Estimating rice growth parameters using X-band scatterometer data

Kim, YiHyun; Hong, Su Young; Choe, Eunyoung; Lee, Hoonyol

doi:10.1109/igarss.2010.5654066

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…Second, the correspondence between the two HH radar backscatter signatures is evident, confirming the validity of the proposed approach. Third, and confirming the studies of [12,16], the HV polarization contributes in the discrimination of rice from other land covers. Typically, the HH/VV ratio is preferred because it shows a variation up to 7 dB from the beginning of the season to the plant maturity phase [11].…”

Section: Multi-year Rice Extent From the Envisat Ws Archivesupporting

confidence: 80%

“…SAR systems have a proven ability to detect irrigated and lowland rice through the unique backscatter temporal signature. In the past three decades, a significant number of publications have been dedicated to the rice signature and to its detection and monitoring [10][11][12][13][14][15][16][17]. In summary, these studies have shown that:…”

Section: Multi-temporal Sar and Optical Data For Rice Mapping And Monmentioning

confidence: 99%

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Combining Moderate-Resolution Time-Series RS Data from SAR and Optical Sources for Rice Crop Characterisation: Examples from Bangladesh

Nelson¹,

Boschetti²,

Manfron³

et al. 2014

Land Applications of Radar Remote Sensing

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Of the thousands of edible plants, just three-rice, wheat, and maize-provide 60% of the global population's food energy intake, and the top 15 crops amount to 90% [1]. Seasonal or monthly estimates of production and availability of these staples form a part of many agricultural bulletins and agricultural outlook reports. These reports are used for decision-making and policies on imports, exports, subsidies, and investments, which, in turn, affect prices. Food security is fundamentally about availability and price; such reports, and the responses to these reports, affect both. International events, such as the food price crisis of 2008, are the unintended outcome of national and international policy decisions that can detrimentally affect

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Section: Multi-year Rice Extent From the Envisat Ws Archivesupporting

confidence: 80%

Section: Multi-temporal Sar and Optical Data For Rice Mapping And Monmentioning

confidence: 99%

Combining Moderate-Resolution Time-Series RS Data from SAR and Optical Sources for Rice Crop Characterisation: Examples from Bangladesh

Nelson¹,

Boschetti²,

Manfron³

et al. 2014

Land Applications of Radar Remote Sensing

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show abstract

“…In the past three decades, a significant number of publications have been dedicated to better understanding this relationship and applying it to rice detection and rice monitoring [14][15][16][17][18][19][20][21][22][23]. In summary, these studies have shown that: − The frequency ratios for HH and VV (C-VV/L-VV and C-HH/L-HH) are significantly lower in the latter part of the rice season when thick vegetation canopy hampers wave penetration [18].…”

Section: A Summary Of Sar Research and Applications For Rice Mappingmentioning

confidence: 99%

Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project

et al. 2014

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Rice is the most important food security crop in Asia. Information on its seasonal extent forms part of the national accounting of many Asian countries. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland rice, especially in tropical and subtropical regions, where pervasive cloud cover in the rainy seasons precludes the use of optical imagery. Here, we present a simple, robust, rule-based classification for mapping rice area with regularly acquired, multi-temporal, X-band, HH-polarized SAR imagery and site-specific parameters for classification. The rules for rice detection are based on the well-studied temporal signature of rice from SAR backscatter and its relationship with crop stages. We also present a procedure for estimating the parameters based on "temporal feature descriptors" that concisely characterize the key information in the rice signatures in monitored field locations within each site. We demonstrate the robustness of the approach on a very large dataset. A total of 127 images across 13 footprints in six countries in Asia were obtained between October 2012, and April 2014, covering 4.78 m ha. More than 1900 in-season site visits were conducted across 228 monitoring locations in the footprints for classification purposes, and more than 1300 field observations were made for accuracy assessment. Some 1.6 m ha of rice were mapped with classification accuracies from 85% to 95% based on the parameters that were closely related to the observed temporal feature descriptors derived for Remote Sens. 2014, 6 10775 each site. The 13 sites capture much of the diversity in water management, crop establishment and maturity in South and Southeast Asia. The study demonstrates the feasibility of rice detection at the national scale using multi-temporal SAR imagery with robust classification methods and parameters that are based on the knowledge of the temporal dynamics of the rice crop. We highlight the need for the development of an open-access library of temporal signatures, further investigation into temporal feature descriptors and better ancillary data to reduce the risk of misclassification with surfaces that have temporal backscatter dynamics similar to those of rice. We conclude with observations on the need to define appropriate SAR acquisition plans to support policies and decisions related to food security.

