Advancements in Estimating Crop Growth Stages Using Radarsat-2 and Terrasar-x Polarimetric Data

This paper uses RADARSAT-2 quad Polarimetric Synthetic Aperture Radar (PolSAR) and TerraSAR-X dual polarimetric SAR data to monitor agriculture crop growth stages. Two RADARSAT-2 Fine Quad Wide (FQW) beam modes FQ2W and FQ10W, each with 5 sets of data and 13 sets of Stripmap TerraSAR-X data were used in the study. Both RADARSAT-2 POLSAR data and TerraSARX data were acquired in summer 2012 outside Winnipeg, Manitoba, Canada. The study was carried out to two crop types: canola and wheat, each contains 5 regions of interest from ground truth crop classification map in the image scene. Polarimetric features such as differential reflectivity bands ratio, entropy, anisotropy, alpha angle, lambda, scattering diversity and polarization index were evaluated for two crop types. The results from both RADARSAT-2 and TerraSAR-X data were compared and they demonstrated clear relationships between crop growth stages and polarimetric parameters. It is observed that entropy, lambda and differential reflectivity from both data have similar responses to crop growth stages in their common coverage period. The results were also validated using ground truth information. * Corresponding author. 1. INTRUDUCTION Using multiple satellites such as RADARSAT-2 and TerraSARX POLSAR data to accurately estimate crop growth stages is an important agriculture application. This can enable growers to proactively prevent disease, increase yields, and enhance longterm planning. Current agricultural practices rely heavily on time-consuming site visits, which can delay responses to potential threats and limit the extent of data that can be acquired for large tracts of land. There is high potential to maximize the use of satellite data to acquire comprehensive crop information quickly, automatically, and with high accuracy, to better manage large areas. Moreover, as many crops are susceptible to diseases and infestations during the narrow windows of specific growth stages, (e.g., canola’s vulnerable flowering period, which lasts only 14 to 21 days on average), the estimation of crop growth stages, phenology and location is vital in order to identify which fields are at risk and when. Currently, PolSAR data utilization for crop management applications has limited researches on crop growth stage estimation [Cloutis2010, McNairn2004, McNairn2014]. Agriculture and Agri-Food Canada and other organizations have conducted preliminary research in this area using RADARSAT-2 and TerraSAR-X data to examine crop conditions in the Soil Moisture Active Passive mission Validation Experiment 2012 (SMAPVEX12) campaign in summer 2012, Manitoba, Canada. The preliminary study in this paper is to demonstrate that by incorporating multi-sensor information, such as RADARSAT-2 and TERRASAR-X data, and considering many more polarimetric parameters, accurate crop stage classification and identification can be achieved. 2. METHODOLOGY At different crop growth stages, crop characteristics will change. In general, the agriculture industry defines ten distinct stages of crop growth [Earth2012]. PolSAR sensors will capture the vegetation difference in Cand Xbands. Various polarization parameters will be calculated for both RADARSAT-2 and TerraSAR-X data in this paper to find out the their responses to the crop stage changing. The purpose of this work is to find possible polarimetric parameters of crop growth stages for different crops. The technology provided in this work will provide possible estimation for different crop growth stages. The RADARSAT-2 fully Polarimetric SAR (PolSAR) system measures for each resolution element in the scene, producing a scattering matrix:          VV VH HV HH S S S S S The scattering matrix elements in Pauli basis can be arranged into a measurement vector, which is called the scattering vector   VH VV HH VV HH S S S S S k 2 , , 2 1     The equation is under the reciprocal assumption, with the three elements referred to as the Pauli components of the signal. Various polarimetric parameters can be obtained based on these three complex elements SHH, SVV and SHV:  Differential Reflectivity Ratio of scattering matrices The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W3, 2015 36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany This contribution has been peer-reviewed. doi:10.5194/isprsarchives-XL-7-W3-1227-2015 1227 For quad-polarized data, the three differential reflectivity ratios are