Remote sensing for agricultural applications: A meta-review

M Weiss, F Jacob, G Duveiller - Remote sensing of environment, 2020 - Elsevier
Agriculture provides humanity with food, fibers, fuel, and raw materials that are paramount
for human livelihood. Today, this role must be satisfied within a context of environmental …

Discovering causal relations and equations from data

G Camps-Valls, A Gerhardus, U Ninad, G Varando… - Physics Reports, 2023 - Elsevier
Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

When Gaussian process meets big data: A review of scalable GPs

H Liu, YS Ong, X Shen, J Cai - IEEE transactions on neural …, 2020 - ieeexplore.ieee.org
The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

[HTML][HTML] Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach

M Jung, C Schwalm, M Migliavacca, S Walther… - …, 2020 - bg.copernicus.org
FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes
between the biosphere and the atmosphere. Since eddy covariance flux towers have a …

[HTML][HTML] Assimilation of remote sensing into crop growth models: Current status and perspectives

J Huang, JL Gómez-Dans, H Huang, H Ma… - Agricultural and forest …, 2019 - Elsevier
Timely monitoring of crop lands is important in order to make agricultural activities more
sustainable, as well as ensuring food security. The use of Earth Observation (EO) data …

Remote sensing in field crop monitoring: A comprehensive review of sensor systems, data analyses and recent advances

E Omia, H Bae, E Park, MS Kim, I Baek, I Kabenge… - Remote Sensing, 2023 - mdpi.com
The key elements that underpin food security require the adaptation of agricultural systems
to support productivity increases while minimizing inputs and the adverse effects of climate …

Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions

K Berger, J Verrelst, JB Féret, Z Wang… - Remote Sensing of …, 2020 - Elsevier
Nitrogen (N) is considered as one of the most important plant macronutrients and proper
management of N therefore is a pre-requisite for modern agriculture. Continuous satellite …

Quantifying vegetation biophysical variables from imaging spectroscopy data: A review on retrieval methods

J Verrelst, Z Malenovský, C Van der Tol… - Surveys in …, 2019 - Springer
An unprecedented spectroscopic data stream will soon become available with forthcoming
Earth-observing satellite missions equipped with imaging spectroradiometers. This data …

[图书][B] Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences

G Camps-Valls, D Tuia, XX Zhu, M Reichstein - 2021 - books.google.com
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …

[HTML][HTML] Retrieval of aboveground crop nitrogen content with a hybrid machine learning method

K Berger, J Verrelst, JB Féret, T Hank, M Wocher… - International Journal of …, 2020 - Elsevier
Hyperspectral acquisitions have proven to be the most informative Earth observation data
source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant …