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

Yonezawa, Chinatsu; Negishi, Masahiro; Azuma, Kenta; Ishitsuka, Naoki; Ogawa, Shigeo; Saito, Genya

doi:10.1080/01431161.2012.665194

Cited by 46 publications

(14 citation statements)

References 22 publications

(23 reference statements)

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“…Studies by Chen et al [48], Bouvet et al [49] and Lam-Dao et al [17] employed Envisat ASAR data to show that the ratio between HH and VV polarization on multi-temporal datasets can be used to classify rice areas with higher accuracy and less temporal coverage compared to the first method. Polarimetric decomposition of fully polarimetric RADARSAT-2 acquisitions showed promising results regarding not only the binary rice/non-rice classification of images but also the detection of rice's growth stages [50][51][52]. Rice classification performance of TerraSAR-X (TSX) images over test sites in Spain and the theoretical models behind multi-polarization and X-band-based rice classification have been extensively described by Lopez-Sanchez et al [53][54][55] as well as for the Mekong Delta [56][57][58].…”

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confidence: 99%

Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data

et al. 2015

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Abstract:Rice is the most important food crop in Asia, and the timely mapping and monitoring of paddy rice fields subsequently emerged as an important task in the context of food security and modelling of greenhouse gas emissions. Rice growth has a distinct influence on Synthetic Aperture Radar (SAR) backscatter images, and time-series analysis of C-band images has been successfully employed to map rice fields. The poor data availability on regional scales is a major drawback of this method. We devised an approach to classify paddy rice with the use of all available Envisat ASAR WSM (Advanced Synthetic Aperture Radar Wide Swath Mode) data for our study area, the Mekong Delta in Vietnam. We used regression-based incidence angle normalization and temporal averaging to combine acquisitions from multiple tracks and years. A crop phenology-based classifier has been applied to this time series to detect single-, double-and triple-cropped rice areas (one to three harvests per year), as well as dates and lengths of growing seasons. Our classification has an overall accuracy of 85.3% and a kappa coefficient of 0.74 compared to a reference dataset and correlates highly with official rice area statistics at the provincial level (R² of 0.98). SAR-based time-series analysis allows accurate mapping and monitoring of rice areas even under adverse atmospheric conditions.

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confidence: 99%

Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data

et al. 2015

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“…Since SAR systems have ability to penetrate through clouds and can operate day or night, they are a valuable resource for agricultural monitoring. In particular, SAR data have been applied for agricultural mapping and monitoring (CEH, 2016;Yonezawa et al, 2012) and measurement of crop biophysical properties (Harrell et al, 1997;McNairn and Brisco, 2004;Vyas et al, 2003). Krieger and Moreira (2006) highlight the potential and challenges of space-borne bi-and multistatic SAR sensors and evaluated their potential for applications such as frequent monitoring, wide-swath imaging, scene classification, single pass cross-track interferometry and resolution enhancement.…”

Section: Radar Based Satellite Sensors For Agriculturementioning

confidence: 99%

Applications of satellite ‘hyper-sensing’ in Chinese agriculture: Challenges and opportunities

Onojeghuo

Blackburn

Huang

et al. 2018

International Journal of Applied Earth Observation and Geoinfor

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Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of 'hyper-sensing' (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite 'hyper-sensing' to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite 'hyper-sensing' in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and ongoing projects in China that are developing 'hyper-sensing' approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China.

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“…To investigate the relative contribution of the scattering component, we present the rate of each scattering mechanism on a triangle plot, which is a similar analysis to [33], shown in Figure 12a,b for the FCP and DCP modes respectively. Note that we exclude the helix scattering contribution from the FCP mode for comparison between both modes.…”

Section: Four-and Three-component Decompositionmentioning

confidence: 99%

Analysis of Dual- and Full-Circular Polarimetric SAR Modes for Rice Phenology Monitoring: An Experimental Investigation through Ground-Based Measurements

et al. 2017

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Circularly polarized synthetic aperture radar (CP-SAR) is known to be insensitive to polarization mismatch losses caused by the Faraday rotation effect and antenna misalignment. Additionally, the dual-circular polarimetric (DCP) mode has proven to have more polarimetric information than that of the corresponding mode of linear polarization, i.e., the dual-linear polarimetric (DLP) mode. Owing to these benefits, this paper investigates the feasibility of CP-SAR for rice monitoring. A ground-based CP-radar system was exploited, and C-band anechoic chamber data of a self-cultivated Japanese rice paddy were acquired from germination to ripening stages. Temporal variations of polarimetric observables derived from full-circular polarimetric (FCP) and DCP as well as synthetically generated DLP data are analyzed and assessed with regard to their effectiveness in phenology retrieval. Among different observations, the H/ᾱ plane and triangle plots obtained by three scattering components (surface, double-bounce, and volume scattering) for both the FCP and DCP modes are confirmed to have reasonable capability in discriminating the relevant intervals of rice growth.

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

Cited by 46 publications

References 22 publications

Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data

Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data

Applications of satellite ‘hyper-sensing’ in Chinese agriculture: Challenges and opportunities

Analysis of Dual- and Full-Circular Polarimetric SAR Modes for Rice Phenology Monitoring: An Experimental Investigation through Ground-Based Measurements

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