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“…In the past years, a large number of publications have been dedicated to better understanding this relationship and applying it to rice detection and rice monitoring (Le Toan et al, 1997, Inoue et al, 2002, Suga and Konishi, 2008, and Bouvet et al, 2009). In summary, these studies have shown that lower frequencies (L-and Cband) penetrate deeper into the rice plant than higher frequencies, while only higher frequencies (X-band) interact with grain water content and grain weight sufficiently to show a dual-peak signal in σ° during the rice season (Inoue et al, 2002, Suga and Konishi, 2008, Oh et al, 2009and Kim et al, 2009. Further, short wavelengths (X-, Ka-, Ku-band), especially at large incident angles, are sensitive enough to detect even very small rice seedlings just after transplanting.…”

Section: Background Of Sar Research and Applications For Rice Mappingmentioning

confidence: 99%

“…Further, short wavelengths (X-, Ka-, Ku-band), especially at large incident angles, are sensitive enough to detect even very small rice seedlings just after transplanting. The correlation between σ° and rice biophysical parameters shows that lower frequencies are more closely related to total fresh weight, leaf area index (LAI) and plant height than other parameters (Inoue et al, 2009 andKim et al, 2009).…”

Section: Background Of Sar Research and Applications For Rice Mappingmentioning

confidence: 99%

Rice Crop Monitoring and Yield Estimation Through Cosmo Skymed and TerraSAR-X: A SAR-Based Experience in India

Pazhanivelan

Kannan

Mary

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Rice is the most important cereal crop governing food security in Asia. Reliable and regular information on the area under rice production is the basis of policy decisions related to imports, exports and prices which directly affect food security. Recent and planned launches of SAR sensors coupled with automated processing can provide sustainable solutions to the challenges on mapping and monitoring rice systems. High resolution (3m) Synthetic Aperture Radar (SAR) imageries were used to map and monitor rice growing areas in selected three sites in TamilNadu, India to determine rice cropping extent, track rice growth and estimate yields. A simple, robust, rule-based classification for mapping rice area with multi-temporal, X-band, HH polarized SAR imagery from COSMO Skymed and TerraSAR X and site specific parameters were used. The robustness of the approach is demonstrated on a very large dataset involving 30 images across 3 footprints obtained during 2013-14. A total of 318 in-season site visits were conducted across 60 monitoring locations for rice classification and 432 field observations were made for accuracy assessment. Rice area and Start of Season (SoS) maps were generated with classification accuracies ranging from 87-92 per cent. Using ORYZA2000, a weather driven process based crop growth simulation model; yield estimates were made with the inclusion of rice crop parameters derived from the remote sensing products viz., seasonal rice area, SoS and backscatter time series. Yield Simulation accuracy levels of 87 per cent at district level and 85-96 per cent at block level demonstrated the suitability of remote sensing products for policy decisions ensuring food security and reducing vulnerability of farmers in India.

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Estimating rice growth parameters using X-band scatterometer data

Cited by 4 publications

References 4 publications

Combining Moderate-Resolution Time-Series RS Data from SAR and Optical Sources for Rice Crop Characterisation: Examples from Bangladesh

Combining Moderate-Resolution Time-Series RS Data from SAR and Optical Sources for Rice Crop Characterisation: Examples from Bangladesh

Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project

Rice Crop Monitoring and Yield Estimation Through Cosmo Skymed and TerraSAR-X: A SAR-Based Experience in India

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