Regional Crop Characterization Using Multi-Temporal Optical and Synthetic Aperture Radar Earth Observations Data
Crop biophysical parameters, such as Leaf Area Index (LAI) and biomass, are essential for estimating crop productivity, yield modeling, and agronomic management. This study used several features extracted from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) and spectral vegetation indices e...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-03-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2021.2011180 |
| _version_ | 1797661122524872704 |
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| author | Hazhir Bahrami Saeid Homayouni Heather McNairn Mehdi Hosseini Masoud Mahdianpari |
| author_facet | Hazhir Bahrami Saeid Homayouni Heather McNairn Mehdi Hosseini Masoud Mahdianpari |
| author_sort | Hazhir Bahrami |
| collection | DOAJ |
| description | Crop biophysical parameters, such as Leaf Area Index (LAI) and biomass, are essential for estimating crop productivity, yield modeling, and agronomic management. This study used several features extracted from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) and spectral vegetation indices extracted from Sentinel-2 optical data to estimate crop LAI and wet and dry biomass. Various machine learning algorithms, including Random Forest Regression (RFR), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were trained and assessed for three major crops (wheat, soybeans and canola). ANN provided the best accuracy for all wheat parameters and soybean LAI and canola wet biomass and LAI. RFR led to higher accuracy for soybean dry and wet biomass. However, SVR could accurately estimate only canola dry biomass. All data were then pooled to investigate if a single algorithm could estimate biophysical parameters for all crops. The RFR model accurately estimated wet and dry biomass and LAI across all crop types in this scenario. This generic model is fast and accurate and can be easily applied for crop mapping and monitoring over large geographies using cloud computing platforms, such as Google Earth Engine. |
| first_indexed | 2024-03-11T18:39:54Z |
| format | Article |
| id | doaj.art-83885dd6c6ee40b7906a19a62867e4fa |
| institution | Directory Open Access Journal |
| issn | 1712-7971 |
| language | English |
| last_indexed | 2024-03-11T18:39:54Z |
| publishDate | 2022-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj.art-83885dd6c6ee40b7906a19a62867e4fa2023-10-12T13:36:24ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712022-03-0148225827710.1080/07038992.2021.20111802011180Regional Crop Characterization Using Multi-Temporal Optical and Synthetic Aperture Radar Earth Observations DataHazhir Bahrami0Saeid Homayouni1Heather McNairn2Mehdi Hosseini3Masoud Mahdianpari4Centre Eau Terre Environnement, Institut National de la Recherche ScientifiqueCentre Eau Terre Environnement, Institut National de la Recherche ScientifiqueScience and Technology Branch, Agriculture and Agri-Food CanadaDepartment of Geographical Sciences, University of MarylandC-CORECrop biophysical parameters, such as Leaf Area Index (LAI) and biomass, are essential for estimating crop productivity, yield modeling, and agronomic management. This study used several features extracted from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) and spectral vegetation indices extracted from Sentinel-2 optical data to estimate crop LAI and wet and dry biomass. Various machine learning algorithms, including Random Forest Regression (RFR), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were trained and assessed for three major crops (wheat, soybeans and canola). ANN provided the best accuracy for all wheat parameters and soybean LAI and canola wet biomass and LAI. RFR led to higher accuracy for soybean dry and wet biomass. However, SVR could accurately estimate only canola dry biomass. All data were then pooled to investigate if a single algorithm could estimate biophysical parameters for all crops. The RFR model accurately estimated wet and dry biomass and LAI across all crop types in this scenario. This generic model is fast and accurate and can be easily applied for crop mapping and monitoring over large geographies using cloud computing platforms, such as Google Earth Engine.http://dx.doi.org/10.1080/07038992.2021.2011180 |
| spellingShingle | Hazhir Bahrami Saeid Homayouni Heather McNairn Mehdi Hosseini Masoud Mahdianpari Regional Crop Characterization Using Multi-Temporal Optical and Synthetic Aperture Radar Earth Observations Data Canadian Journal of Remote Sensing |
| title | Regional Crop Characterization Using Multi-Temporal Optical and Synthetic Aperture Radar Earth Observations Data |
| title_full | Regional Crop Characterization Using Multi-Temporal Optical and Synthetic Aperture Radar Earth Observations Data |
| title_fullStr | Regional Crop Characterization Using Multi-Temporal Optical and Synthetic Aperture Radar Earth Observations Data |
| title_full_unstemmed | Regional Crop Characterization Using Multi-Temporal Optical and Synthetic Aperture Radar Earth Observations Data |
| title_short | Regional Crop Characterization Using Multi-Temporal Optical and Synthetic Aperture Radar Earth Observations Data |
| title_sort | regional crop characterization using multi temporal optical and synthetic aperture radar earth observations data |
| url | http://dx.doi.org/10.1080/07038992.2021.2011180 |
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