Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation
TL;DR: LAI data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate model to estimate crop yield using a re-calibration data assimilation approach and provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed.
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Abstract: Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.
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Assimilation of remote sensing into crop growth models: Current status and perspectives
Jianxi Huang,Jose Gomez-Dans,Hai Huang,Hongyuan Ma,Qingling Wu,Philip Lewis,Shunlin Liang,Shunlin Liang,Zhongxin Chen,Jing-Hao Xue,Yantong Wu,Feng Zhao,Jing Wang,Xianhong Xie +13 more
TL;DR: A critique of both the advantages and disadvantages of both EO data and crop growth models is provided, and a solid and robust framework for DA is introduced, where different DA methods are shown to be derived from taking different assumptions in solving for the a posteriori probability density function using Bayes’ rule.
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Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
TL;DR: It is concluded that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements, and compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel- 2 A + B constellation features.
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Developing a machine learning based cotton yield estimation framework using multi-temporal UAS data
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TL;DR: In this paper, a machine learning framework was developed for cotton yield estimation using multi-temporal remote sensing data collected from unmanned aircraft system (UAS) data, which was based on an artificial neural network (ANN).
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Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data
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TL;DR: In this article , a training dataset was generated using the leaf-canopy RTM PROSAIL in combination with the atmospheric model 6SV. Successful reduction of the training dataset by 78% was achieved using the active learning technique Euclidean distance-based diversity (EBD), and highly accurate validation results of LAI and upscaled leaf variables were obtained against in situ field data from the validation study site Munich-North-Isar (MNI), with normalized root mean square errors (NRMSE) from 6% to 13%.
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Remote Sensing Applications in Sugarcane Cultivation: A Review
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