Relationship between X-band backscattering coefficients from high-resolution satellite SAR and biophysical variables in paddy rice

Inoue, Yoshio; Sakaiya, Eiji

doi:10.1080/2150704x.2012.725482

Cited by 49 publications

(30 citation statements)

References 16 publications

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“…High-frequency bands are sensitive to early rice growth stages (with LAI < 1.5) within one month after transplanting and lose sensitivity after that [7]. Backscatter of rice from X-band saturates at an earlier growth stage [34,71]. This research has identified the peak to occur within the tillering to stem elongation stage.…”

Section: Rice Sar Temporal Backscatter Signature and Sar-based Rice Cmentioning

confidence: 95%

Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR

et al. 2014

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Different rice crop information can be derived from different remote sensing sources to provide information for decision making and policies related to agricultural production and food security. The objective of this study is to generate complementary and comprehensive rice crop information from hypertemporal optical and multitemporal high-resolution SAR imagery. We demonstrate the use of MODIS data for rice-based system characterization and X-band SAR data from TerraSAR-X and CosmoSkyMed for the identification and detailed mapping of rice areas and flooding/transplanting dates. MODIS was classified using ISODATA to generate cropping calendar, cropping intensity, cropping pattern and rice ecosystem information. Season and location specific thresholds from field observations were used to generate detailed maps of rice areas and flooding/transplanting dates from the SAR data. Error matrices were used for the accuracy assessment of the MODIS-derived rice characteristics map and the SAR-derived detailed rice area map, while Root Mean Square Error (RMSE) and linear correlation were used to assess the TSX-derived flooding/transplanting dates. Results showed that multitemporal high spatial resolution SAR data is effective for mapping rice areas and flooding/transplanting dates with an overall accuracy of 90% and a kappa of 0.72 and that hypertemporal moderate-resolution optical imagery is effective for the basic characterization of rice areas with an overall accuracy that ranged from 62% to 87% and a kappa of 0.52 to 0.72. This study has also provided the first assessment of the temporal variation in the backscatter of rice from CSK and TSX using large incidence angles covering all rice crop stages from pre-season until harvest. This complementarity in optical and SAR data can be further exploited in the near future with the increased availability of space-borne optical and SAR sensors. This new information can help improve the identification of rice areas.

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Section: Rice Sar Temporal Backscatter Signature and Sar-based Rice Cmentioning

confidence: 95%

Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR

et al. 2014

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“…The late maturing stage was targeted because of two reasons; (1) it is an important growth stage for prediction of yield and/or grain quality; and (2) X-band was assumed to be effective to derive some unique information during the stage based on the previous study [23]. The second reason implies that the X-band σ 0 is not suitable for estimating the whole canopy variables such as total biomass and leaf area during the most part of vegetative growth period because the volume scattering is easily saturated with a small volume of biomass, i.e., at an early growth stage [12]. In other words, from ecophysiological or agronomic points of view, acquisition of time-series images is not always useful to derive critical information for crop diagnosis.…”

Section: Acquisition and Processing Of Csk And Tsx Sar Imagesmentioning

confidence: 99%

“…A recent preliminary analysis using CSK suggested that the X-band σ 0 at VV polarization at a shallow incidence angle was closely correlated with the weight of rice panicle (ear) [12]. The study also undertook a comprehensive comparison of σ 0 with a range of canopy biophysical variables during the maturing stage.…”

Section: Introductionmentioning

confidence: 99%

“…The negative impacts of agriculture, such as air and water pollution by N 2 O, e.g., [5] and NO 3 , e.g., [6] from farming practices, can be minimized based on geospatial information on actual crop and farmland status, e.g., [7]. Many studies have shown that the synthetic aperture radar (SAR) sensors have great potential for a wide range of agricultural applications due to their superior ability in timely observation of land surfaces, e.g., [8][9][10][11][12][13]. The rain or heavy clouds would affect SAR image data, especially in high frequency bands (e.g., Ka, Ku, or X), through the disturbance in signal-propagation and/or the physical changes in land surfaces [14].…”

Section: Introductionmentioning

confidence: 99%

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Potential of X-Band Images from High-Resolution Satellite SAR Sensors to Assess Growth and Yield in Paddy Rice

2014

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Abstract:The comprehensive relationship of backscattering coefficient (σ 0 ) values from two current X-band SAR sensors (COSMO-SkyMed and TerraSAR-X) with canopy biophysical variables were investigated using the SAR images acquired at VV polarization and shallow incidence angles. The difference and consistency of the two sensors were also examined. The chrono-sequential change of σ 0 in rice paddies during the transplanting season revealed that σ 0 reached the value of nearby water surfaces a day before transplanting, and increased significantly just after transplanting event (3 dB). Despite a clear systematic shift (6.6 dB) between the two sensors, the differences in σ 0 between target surfaces and water surfaces in each image were comparable in both sensors. Accordingly, an image-based approach using the "water-point" was proposed. It would be useful especially when absolute σ 0 values are not consistent between sensors and/or images. Among the various canopy variables, the panicle biomass was found to be best correlated with X-band σ 0 . X-band SAR would be promising for direct assessments of rice grain yields at regional scales from space, whereas it would have limited capability to assess the whole-canopy variables only during the very early growth stages. The results provide a clear insight on the potential capability of X-band SAR sensors for rice monitoring. OPEN ACCESSRemote Sens. 2014, 6 5996

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“…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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Relationship between X-band backscattering coefficients from high-resolution satellite SAR and biophysical variables in paddy rice

Cited by 49 publications

References 16 publications

Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR

Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR

Potential of X-Band Images from High-Resolution Satellite SAR Sensors to Assess Growth and Yield in Paddy Rice

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

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