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A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods

Yanling Ding, Hongyan Zhang, Zhongqiang Wang, Qiaoyun Xie, Yeqiao Wang, Lin Liu, Christopher C. Hall
2020 Remote Sensing  
This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data.  ...  This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended.  ...  Acknowledgments: The authors would like to extend their appreciation to Xingming Zheng and Tao Jiang of Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences and their laboratory  ... 
doi:10.3390/rs12091470 fatcat:vj753t3v3fekti7va53fxyjola

Estimation of Winter Wheat Residue Coverage Based on GF-1 Imagery and Machine Learning Algorithm

Qilei Zhu, Xingang Xu, Zhendong Sun, Dong Liang, Xiaofei An, Liping Chen, Guijun Yang, Linsheng Huang, Sizhe Xu, Min Yang
2022 Agronomy  
and method can also provide a useful reference for CRC estimates of other crops.  ...  The estimations of wheat CRC with the high-resolution GF-1 data were significantly better than those with the Sentinel-2 data, and among multiple machine learning algorithms adopted to estimate wheat CRC  ...  Acknowledgments: We extend our warm thanks to the technical teams at the Beijing Academy of Agricultural and Forestry Sciences who participated in the ground truth measurement campaigns and data processing  ... 
doi:10.3390/agronomy12051051 fatcat:5hbj5dprrva7lg6lafcziiogiq

Estimating soil moisture over winter wheat fields during growing season using machine learning methods

Lin Chen, Minfeng Xing, Binbin He, Jinfei Wang, Jiali Shang, Xiaodong Huang, Min Xu
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
This study finally concluded that using polarimetric decomposition parameters combined with machine learning and feature selection methods could effectively estimate soil moisture at a high accuracy, which  ...  His research interests include the SAR image processing, the application of UAV, and quantitative estimation of land surface variables from satellite remote sensing and on integration of multiple data  ...  Generally, soil moisture estimation based on SAR data can be realized using the following approaches [25] : (1) empirical/semi-empirical models; (2) theoretical electromagnetic models; (3) machine learning  ... 
doi:10.1109/jstars.2021.3067890 fatcat:ddeyytfn4zeyrht7ybucykwzra

A Method to Estimate Surface Soil Moisture and Map the Irrigated Cropland Area Using Sentinel-1 and Sentinel-2 Data

Saman Rabiei, Ehsan Jalilvand, Massoud Tajrishy
2021 Sustainability  
Moreover, a time series of estimated SSM based on Sentinel-1 (SSM-S1), Sentinel-2 (SSM-S2), and SSM derived from SMAP-Sentinel1 was compared.  ...  Three supervised machine learning algorithms, multilayer perceptron (MLP), a convolutional neural network (CNN), and linear regression models, were trained to estimate changes in SSM based on the variation  ...  The authors would like to thank the following people for helping us with this project; Goldansaz, Jahansuz, and Rafiei, whose collaboration greatly enhanced the data collection procedure.  ... 
doi:10.3390/su132011355 fatcat:5cyrbo2xjbegbfw7wrh4q3rp7e

Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3

Jochem Verrelst, Jordi Muñoz, Luis Alonso, Jesús Delegido, Juan Pablo Rivera, Gustavo Camps-Valls, José Moreno
2012 Remote Sensing of Environment  
By using data from the ESA-led field campaign SPARC (Barrax, Spain) we have compared the utility of four state-of-the-art machine learning regression algorithms and four different S2 and S3 band settings  ...  Machine learning regression algorithms may be powerful candidates for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear  ...  This work was partially supported by projects AYA2008-05965-C04-03, AYA2010-21432-C02-01 and CSD2007-00018 funded by the Spanish Ministry of Science and Innovation.  ... 
doi:10.1016/j.rse.2011.11.002 fatcat:m2q4sjikmndmzphn6jloiv7obu

Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India

Sanjiv K. Sinha, Hitendra Padalia, Anindita Dasgupta, Jochem Verrelst, Juan Pablo Rivera
2020 International Journal of Applied Earth Observation and Geoinformation  
Look-Up Table ( LUT)-inversion, MLRA-GPR (Machine Learning Regression Algorithm-Gaussian Processes Regression) and empirical models, for estimating the LAI of tropical deciduous plantation using ARTMO  ...  Vegetation indices (VIs) derived from 740 nm, 783 nm and 2190 nm band combinations of Sentinel-2 offered the best prediction of LAI.  ...  Prakash Chauhan, Director, and Dr. S. K Srivastav, Dean (A), Indian Institute of Remote Sensing for providing logistic facilities to execute this research.  ... 
doi:10.1016/j.jag.2019.102027 fatcat:hliikqcvgjfflg6hs7fouoghri

Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

Phamchimai Phan, Nengcheng Chen, Lei Xu, Zeqiang Chen
2020 Remote Sensing  
Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction.  ...  Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam.  ...  provided the data for this study.  ... 
doi:10.3390/rs12111814 fatcat:zs4bwgb26zeyfpouo4224fwcc4

Estimating adoption and impacts of agricultural management practices in developing countries using satellite data. A scoping review

