identifies based development corresponding reflectance characteristics in multitemporal satellite images four sensors Landsat-7 -8, Sentinel-2A RapidEye. It distinguishes not only retrospectively, but progressively...
Core Ideas TERENO‐NE investigates the regional impact of global change. We facilitate interdisciplinary geo‐ecological research. Our data sets comprise monitoring and geoarchives. are able to bridge time scales from minutes millennia. The Northeast German Lowland Observatory (TERENO‐NE) was established investigate climate land use focuses on lowlands, for which a high vulnerability has been determined due increasing temperatures decreasing amounts precipitation projected coming decades. To...
The machine learning method, random forest (RF), is applied in order to derive biophysical and structural vegetation parameters from hyperspectral signatures. Hyperspectral data are, among other things, characterized by their high dimensionality autocorrelation. Common multivariate regression approaches, which usually include only a limited number of spectral indices as predictors, do not make full use the available information. In contrast, methods, such RF, are supposed be better suited...
In the face of rapid global change it is imperative to preserve geodiversity for overall conservation biodiversity. Geodiversity important understanding complex biogeochemical and physical processes directly indirectly linked biodiversity on all scales ecosystem organization. Despite great importance geodiversity, there a lack suitable monitoring methods. Compared conventional in-situ techniques, remote sensing (RS) techniques provide pathway towards cost-effective, increasingly more...
Abstract Information provided by satellite data is becoming increasingly important in the field of agriculture. Estimating biomass, nitrogen content or crop yield can improve farm management and optimize precision agriculture applications. A vast amount made available both as map material from space. However, it up to user select appropriate for a particular problem. Without knowledge, this may even entail an economic risk. This study therefore investigates direct relationship between six...
The time series of synthetic aperture radar (SAR) data are commonly and successfully used to monitor the biophysical parameters agricultural fields. Because, until now, mainly backscatter coefficients have been analysed, this study examines potentials entropy, anisotropy, alpha angle derived from a dual-polarimetric decomposition Sentinel-1 crop development. temporal profiles these analysed for wheat barley in vegetation periods 2017 2018 13 fields two test sites Northeast Germany. relation...
Satellites are now routinely used for measuring water and land surface reflectance hence environmentally relevant parameters such as aquatic chlorophyll a concentration terrestrial vegetation indices. For each satellite mission, radiometric validation is needed at bottom of atmosphere all spectral bands covering typical conditions where the data will be used. Existing networks AERONET-OC RadCalNet provide vital information validation, but (AERONET-OC) do not cover or (RadCalNet) types...
When defining indicators on the environment, use of existing initiatives should be a priority rather than redefining each time. From an Information, Communication and Technology perspective, data interoperability standardization are critical to improve access exchange as promoted by Group Earth Observations. GEOEssential is following end-user driven approach Essential Variables (EVs), intermediate value between environmental policy their appropriate sources. international local scales,...
The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that WCM accurately LAI if is effectively calibrated. However, calibration this requires access field measures as well soil moisture. In contrast, machine learning (ML) algorithms trained satellite data, even moisture are not available. study, a support vector (SVM) was for corn, soybeans, rice, and...
Several holistic approaches are based on the description of socio-ecological systems to address sustainability challenge. Essential Variables (EVs) have potential support these by describing status Earth system through monitoring and modeling. The different classes EVs can be organized along environmental policy framework Drivers, Pressures, States, Impacts Responses. EV concept represents an opportunity strengthen providing observations seize fundamental dimensions Group Observation (GEO)...
Soil degradation is a major threat for European soils and therefore, the Commission recommends intensifying research on soil monitoring to capture changes over time space. Imaging spectroscopy promising technique create spatially accurate topsoil maps based hyperspectral remote sensing data. We tested application of local partial least squares regression (PLSR) airborne HySpex simulated satellite EnMAP (Environmental Mapping Analysis Program) data acquired in north-eastern Germany quantify...
Monitoring the phenological development of agricultural plants is high importance for farmers to adapt their management strategies and estimate yields. The aim this study analyze sensitivity remote sensing features winter wheat barley test transferability in two sites Northeast Germany years. Local minima, local maxima breakpoints smoothed time series synthetic aperture radar (SAR) data Sentinel-1 VH (vertical-horizontal) VV (vertical-vertical) intensities ratio VH/VV; polarimetric entropy,...
Forest biochemical and biophysical variables their spatial temporal distribution are essential inputs to process-orientated ecosystem models. To provide this information, imaging spectroscopy appears be a promising tool. In context, the present study investigates potential of spectral unmixing derive sub-pixel crown component fractions in temperate deciduous forest ecosystem. However, high proportion foliage complex vegetation structure leads problem saturation effects, when applying...
Hyperspectral images are of increasing importance in remote sensing applications. Imaging spectrometers provide semi-continuous spectra that can be used for physics based surface cover material identification and quantification. Preceding radiometric calibrations serve as a basis the transformation measured signals into units such radiance. Pushbroom sensors collect incident radiation by at least one detector array utilizing photoelectric effect. Temporal variations characteristics differ...
Abstract Precision agriculture, as part of modern thrives on an enormously growing amount information and data for processing application. The spatial used yield forecasting or the delimitation management zones are very diverse, often different quality in units to each other. For various reasons, approaches combining geodata complex, but necessary if all relevant is be taken into account. Data fusion with belief structures offers possibility link expert knowledge, include experiences beliefs...
This study proposes the development of a landscape-scale multitemporal soil pattern analysis (MSPA) method for organic matter (OM) estimation using RapidEye time series data and GIS spatial modeling, which is based on methodology Blasch et al. The results demonstrate (i) potential MSPA to predict OM single fields field composites with varying geomorphological, topographical, pedological backgrounds (ii) conversion from scale multi-field landscape scale. For fields, as well composites,...
There is a growing recognition of the interdependencies among supply systems that rely upon food, water and energy. Billions people lack safe sufficient access to these systems, coupled with rapidly global demand increasing resource constraints. Modeling frameworks are considered one few means available understand complex interrelationships sectors, however development nexus related has been limited. We describe three open-source models well known in their respective domains (i.e. TerrSysMP,...
The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results the assignment incident electromagnetic radiation to digital numbers and reduces striping caused by slightly different responses pixel detectors. However, due uncertainties some remains. This publication presents a new reduction framework that efficiently linear nonlinear miscalibrations image-driven, recalibration rescaling. proposed framework—Reduction Of Miscalibration...