Crop phenology retrieval via polarimetric SAR decomposition and Random Forest algorithm

Knowledge of crop phenology assists in making agricultural decisions such as appropriate irrigation and fertilization applications in order to optimize crop yield. The objective of this study is to monitor crop phenology using Synthetic Aperture Radar (SAR) polarimetric decompositions and a random f...

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Vydáno v:Remote sensing of environment Ročník 231; s. 111234
Hlavní autoři: Wang, Hongquan, Magagi, Ramata, Goïta, Kalifa, Trudel, Melanie, McNairn, Heather, Powers, Jarrett
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier BV 15.09.2019
Témata:
Algorithms
Artificial neural networks
canola
Corn
Correlation coefficient
Correlation coefficients
Crop phenology
Crop yield
Crops
data collection
Decision trees
Decomposition
Fertilization
Irrigation
Manitoba
Mathematical models
Neural networks
Parameter identification
Parameter sensitivity
Phenology
polarimetry
Radar
Radar polarimetry
Radarsat
Regression analysis
remote sensing
Retrieval
satellites
Scattering
soil
Soil moisture
Soils
Soybeans
Support vector machines
Synthetic aperture radar
vegetation
Wheat
Manitoba
ISSN:0034-4257, 1879-0704
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Shrnutí:Knowledge of crop phenology assists in making agricultural decisions such as appropriate irrigation and fertilization applications in order to optimize crop yield. The objective of this study is to monitor crop phenology using Synthetic Aperture Radar (SAR) polarimetric decompositions and a random forest algorithm applied to a multi-temporal RADARSAT-2 dataset, acquired during the Soil Moisture Active Passive (SMAP) Validation Experiment 2016 in Manitoba (SMAPVEX16-MB). The model-based and eigen-based polarimetric parameters are used to separate the vegetation and soil scattering contributions in the total radar signal. As the crop morphological shape and structure vary with phenological growth, our study assumes that the polarimetric parameters related to the volume scattering mechanism have the potential to track the crop phenology. The sensitivity of the polarimetric parameters to the ground identified crop phenology is analyzed for different crop types. For canola, a single polarimetric parameter is sufficient to characterize the crop phenology, due to the high volume scattering power and large temporal dynamic. For corn, soybean and wheat, combinations of multiple polarimetric parameters are required. For each crop type, the Random Forest algorithm trained using 60% of the data is used to retrieve the crop phenology. Performances are compared to Artificial Neural Network, Support Vector Machine Regression and k-Nearest neighborhood algorithms. The Random Forest algorithm provides the best phenology retrieval with significant (p-value < 0.01) spearman correlation coefficients (between the retrieved and ground identified phenology) of 0.93, 0.90, 0.85 and 0.91 for canola, corn, soybean and wheat, respectively. While a single polarimetric parameter demonstrates limited sensitivity to corn phenology, the retrieved phenology from the Random Forest algorithm using multiple polarimetric parameters agrees well with the ground measurements. Furthermore, the importance of different polarimetric parameters for phenology retrieval using the Random Forest algorithm is quantified for different crop types. These findings will be of interest in developing future analytical retrieval models.
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ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2019.111234