Vegetation (pathology)

MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison

Jing Wei, Zhanqing Li, Yiran Peng, Lin Sun

Citations: 404 • 2018

UAV-based crop and weed classification for smart farming

Philipp Lottes, Raghav Khanna, Johannes Pfeifer, Roland Siegwart, Cyrill Stachniss

Citations: 384 • 2017

Plant Disease Detection Using Hyperspectral Imaging

Peyman Moghadam, Daniel B. Ward, Ethan Goan, Srimal Jayawardena, Pavan Sikka, Emili Hernández

Citations: 158 • 2017

Effective Vision‐based Classification for Separating Sugar Beets and Weeds for Precision Farming

Philipp Lottes, Markus Hörferlin, Slawomir Sander, Cyrill Stachniss

Citations: 128 • 2016

Aerosol optical thickness determination by exploiting the synergy of TERRA and AQUA MODIS

Jiakui Tang, Yong Xue, Tong Yu, Yanning Guan

Citations: 127 • 2004

Effects of atmospheric variation on AVHRR NDVI data

Jyoteshwar Nagol, Éric Vermote, Stephen D. Prince

Citations: 117 • 2008

Evaluation of the Moderate Resolution Imaging Spectroradiometer aerosol products at two Aerosol Robotic Network stations in China

Wen Mi, Zhanqing Li, Xiangao Xia, B. N. Holben, R. C. Levy, Fengsheng Zhao, Hongbin Chen, Maureen Cribb

Citations: 108 • 2007

A mobile tram system for systematic sampling of ecosystem optical properties

John A. Gamon, Yuanping Cheng, H CLAUDIO, L. Mackinney, David Sims

Citations: 98 • 2006

An effective classification system for separating sugar beets and weeds for precision farming applications

Philipp Lottes, Markus Hoeferlin, Slawomir Sander, Matthias Müter, Paul Schülze

Citations: 97 • 2016

Improving robot navigation in structured outdoor environments by identifying vegetation from laser data

Kai M. Wurm, Rainer Kümmerle, Cyrill Stachniss, Wolfram Burgard

Citations: 93 • 2009

Self‐supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain

Shengyan Zhou, Junqiang Xi, Matthew W. McDaniel, Takayuki Nishihata, Phil Salesses, Karl Iagnemma

Citations: 81 • 2012

Vegetation Detection for Driving in Complex Environments

David M. Bradley, Ranjith Unnikrishnan, J. Andrew Bagnell

Citations: 80 • 2007

Revised mineral dust emissions in the atmospheric chemistry–climate model EMAC (MESSy 2.52 DU_Astitha1 KKDU2017 patch)

Klaus Klingmüller, Swen Metzger, Mohamed Abdelkader, Vlassis A. Karydis, Georgiy Stenchikov, Andrea Pozzer, Jos Lelieveld

Citations: 69 • 2018

Biomass burning aerosol characteristics for different vegetation types in different aging periods

Shuaiyi Shi, Tianhai Cheng, Xingfa Gu, Hong Guo, Yu Wu, Ying Wang

Citations: 59 • 2019

A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture

Ebrahim Babaeian, Paheding Sidike, Maria Newcomb, Maitiniyazi Maimaitijiang, Scott A. White, Jeffrey Demieville, Richard W. Ward, Morteza Sadeghi, David LeBauer, Scott B. Jones, Vasit Sagan, Markus Tuller

Citations: 59 • 2019

UAV-based high-throughput phenotyping to discriminate barley vigour with visible and near-infrared vegetation indices

Salvatore Filippo Di Gennaro, Fulvia Rizza, Franz‐W. Badeck, Andrea Berton, Stefano Delbono, Beniamino Gioli, Piero Toscano, Alessandro Zaldei, Alessandro Matese

Citations: 55 • 2017

Retrieval of the Haze Optical Thickness in North China Plain Using MODIS Data

Shenshen Li, Liangfu Chen, Xiaozhen Xiong, Jinhua Tao, Lin Su, Dong Han, Yang Liu

Citations: 50 • 2012

Improved vegetation segmentation with ground shadow removal using an HDR camera

Hyun Kwon Suh, J.W. Hofstee, E.J. van Henten

Citations: 49 • 2017

Impacts of dust aerosol and adjacency effects on the accuracy of Landsat 8 and RapidEye surface reflectances

Rasmus Houborg, Matthew F. McCabe

Citations: 48 • 2017

Getting simultaneous red and near-infrared band data from a single digital camera for plant monitoring applications: Theoretical and practical study

Gilles Rabatel, Nathalie Gorretta, Sabrina Labbé

Citations: 47 • 2013