Remote Sensing

PARSe Biodiversity

Biodiversity effects on Plant-Atmosphere interactions analysed with Remote Sensing (PARSe Biodiversity)

Cubo

Easily create EO mini cubes from STAC in Python.

Awesome Spectral Indices

A ready-to-use curated list of Spectral Indices for Remote Sensing applications.

The Leaf Is Always Greener on the Other Side of the Lab: Optical in-Situ Indicators for Leaf Chlorophyll Content Need Improvement for Semi-Natural Grassland Areas

Leaf chlorophyll content (LCC) is an important indicator of plant health. Earth observation facilitates LCC monitoring on large spatial scales and within short time intervals by retrieving canopy LCC from spectral signals. However, in-situ …

Spectral: Awesome Spectral Indices Deployed via the Google Earth Engine JavaScript API

Spectral Indices derived from Remote Sensing (RS) data are widely used for characterizing Earth System dynamics. The increasing amount of spectral indices led to the creation of spectral indices catalogues, such as the Awesome Spectral Indices (ASI) …

Remote Sensing of Soil Moisture

Soil moisture is an essential climate variable and knowledge about its state and dynamics is vital for numerous applications, from agricultural drought monitoring to studying land– atmosphere interactions. Remote sensing instruments mounted on …

Transfer Learning from Citizen Science Photographs Enables Plant Species Identification in UAV Imagery

Accurate information on the spatial distribution of plant species and communities is in high demand for various fields of application, such as nature conservation, forestry, and agriculture. A series of studies has shown that Convolutional Neural …

A Roadmap for High-Resolution Satellite Soil Moisture Applications – Confronting Product Characteristics with User Requirements

Soil moisture observations are of broad scientific interest and practical value for a wide range of applications. The scientific community has made significant progress in estimating soil moisture from satellite-based Earth observation data, …

Convolutional Neural Networks Accurately Predict Cover Fractions of Plant Species and Communities in Unmanned Aerial Vehicle Imagery

Unmanned Aerial Vehicles (UAV) greatly extended our possibilities to acquire high resolution remote sensing data for assessing the spatial distribution of species composition and vegetation characteristics. Yet, current pixel- or texture-based …

Monitoring Plant Functional Diversity Using the Reflectance and Echo from Space

Plant functional diversity (FD) is an important component of biodiversity. Evidence shows that FD strongly determines ecosystem functioning and stability and also regulates various ecosystem services that underpin human well-being. Given the …