Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets

Accurate estimation and monitoring of rice phenology is necessary for the management and yield prediction of rice. The radar backscattering coefficient, one of the most direct and accessible parameters has been proved to be capable of retrieving rice growth parameters. This paper aims to investigate the possibility of monitoring the rice phenology (i.e., transplanting, vegetative, reproductive, and maturity) using the backscattering coefficients or their simple combinations of multi-temporal RADARSAT-2 datasets only. Four RADARSAT-2 datasets were analyzed at 30 sample plots in Meishan City, Sichuan Province, China. By exploiting the relationships of the backscattering coefficients and their combinations versus the phenology of rice, HH/VV, VV/VH, and HH/VH ratios were found to have the greatest potential for phenology monitoring. A decision tree classifier was applied to distinguish the four phenological phases, and the classifier was effective. The validation of the classifier indicated an overall accuracy level of 86.2%. Most of the errors occurred in the vegetative and reproductive phases. The corresponding errors were 21.4% and 16.7%, respectively.

[1]  Alexandre Bouvet,et al.  Monitoring of the Rice Cropping System in the Mekong Delta Using ENVISAT/ASAR Dual Polarization Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Li Wang,et al.  Mapping Early, Middle and Late Rice Extent Using Sentinel-1A and Landsat-8 Data in the Poyang Lake Plain, China , 2018, Sensors.

[3]  Irena Hajnsek,et al.  Estimation of Rice Crop Height From X- and C-Band PolSAR by Metamodel-Based Optimization , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Angela Lausch,et al.  Deriving phenology of barley with imaging hyperspectral remote sensing , 2015 .

[5]  Long Liu,et al.  Rice growth monitoring using simulated compact polarimetric C band SAR , 2014 .

[6]  Cuizhen Wang,et al.  Capability of C-band backscattering coefficients from high-resolution satellite SAR sensors to assess biophysical variables in paddy rice , 2014 .

[7]  R. Congalton.  Accuracy assessment and validation of remotely sensed and other spatial information , 2001 .

[8]  Xiao-dong Song,et al.  Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images , 2015, Journal of Zhejiang University-SCIENCE B.

[9]  H. Liu,et al.  Estimation of rice biophysical parameters using multitemporal RADARSAT-2 images , 2016 .

[10]  Xiaoyan Zhao,et al.  Interpreting RADARSAT-2 Quad-Polarization SAR Signatures From Rice Paddy Based on Experiments , 2012, IEEE Geoscience and Remote Sensing Letters.

[11]  Irena Hajnsek,et al.  Rice Growth Monitoring by Means of X-Band Co-polar SAR: Feature Clustering and BBCH Scale , 2015, IEEE Geoscience and Remote Sensing Letters.

[12]  Seiho Uratsuka,et al.  Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables , 2002 .

[13]  Fernando Vicente-Guijalba,et al.  Polarimetric Response of Rice Fields at C-Band: Analysis and Phenology Retrieval , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Long Liu,et al.  An improved scheme for rice phenology estimation based on time-series multispectral HJ-1A/B and polarimetric RADARSAT-2 data , 2017 .

[15]  Yue Zhang,et al.  Land surface phenology of Northeast China during 2000–2015: temporal changes and relationships with climate changes , 2017, Environmental Monitoring and Assessment.

[16]  J.,et al.  A decimal code for the growth stages of cereals , 2022 .

[17]  Dailiang Peng,et al.  Detection and estimation of mixed paddy rice cropping patterns with MODIS data , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[18]  Yoshio Inoue,et al.  Relationship between X-band backscattering coefficients from high-resolution satellite SAR and biophysical variables in paddy rice , 2013 .

[19]  Heather McNairn,et al.  Using RADARSAT-2 and TerraSAR-X satellite data for the identification of canola crop phenology , 2016, Remote Sensing.

[20]  J. Kovacs,et al.  Tracking crop phenological development using multi-temporal polarimetric Radarsat-2 data , 2017, Remote Sensing of Environment.

[21]  Yuan Zhang,et al.  Retrieving canopy height and density of paddy rice from Radarsat-2 images with a canopy scattering model , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[22]  B. Bouman,et al.  Crop parameter estimation from ground-based x-band (3-cm wave) radar backscattering data , 1991 .

[23]  Irena Hajnsek,et al.  Retrieval of agricultural crop height from space: A comparison of SAR techniques , 2016 .

[24]  Cheng-Ru Chen,et al.  Monitoring of rice cropping intensity in the upper Mekong Delta, Vietnam using time-series MODIS data , 2012 .

[25]  Esra Erten,et al.  Paddy-Rice Phenology Classification Based on Machine-Learning Methods Using Multitemporal Co-Polar X-Band SAR Images , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Meichen Fu,et al.  Detection and Modeling of Vegetation Phenology Spatiotemporal Characteristics in the Middle Part of the Huai River Region in China , 2015 .

[27]  Ferdinando Nunziata,et al.  A study of the use of COSMO-SkyMed SAR PingPong polarimetric mode for rice growth monitoring , 2016 .

[28]  Jin Au Kong,et al.  Electromagnetic Scattering Model for Rice Canopy Based on Monte Carlo Simulation , 2005 .

[29]  J. A. Kong,et al.  ELECTROMAGETIC SCATTERING MODEL FOR RICE CANOPY BASED ON MONTE CARLO SIMULATION , 2005 .

[30]  Cristian Rossi,et al.  Paddy-Rice Monitoring Using TanDEM-X , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Eric Pottier,et al.  An entropy based classification scheme for land applications of polarimetric SAR , 1997, IEEE Trans. Geosci. Remote. Sens..

[32]  Tomás Martínez-Marín,et al.  Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy , 2014, IEEE Geoscience and Remote Sensing Letters.

[33]  Christoph Hütt,et al.  Rice monitoring with multi-temporal and dual-polarimetric TerraSAR-X data , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[34]  D. Roy,et al.  Strategies for the fusion of satellite fire radiative power with burned area data for fire radiative energy derivation , 2009 .

[35]  Paul M. Mather,et al.  An assessment of the effectiveness of decision tree methods for land cover classification , 2003 .

[36]  Tomás Martínez-Marín,et al.  Estimation of Key Dates and Stages in Rice Crops Using Dual-Polarization SAR Time Series and a Particle Filtering Approach , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[37]  Fawwaz T. Ulaby,et al.  Relating the microwave backscattering coefficient to leaf area index , 1984 .

[38]  Francis Canisius,et al.  ALOS PALSAR L-band polarimetric SAR data and in situ measurements for leaf area index assessment , 2012 .

[39]  T. Motohka,et al.  Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset , 2009 .

[40]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[41]  Hui Lin,et al.  Rice heading date retrieval based on multi-temporal MODIS data and polynomial fitting , 2012 .

[42]  Bo Zhang,et al.  Rice Crop Monitoring in South China With RADARSAT-2 Quad-Polarization SAR Data , 2011, IEEE Geoscience and Remote Sensing Letters.

[43]  Peter M. Atkinson,et al.  The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India , 2010 .

[44]  L. Shihua,et al.  Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province, China. , 2014 .

[45]  Juan M. Lopez-Sanchez,et al.  Rice Phenology Monitoring by Means of SAR Polarimetry at X-Band , 2012, IEEE Transactions on Geoscience and Remote Sensing.