Combination of Linear Regression Lines to Understand the Response of Sentinel-1 Dual Polarization SAR Data with Crop Phenology - Case Study in Miyazaki, Japan

This study investigated the relationship between backscattering coefficients of a synthetic aperture radar (SAR) and the four biophysical parameters of rice crops—plant height, green vegetation cover, leaf area index, and total dry biomass. A paddy rice field in Miyazaki, Japan was studied from April to July of 2018, which is the rice cultivation season. The SAR backscattering coefficients were provided by Sentinel-1 satellite. Backscattering coefficients of two polarization settings—VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving)—were investigated. Plant height, green vegetation cover, leaf area index, and total dry biomass were measured at ground level, on the same dates as satellite image acquisition. Polynomial regression lines indicated relationships between backscattering coefficients and plant biophysical parameters of the rice crop. The biophysical parameters had stronger relationship to VH than to VV polarization. A disadvantage of adopting polynomial regression equations is that the equation can have two biophysical parameter solutions for a particular backscattering coefficient value, which prevents simple conversion from backscattering coefficients to plant biophysical parameters. To overcome this disadvantage, the relationships between backscattering coefficients and the plant biophysical parameters were expressed using a combination of two linear regression lines, one line for the first sub-period and the other for the second sub-period during the entire cultivation period. Following this approach, all four plant biophysical parameters were accurately estimated from the SAR backscattering coefficient, especially with VH polarization, from the date of transplanting to about two months, until the mid-reproductive stage. However, backscattering coefficients saturate after two months from the transplanting, and became insensitive to the further developments in plant biophysical parameters. This research indicates that SAR can effectively and accurately monitor rice crop biophysical parameters, but only up to the mid reproductive stage.

[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]  M. Chakraborty,et al.  Rice crop parameter retrieval using multi-temporal, multi-incidence angle Radarsat SAR data , 2005 .

[3]  W. Wagner,et al.  Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data , 2016 .

[4]  Masaharu Fujita,et al.  Monitoring of rice crop growth from space using the ERS-1 C-band SAR , 1995, IEEE Trans. Geosci. Remote. Sens..

[5]  Jiaguo Qi,et al.  Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2 , 2017, Remote. Sens..

[6]  Ping Chen,et al.  Application of multitemporal ERS-2 synthetic aperture radar in delineating rice cropping systems in the Mekong River Delta, Vietnam , 1998, IEEE Trans. Geosci. Remote. Sens..

[7]  David Small,et al.  Improving PolSAR Land Cover Classification With Radiometric Correction of the Coherency Matrix , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  T. Ochsner,et al.  Canopeo: A Powerful New Tool for Measuring Fractional Green Canopy Cover , 2015, Agronomy Journal.

[9]  David Small,et al.  Flattening Gamma: Radiometric Terrain Correction for SAR Imagery , 2011, IEEE Transactions on Geoscience and Remote Sensing.

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

[11]  I. Hajnsek,et al.  A tutorial on synthetic aperture radar , 2013, IEEE Geoscience and Remote Sensing Magazine.

[12]  A. Monti Guarnieri,et al.  Sentinel 1 SAR interferometry applications: The outlook for sub millimeter measurements , 2012 .

[13]  Jie Wang,et al.  Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[14]  B. Brisco,et al.  Precision Agriculture and the Role of Remote Sensing: A Review , 1998 .

[15]  Jingfeng Huang,et al.  Evaluating the potential of temporal Sentinel-1A data for paddy rice discrimination at local scales , 2017 .

[16]  A. Lopes,et al.  Multitemporal and dual-polarization observations of agricultural vegetation covers by X-band SAR images , 1989, IEEE Transactions on Geoscience and Remote Sensing.

[17]  H. Mcnairn,et al.  A neural network integrated approach for rice crop monitoring , 2006 .

[18]  Thuy Le Toan,et al.  Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results , 1997, IEEE Trans. Geosci. Remote. Sens..

[19]  Luca Gatti,et al.  Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project , 2014, Remote. Sens..

[20]  Nathan Torbick,et al.  Mapping agricultural wetlands in the Sacramento Valley, USA with satellite remote sensing , 2015, Wetlands Ecology and Management.

[21]  B. Brisco,et al.  Rice monitoring and production estimation using multitemporal RADARSAT , 2001 .

[22]  Hui Lin,et al.  Application of ENVISAT ASAR Data in Mapping Rice Crop Growth in Southern China , 2007, IEEE Geoscience and Remote Sensing Letters.