Journal ArticleDOI
Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps
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TLDR
A variety of spectral indices now exist for various precision agriculture applications, rather than a focus on only normalised difference vegetation indices as discussed by the authors, and the spectral bandwidth has decreased dramatically with the advent of hyperspectral remote sensing, allowing improved analysis of specific compounds, molecular interactions, crop stress, and crop biophysical or biochemical characteristics.About:
This article is published in Biosystems Engineering.The article was published on 2013-04-01. It has received 1296 citations till now. The article focuses on the topics: Remote sensing application & Precision agriculture.read more
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Managing nitrogen for sustainable development
TL;DR: Historical patterns of agricultural nitrogen-use efficiency are examined and a broad range of national approaches to agricultural development and related pollution are found, to meet the 2050 global food demand projected by the Food and Agriculture Organization.
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Significant remote sensing vegetation indices: A review of developments and applications
Jinru Xue,Baofeng Su +1 more
TL;DR: The spectral characteristics of vegetation are introduced and the development of VIs are summarized, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision.
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Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry
TL;DR: A survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry—wherein the combination of UAV and hyperspectrals plays a center role—is presented in this paper.
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Remote sensing for agricultural applications: A meta-review
TL;DR: In this paper, the authors present the agronomical variables and plant traits that can be estimated by remote sensing, and describe the empirical and deterministic approaches to retrieve them, and provide a synthesis of the emerging opportunities that should strengthen the role of remote sensing in providing operational, efficient and long-term services for agricultural applications.
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Evaluating multispectral images and vegetation indices for precision farming applications from UAV images
TL;DR: The paper demonstrates the great potential of high-resolution UAV data and photogrammetric techniques applied in the agriculture framework to collect multispectral images and evaluate different VI, suggesting that these instruments represent a fast, reliable, and cost-effective resource in crop assessment for precision farming applications.
References
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Red and photographic infrared linear combinations for monitoring vegetation
TL;DR: In this article, the relationship between various linear combinations of red and photographic infrared radiances and vegetation parameters is investigated, showing that red-IR combinations to be more significant than green-red combinations.
Monitoring Vegetation Systems in the Great Plains with Erts
TL;DR: In this paper, a method has been developed for quantitative measurement of vegetation conditions over broad regions using ERTS-1 spectral bands 5 and 7, corrected for sun angle, which is shown to be correlated with aboveground green biomass on rangelands.
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A soil-adjusted vegetation index (SAVI)
TL;DR: In this article, a transformation technique was presented to minimize soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths, which nearly eliminated soil-induced variations in vegetation indices.
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A Modified Soil Adjusted Vegetation Index
TL;DR: In this article, a modified SAVI (MSAVI) was proposed to increase the dynamic range of the vegetation signal while further minimizing the soil background influences, resulting in greater vegetation sensitivity as defined by a vegetation signal to soil noise ratio.
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Derivation of leaf-area index from quality of light on the forest floor
TL;DR: The Leaf—area index of a forest can be measured by determining the ratio of light at 800 μm to that at 675 μm on the forest floor, based on the principle that leaves absorb relatively more red than infrared light.