Naoki Ishitsuka

Crop discrimination with multitemporal SPOT/HRV data in the Saga Plains, Japan

Nine scenes of SPOT/HRV data obtained in eight different months in 1997 were evaluated for crop discrimination in the Saga Plains, Japan. All images were atmospherically corrected with the 6S code. Annual Normalized Difference Vegetation Index (NDVI) profiles were generated to characterize seasonal trends in six cropping systems (rice, rice-winter cereal, soybean, soybean-winter cereal, lotus, and rush). The dataset of this study showed the unique temporal change patterns of NDVI for each cropping system. Separability analyses determined optimal scene combinations for the highest accuracy in classifying the cropping systems. The scene combinations for the accurate classification of cropping systems were obtained from three separability measurements (Euclidean spectral distance, divergence, and Jeffries-Matsushita distance). Kappa statistics were applied to evaluate the classification accuracies. The four-scene combination that was derived from April, June, July and September classified the cropping systems almost as well as those combinations including more scenes. A colour composition technique applied to the three-scene combination that showed the highest separability also discriminated each cropping system. Based on these results, we can request observations during specific time intervals considering local crop calendars and environmental conditions.

Growth monitoring and classification of rice fields using multitemporal RADARSAT-2 full-polarimetric data

Spaceborne synthetic aperture radar (SAR) can be used for agricultural monitoring. In this study, three single-polarimetric and four full-polarimetric observation data sets were analysed. A rice paddy field in northern Japan was used as the study site; the data for this site were obtained using RADARSAT-2, which carries a full-polarimetric C-band SAR. Soybean and grass fields were also present within the paddy fields. The temporal change in the backscattering coefficient of the rice paddy fields for the single-polarization data agreed with the temporal change obtained for a rice growth model based on radiative transfer theory. A three-component decomposition approach was applied to the full-polarimetric data. With each rice growth stage, the volume scattering component ratio increased, whereas the surface scattering component ratio generally decreased. The soybean and grass fields showed a smaller double-bounce scattering component than the rice fields for all the acquired data. The results of this study show that multitemporal observation by full-polarimetric SAR has great potential to be utilized for estimating rice-planted areas and monitoring rice growth.

On the Bragg Scattering Observed in L-Band Synthetic Aperture Radar Images of Flooded Rice Fields

This article presents the analysis of the Bragg scattering phenomenon which has been observed in the images of machine-planted rice paddies acquired by the JERS-1 L-band synthetic aperture radar (SAR). The simultaneous measurements of rice plants were made at the SAR data acquisition times. Large differences of 20-25 dB in image intensity between the transplanting and ripening stages are found to be dependent on the planting direction and bunch separation. This selective image enhancement is a result of the Bragg resonance backscatter due to the double-bounce of incident L-band microwave between the flooded water surface and periodically planted bunches of rice plants. Support for the idea of double-bounce scattering is provided by the decomposition analysis of L-band and X-band polarimetric Pi-SAR data; and a simple numerical simulation based on the physical optics model shows fairly good agreement with the JERS-1 SAR data. The results presented in this paper is mainly of academic interest, but a suggestion can be made on the selection of suitable microwave band for monitoring rice fields.

Single‐look classification accuracy for polarimetric SAR

Polarimetric Synthetic Aperture Radar (SAR) systems such as ALOS‐PALSAR and Radarsat‐2 can operate in many different modes. The use of additional polarizations may require additional time and operating power and it is important to justify this by increased classification accuracy. A fully polarimetric, dual frequency AIRSAR scene from a rice‐growing area in Japan was classified by a maximum likelihood method based on the Wishart distribution. It is shown how the measured covariance matrices determine the separation accuracy between two classes. Closed form expressions are then given for the expected single‐look accuracy of the maximum likelihood classifier as a function of the class covariance matrices. This can be used to quickly compare the high spatial resolution classification performance of different polarimetric systems to decide upon a particular operating mode.

Fully polarimetric classification accuracy

Assuming that polarimetric data is entirely described by the underlying complex covariance matrix, this paper gives expressions for the maximum likelihood classification accuracy, and applies these to real data. This assumes negligible interclass environmental variation and a homogeneous image structure which is overly simplistic. By including a model of environmental variation, more realistic results may be possible.

Automatic rice-crop mapping using maximum likelihood SAR segmentation and Gaussian expectation maximisation

Accurate, large scale crop monitoring requires the use of weather-independent sensors such as SAR. Rice is the staple food in many parts of Asia and knowledge of rice growth provides valuable economic and environmental information. Extensive pixel accuracy ground truth from an area of Kojima, Japan is used to compare the accuracy of unsupervised mapping algorithms. An acceptable classification is achieved by Gaussian expectation maximisation applied to existing maximum-likelihood (ML) based segmentation methods. Using a Bayesian extension of the ML segmentation scheme gives a dramatic improvement in accuracy which results in a pixel classification accuracy of 86% (68% kappa) and an essentially exact estimate of rice coverage within a 1000 hectare area.

Application of ALOS data for agriculture monitoring on East Asia

ALOS has three main sensors: PALSAR, AVNIR2, and PRISM. We examined the utilization of PALSAR and AVNIR2 for agriculture monitoring. L-band and multi-polarization SAR characterize PALSAR. We have investigated the capability of SAR applications for agriculture monitoring using RADARSAT data, and AVNIR was carried on ADEOS that operated from August 1996 to June 1997. There were very little AVNIR data for agriculture monitoring because the acquisitions of data were only during the wintertime in the northern hemisphere. We alternatively examined AVNIR abilities for frequent data acquisition with pointing function using SPOT/HRV data.