Remote sensing of vegetation dynamics in agro-ecosystems using smap vegetation optical depth and optical vegetation indices

机译：基于smap植被光学深度和光学植被指数的农业生态系统植被动态遥感

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The ESA's SMOS and the NASA's SMAP missions, launched in 2009 and 2015, respectively, are the first two missions having on-board L-band microwave sensors, which are very sensitive to the water content in soils and vegetation. Focusing on the vegetation signal at L-band, we have implemented an inversion approach for SMAP that allows deriving vegetation optical depth (VOD, a microwave parameter related to biomass and plant water content) alongside soil moisture, without reliance on ancillary optical information on vegetation. This work aims at using this new observational data to monitor the phenology of crops in major global agro-ecosystems and enhance present agricultural monitoring and prediction capabilities. Core agricultural regions have been selected worldwide covering major crops (corn, soybean, wheat, rice). The complementarity and synergies between the microwave vegetation signal, sensitive to biomass water-uptake dynamics, and optical indices, sensitive to canopy greenness, are explored. Results reveal the value of L-band VOD as an independent ecological indicator for global terrestrial biosphere studies.

机译：ESA的SMOS和NASA的SMAP任务分别于2009年和2015年启动，是前两个带有机载L波段微波传感器的任务，它们对土壤和植被中的水分非常敏感。针对L波段的植被信号，我们对SMAP实施了一种反演方法，该方法可与土壤水分一起导出植被光学深度（VOD，与生物量和植物水分含量相关的微波参数），而无需依赖于植被的辅助光学信息。这项工作旨在利用这一新的观测数据来监测全球主要农业生态系统中作物的物候状况，并增强当前的农业监测和预报能力。全世界已选定涵盖主要农作物（玉米，大豆，小麦，水稻）的核心农业地区。探索了对生物量吸水动力学敏感的微波植被信号与对冠层绿色敏感的光学指标之间的互补性和协同性。结果揭示了L波段VOD作为全球陆地生物圈研究的独立生态指标的价值。

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《International Geoscience and Remote Sensing Symposium》|2017年|4346-4349|共4页
会议地点 Fort Worth(US)
作者
M. Piles; G. Camps-Valls; D. Chaparro; D. Entekhabi; A. G. Konings; T. Jagdhuber;
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作者单位

Image Processing Lab (IPL) Universitat de València 46980 València Spain;

Universitat Politècnica de Catalunya IEEC/UPC Jordi Girona 1-3 08034 Barcelona Spain;

Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA;

Department of Earth System Science Stanford University Stanford CA 94305 USA;

German Aerospace Center Microwaves and Radar Institute P.O. Box 1116 82234 Wessling;

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Remote sensing; Indexes; Geoscience;

机译：遥感;索引；地球科学;

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专利

1. Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel [J] . Tian Feng, Brandt Martin, Liu Yi Y., Remote Sensing of Environment: An Interdisciplinary Journal . 2016,第Null期

机译：遥感干旱地区的植被动态：使用AVHRR NDVI和西非萨赫勒地区的原地绿色生物量数据评估植被光学深度（VOD）
2. Remote sensing of vegetation dynamics in drylands : Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel [J] . 関山　絢子写真测量とリモ—トセンシング . 2016,第2期

机译：遥感干旱地区的植被动态：使用AVHRR NDVI和西非萨赫勒地区的原地绿色生物量数据评估植被光学深度（VOD）
3. Effective vegetation optical depth retrieval using microwave vegetation indices from WindSat data for short vegetation [J] . Li Yunqing, Shi Jiancheng, Zhao Tianjie Journal of Applied Remote Sensing . 2015,第Null期

机译：利用WindSat数据中的微波植被指数对短植被进行有效的植被光学深度检索
4. Remote sensing of vegetation dynamics in agro-ecosystems using smap vegetation optical depth and optical vegetation indices [C] . M. Piles, G. Camps-Valls, D. Chaparro, IEEE International Geoscience and Remote Sensing Symposium . 2017

机译：使用Smap植被光学深度和光学植被指数遥感植被动力学
5. Estimation of annual Groundwater Evapotranspiration from Phreatophyte Vegetation in the Great Basin using Remotely Sensed Vegetation Indices and Ground Based Flux Tower measurements [D] . Beamer, Jordan P. 2011

机译：利用遥感植被指数和地基通量塔测量法估算大盆地植物群落植物的年度地下水蒸散量
6. Relationship Between Remotely-sensed Vegetation Indices Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape [O] . Edward P. Glenn, Alfredo R. Huete, Pamela L. Nagler, 2008

机译：遥感植被指数冠层属性与植物生理过程之间的关系：什么是植被指数可以告诉我们的景观
7. Remote sensing of vegetation dynamics in agro-ecosystems using smap vegetation optical depth and optical vegetation indices [O] . M. Piles, G. Camps-Valls, D. Chaparro, 2017

机译：使用Smap植被光学深度和光学植被指数遥感植被动力学
8. Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neural Networks [R] . Kimes, Daniel S., Nelson, Ross F. 1998

机译：利用神经网络从遥感光学/雷达数据预测生态重要植被变量

Remote sensing of vegetation dynamics in agro-ecosystems using smap vegetation optical depth and optical vegetation indices

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