Optical vegetation indices for monitoring terrestrial ecosystems globally

Y Zeng, D Hao, A Huete, B Dechant, J Berry… - Nature Reviews Earth & …, 2022‏ - nature.com
Vegetation indices (VIs), which describe remotely sensed vegetation properties such as
photosynthetic activity and canopy structure, are widely used to study vegetation dynamics …

[HTML][HTML] Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

K Berger, M Machwitz, M Kycko, SC Kefauver… - Remote sensing of …, 2022‏ - Elsevier
Remote detection and monitoring of the vegetation responses to stress became relevant for
sustainable agriculture. Ongoing developments in optical remote sensing technologies have …

Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress

GH Mohammed, R Colombo, EM Middleton… - Remote sensing of …, 2019‏ - Elsevier
Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front
in terrestrial vegetation science, with emerging capability in space-based methodologies …

Significant remote sensing vegetation indices: A review of developments and applications

J Xue, B Su - Journal of sensors, 2017‏ - Wiley Online Library
Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and
effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor …

A review of advanced technologies and development for hyperspectral-based plant disease detection in the past three decades

N Zhang, G Yang, Y Pan, X Yang, L Chen, C Zhao - Remote Sensing, 2020‏ - mdpi.com
The detection, quantification, diagnosis, and identification of plant diseases is particularly
crucial for precision agriculture. Recently, traditional visual assessment technology has not …

PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle

JB Féret, AA Gitelson, SD Noble… - Remote Sensing of …, 2017‏ - Elsevier
Leaf pigments provide valuable information about plant physiology. High resolution
monitoring of their dynamics will give access to better understanding of processes occurring …

Remote sensing and machine learning for crop water stress determination in various crops: a critical review

SS Virnodkar, VK Pachghare, VC Patil, SK Jha - Precision Agriculture, 2020‏ - Springer
The remote sensing (RS) technique is less cost-and labour-intensive than ground-based
surveys for diverse applications in agriculture. Machine learning (ML), a branch of artificial …

Hyperspectral sensors and imaging technologies in phytopathology: state of the art

AK Mahlein, MT Kuska, J Behmann… - Annual review of …, 2018‏ - annualreviews.org
Plant disease detection represents a tremendous challenge for research and practical
applications. Visual assessment by human raters is time-consuming, expensive, and error …

Unmanned Aerial System (UAS)-based phenoty** of soybean using multi-sensor data fusion and extreme learning machine

M Maimaitijiang, A Ghulam, P Sidike, S Hartling… - ISPRS Journal of …, 2017‏ - Elsevier
Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is
imperative for high-throughput phenoty** in precision agriculture. Although fusion of data …

Urban tree classification based on object-oriented approach and random forest algorithm using unmanned aerial vehicle (UAV) multispectral imagery

Q Guo, J Zhang, S Guo, Z Ye, H Deng, X Hou… - Remote Sensing, 2022‏ - mdpi.com
Timely and accurate information on the spatial distribution of urban trees is critical for
sustainable urban development, management and planning. Compared with satellite-based …