Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII最新文献

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII Pub Date : 2021-10-12 DOI: 10.1117/12.2615051

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Single photon infrared lidar imagers for long range, continuous and autonomous methane monitoring 用于远程、连续和自主甲烷监测的单光子红外激光雷达成像仪

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII Pub Date : 2021-09-12 DOI: 10.1117/12.2598862

M. Reed

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Comparison of modeled evapotranspiration from the SETMI hybrid model informed with multispectral and thermal infrared imagery acquired with an unmanned aerial system SETMI混合模型的蒸散发模型与无人机系统获取的多光谱和热红外图像的比较

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII Pub Date : 2021-09-12 DOI: 10.1117/12.2604207

Mitch S Maguire, C. Neale, W. Woldt

{"title":"Comparison of modeled evapotranspiration from the SETMI hybrid model informed with multispectral and thermal infrared imagery acquired with an unmanned aerial system","authors":"Mitch S Maguire, C. Neale, W. Woldt","doi":"10.1117/12.2604207","DOIUrl":"https://doi.org/10.1117/12.2604207","url":null,"abstract":"Estimating actual crop evapotranspiration (ETc) is a critical component in tracking soil water availability and managing near real-time irrigation scheduling. Energy and water balance models are two common approaches for estimating daily crop ETc. The Spatial EvapoTranspiration Modeling Interface (SETMI) hybrid model combines these two approaches and has been used to increase the accuracy of modeled ETc and soil water content by assimilating actual ET values to update the soil water balance. In this study, modeled daily ETc from the two-source energy balance (TSEB), root zone water balance, and the hybrid modeling approach were compared to measured ETc from eddy covariance flux tower systems to quantify model accuracy. The TSEB model used the Priestly-Taylor approximation for estimating ETc and the water balance model was updated with reflectance-based crop coefficients. The models were informed with UAS acquired multispectral reflectance and thermal infrared imagery collected over irrigated and rainfed maize and soybean fields during the 2018-2020 growing seasons.","PeriodicalId":383037,"journal":{"name":"Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII","volume":"350 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123539856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Estimation of flood water depth distribution based on synthetic aperture radar images and inundation simulation 基于合成孔径雷达图像和淹没模拟的洪水水深分布估算

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII Pub Date : 2021-09-12 DOI: 10.1117/12.2599486

K. Yawata, S. Yamaguchi, Tomonori Yamamoto

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Probing of the multilayer structure of sunflower leaf 向日葵叶片多层结构的探讨

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII Pub Date : 2021-09-12 DOI: 10.1117/12.2600295

Yannick Abautret, M. Zerrad, D. Coquillat, R. Bendoula, G. Soriano, D. Héran, B. Grèzes-Besset, Frédéric Chazalet, C. Amra

{"title":"Probing of the multilayer structure of sunflower leaf","authors":"Yannick Abautret, M. Zerrad, D. Coquillat, R. Bendoula, G. Soriano, D. Héran, B. Grèzes-Besset, Frédéric Chazalet, C. Amra","doi":"10.1117/12.2600295","DOIUrl":"https://doi.org/10.1117/12.2600295","url":null,"abstract":"New techniques for agriculture science are widely explored since several decades in order to improve production yield. Measurements of optical properties at different scales of the crop are investigated and exploited to assess different parameters of interest such as state of stress. For instance, nowadays, there exists acquisition systems embedded in drones, mobile machines and satellites that are able to collect huge amount of hyperspectral imaging data. Identification of optical signature extracted from these techniques can help agronomist with adapting irrigation or distinguishing different plant varieties. These techniques allow to improve greatly the agricultural management, however they do not provide information about the internal structure of the plant leaf and their interaction with electromagnetic fields. Knowing precisely the plant leaf structure can bring critical information that can lead to the development of new techniques for phenotyping and precocious stress detection. To do this it is necessary to probe the plant at the leaf scale using THz instead of optical frequencies because the scattering sensitive phenomenon for plants is more drastic at optical frequencies. To find out how the light interact with the leaf, in a deterministic way, we can model the vegetal tissue as a stack of different physical layers characterized by the thickness and the optical index. \u0000In this study, funded by ANR project OptiPAG, we use a well-known reverse engineering technique to retrieve leaf architecture from the reflection data. In time domain, a short Terahertz pulse illuminates a multilayer sample that reflects a part of the signal carrying information about the sample structure. Using a numerical fit in the frequency domain allows to identify each layer and deduce the respective optical index over the input frequency range. \u0000We use a few classical (inorganic) etalon samples and analyze the echoes to reveal their thicknesses under the assumption of negligible absorption. Then, we use reverse engineering technique to fit the data in the THz range by taking into account the absorption, making an excellent agreement with the previous results with more accuracy. The measured thickness of the samples correspond very well with the manufacturing specifications. \u0000And finally we use this technique with vegetal tissues (sunflower leaves), that poses a much more complex situation. Results emphasize a 8-layer stack including trichomes, cuticules, epidermis and mesophyll layers and for each layer we extract the thickness and the complex index. To our knowledge this is the first time that the leaf multilayer structure is extracted with accuracy using a non-contact techniques.","PeriodicalId":383037,"journal":{"name":"Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121027193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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