Christoph Kubitza, Vijesh V. Krishna, Urs Schulthess, Meha Jain
2020 Agronomy for Sustainable Development  
The main findings of the paper are threefold: (1) satellite data have been successfully used to detect agricultural practices, such as cropping intensity, tillage, crop residue cover, irrigation, and soil  ...  and water conservation; (2) only a few studies have estimated the yield impacts of agricultural practices, although the estimation of crop yields with satellite data is fairly developed; and (3) only  ...  Funding information This study received support from the CGIAR Research Program on wheat agri-food systems (CRP WHEAT) in the form of consultation fee of the first author (C Kubitza) and salary of the  ... 
doi:10.1007/s13593-020-0610-2 fatcat:3qwigl2kdzh6jpjb6km5gh7r6y

High-Resolution Soybean Yield Mapping Across the US Midwest Using Subfield Harvester Data

Walter T. Dado, Jillian M. Deines, Rinkal Patel, Sang-Zi Liang, David B. Lobell
2020 Remote Sensing  
Here, we assess machine learning models' capacity for soybean yield prediction using a unique ground-truth dataset of high-resolution (5 m) yield maps generated from combine harvester yield monitor data  ...  First, we compare random forest (RF) implementations, testing a range of feature engineering approaches using Sentinel-2 and Landsat spectral data for 20- and 30-m scale yield prediction.  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/rs12213471 fatcat:ytljbl5p7fdfff5g23gxmyffty

Mapping Regional Soil Organic Matter Based on Sentinel-2A and MODIS Imagery Using Machine Learning Algorithms and Google Earth Engine

Meiwei Zhang, Meinan Zhang, Haoxuan Yang, Yuanliang Jin, Xinle Zhang, Huanjun Liu
2021 Remote Sensing  
This study aimed to investigate the influence of different remotely sensed images and machine learning algorithms on SOM prediction.  ...  ., full-band and common-band variable datasets of Sentinel-2A and MODIS images using Google Earth Engine (GEE).  ...  Acknowledgments: We would like to sincerely thank the editors and anonymous reviewers for their valuable and constructive suggestions that helped us improve the manuscript.  ... 
doi:10.3390/rs13152934 fatcat:oy3r7tnolzbgtamqyhjnkn25ka

Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields

Ephrem Habyarimana, Faheem S. Baloch, Jie Zhang
2021 PLoS ONE  
Hand-held optical meter-derived CI and Sentinel-2-derived fAPAR showed comparable effects on machine learning performance, but CI outperformed NDVI and was therefore considered as a good alternative to  ...  This work aimed therefore at closing this gap by evaluating the performance of machine learning modelling of in-season sorghum biomass yields based on Sentinel-2-derived fAPAR and simpler high-throughput  ...  Using the best identified machine learning method (Bayesian ridge regression), the model based on Sentinel-2-derived fAPAR was comparable with those based on handheld FieldScout-derived NDVI and CI, and  ... 
doi:10.1371/journal.pone.0249136 pmid:33765103 fatcat:jv5q3osttfamxf3zet5du62uee

Deep Learning for Feature-Level Data Fusion: Higher Resolution Reconstruction of Historical Landsat Archive

Bin Chen, Jing Li, Yufang Jin
2021 Remote Sensing  
Here we developed a feature-level data fusion framework using a generative adversarial network (GAN), a deep learning technique, to leverage the overlapping Landsat and Sentinel-2 observations during 2016  ...  This will enhance Landsat and Sentinel-2 data science, facilitate higher resolution land cover and land use monitoring, and global change research.  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13020167 fatcat:njqkjmsfefhp3f5rpmtu2hhkua

Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques

Ahmed Kayad, Marco Sozzi, Simone Gatto, Francesco Marinello, Francesco Pirotti
2019 Remote Sensing  
This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within  ...  VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations.  ...  Applying ML algorithms on crop yield estimation from RS data is a flexible approach and capable of processing a large number of inputs with the increase in data volumes due to higher resolution and the  ... 
doi:10.3390/rs11232873 fatcat:ctx7at63rjaktbun457orfsume

Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture

Bing Lu, Phuong D. Dao, Jiangui Liu, Yuhong He, Jiali Shang
2020 Remote Sensing  
Remote sensing is a useful tool for monitoring spatio-temporal variations of crop morphological and physiological status and supporting practices in precision farming.  ...  In comparison with multispectral imaging, hyperspectral imaging is a more advanced technique that is capable of acquiring a detailed spectral response of target features.  ...  Evaluating Empirical Regression, Machine Learning, and Radiative Transfer Modelling for Estimating Vegetation Chlorophyll Content Using Bi-Seasonal Hyperspectral Images.  ... 
doi:10.3390/rs12162659 fatcat:bfoe3xuja5b27b7wuu5ak7tdpq

Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China

Yang Chen, Lixia Ma, Dongsheng Yu, Kaiyue Feng, Xin Wang, Jie Song
2021 Remote Sensing  
Machine learning (ML) methods offer promising ways of generating spatially explicit LAI data covering large regions based on optical images.  ...  Here, forest LAI mapping was performed by integrating the MSI, SAR, and DEM data using a stacking learning (SL) approach that incorporates distinct predictions from a set of optimized individual ML algorithms  ...  accuracy comparison of the different machine learning models in Xingguo and Gandong.  ... 
doi:10.3390/rs14010148 fatcat:v6tyi3itmzg5xmjg3ihaa7jpqe
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