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Journal ArticleDOI

Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3

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TLDR
Results confirm the importance of the red-edge bands on particularly Sentinel-2 for agricultural applications, because of the combination with its high spatial resolution of 20 m and linear estimators of canopy chlorophyll and N content.
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This article is published in International Journal of Applied Earth Observation and Geoinformation.The article was published on 2013-08-01. It has received 490 citations till now. The article focuses on the topics: Spectral bands & Red edge.

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Citations
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Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images.

TL;DR: This study analyzed and compared the potential of satellite sensors with various spatial and temporal resolutions to predict SOC content and C:N ratio in Switzerland and revealed that remote sensing variables were the best predictors for soil prediction models.
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Current and near-term advances in Earth observation for ecological applications

TL;DR: In this article, the authors present a review of 48 instruments and 13 platforms with multiple instruments that either collected data in the 2000s, were recently launched, or are planned for launch in this decade.
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Object-based time-constrained dynamic time warping classification of crops using Sentinel-2

TL;DR: The proposed workflow is the first implementation of DTW in an object-based image analysis (OBIA) environment and represents a promising step towards generating fast, accurate, and ready-to-use agricultural data products.
Posted Content

PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents

TL;DR: Findings of this study can conclude that PROSPECT-PRO has a high potential in establishing the carbon-to-nitrogen ratio based on the retrieved CBC- to-proteins ratio.
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Assessment of Water Quality Parameters Using Temporal Remote Sensing Spectral Reflectance in Arid Environments, Saudi Arabia

TL;DR: In this paper, water quality parameters of chlorophyll a concentration, nitrate concentration, and water turbidity were used to estimate the water quality in the dam lake of Wadi Baysh, Saudi Arabia.
References
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Journal ArticleDOI

Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages

TL;DR: Developing spectral indices for prediction of leaf pigment content that are relatively insensitive to species and leaf structure variation and thus could be applied in larger scale remote-sensing studies without extensive calibration are developed.
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Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services

TL;DR: An overview of the GMES Sentinel-2 mission including a technical system concept overview, image quality, Level 1 data processing and operational applications is provided.
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Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance

TL;DR: In this paper, a wide range of leaf chlorophyll levels were established in field-grown corn (Zea mays L.) with the application of 8 N levels: 0, 12.5%, 25, 50, 75, 100, 125, and 150% of the recommended rate.
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Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves

TL;DR: Spectral reflectance of maple, chestnut, wild vine and beech leaves in a wide range of pigment content and composition was investigated and it was shown that reciprocal reflectance (R lambda)-1 in the spectral range lambda related closely to the total chlorophyll content in leaves of all species.
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Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture

TL;DR: In this paper, a combined modeling and indices-based approach is presented to predict the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil background effects.
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