Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results

Toan, Thuy Le; Ribbes, F.; Wang, Lifang; Floury, Nicolas; Ding, Kung‐Hau; Kong, Jin Au; Fujita, Masahiro; Kurosu, T.

doi:10.1109/36.551933

Cited by 506 publications

(265 citation statements)

References 11 publications

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“…Other studies have shown that SAR is sensitive to a number of features thought to be important for mosquito production, e.g. areas cultivated with rice (Kurosu et al 1995, Panigrahy et al 1997, Okamoto and Kawashima 1999, different cultural practices of rice cultivation (Chakraborty et al 1997), local variation in planting dates, and several agronomic parameters of the developing rice (Le Toan et al 1997,Liew et al 1998). In particular, SAR can detect differences in rice plant height and biomass due to growth or variety, factors known to affect larval densities through changes in light, temperature, mechanical obstruction, and nutritional state of the water (Chandler and Highton 1975,1976,Snow 1983).…”

Section: Introductionmentioning

confidence: 99%

Patterns of irrigated rice growth and malaria vector breeding in Mali using multi‐temporal ERS‐2 synthetic aperture radar

Diuk‐Wasser

Dolo

Bagayoko

et al. 2006

International Journal of Remote Sensing

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We explored the use of the European Remote Sensing Satellite 2 Synthetic Aperture Radar (ERS-2 SAR) to trace the development of rice plants in an irrigated area near Niono, Mali and relate that to the density of anopheline mosquitoes, especially An. gambiae. This is important because such mosquitoes are the major vectors of malaria in sub-Saharan Africa, and their development is often coupled to the cycle of rice development. We collected larval samples, mapped rice fields using GPS and recorded rice growth stages simultaneously with eight ERS-2 SAR acquisitions. We were able to discriminate among rice growth stages using ERS-2 SAR backscatter data, especially among the early stages of rice growth, which produce the largest numbers of larvae. We could also distinguish between basins that produced high and low numbers of anophelines within the stage of peak production. After the peak, larval numbers dropped as rice plants grew taller and thicker, reducing the amount of light reaching the water surface. ERS-2 SAR backscatter increased concomitantly. Our data support the belief that ERS-2 SAR data may be helpful for mapping the spatial patterns of rice growth, distinguishing different agricultural practices, and monitoring the abundance of vectors in nearby villages.

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Section: Introductionmentioning

confidence: 99%

Patterns of irrigated rice growth and malaria vector breeding in Mali using multi‐temporal ERS‐2 synthetic aperture radar

Diuk‐Wasser

Dolo

Bagayoko

et al. 2006

International Journal of Remote Sensing

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“…The annual variation of σ˝from rice is higher than any other agricultural crop, thereby making SAR sensors valuable remote sensing tools for rice-crop mapping and monitoring [19,22]. The theoretical background of the C-band microwave interaction with rice plants has been extensively described by Le Toan et al [21], who compared the visible backscatter changes in ERS-1 SAR images over two test sites in Japan and Indonesia with backscatter modelled by means of Monte Carlo simulation. They have shown that wave-plant-water interactions are the primary backscatter mechanisms in flooded rice fields, in addition to direct scattering.…”

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confidence: 99%

“…Microwave-based remote sensing techniques, on the other hand, have the advantage of being non-susceptible to cloud cover. Numerous studies have been conducted to assess the potential of active microwave sensors for backscatter characterization in Mekong River Basin [16] and using Synthetic Aperture Radar (SAR) sensors for rice mapping [17][18][19][20][21][22][23]. The first studies of rice mapping with spaceborne SAR sensors have been undertaken with the European Remote Sensing Satellite 1 (ERS-1), showing promising results [20][21][22][24][25][26].…”

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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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“…First, to perform an estimation of the phenological stage k, the transition matrix J k−1 should be computed from the state space model and also linearized in a local neighborhood as an EKF. The linearized transition at state k − 1 is obtained as the local ratio between joint samples in the model as expressed in (2). This expression allows us to compute the coefficients of the transition matrix relating the model velocity increment between sample models (k and k − 1) with the state variable variation in the vicinity of state k − 1.…”

Section: Phenology Estimation a Kalman Filter Approachmentioning

confidence: 99%

“…In fact, rice crop monitoring has been a very active subject in remote sensing [1], [2], [3]. As input data we have employed in this work dual polarization synthetic aperture radar (SAR) imagery acquired by the German TerraSAR-X sensor at X-band, which provides a short revisit time (11 days) as required by such an application.…”

Section: Introductionmentioning

confidence: 99%

Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy

Vicente-Guijalba

Martínez-Marín

López-Sánchez

2014

IEEE Geosci. Remote Sensing Lett.

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Abstract-In this work a new approach for crop phenology estimation with remote sensing is presented. The proposed methodology is aimed to exploit tools from a dynamical system context. From a temporal sequence of images, a geometrical model is derived which allows us to translate this temporal domain into the estimation problem. The evolution model in state space is obtained through a dimensional reduction by a principal component analysis, defining the state variables, of the observations. Then, estimation is achieved combining the generated model with actual samples in an optimal way using a Kalman filter. As a proof-of-concept, an example with results obtained with this approach over rice fields by exploiting stacks of TerraSAR-X dual polarization images is shown.

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Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results

Cited by 506 publications

References 11 publications

Patterns of irrigated rice growth and malaria vector breeding in Mali using multi‐temporal ERS‐2 synthetic aperture radar

Patterns of irrigated rice growth and malaria vector breeding in Mali using multi‐temporal ERS‐2 synthetic aperture radar

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

Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy

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