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

Inoue, Yoshio; Sakaiya, Eiji; Wang, Cuizhen

doi:10.3390/rs6075995

Cited by 36 publications

(23 citation statements)

References 41 publications

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“…Stable water has a low average backscatter (i.e., a low mean value measured across the whole multitemporal series) compared with rice. Built-up areas, on the other hand, show higher average backscatter than rice [34]. In general, rice can be discriminated from other crops that are not inundated during the planting and vegetative stage if the multitemporal acquisition series is properly synchronized with the cropping calendar.…”

Section: Rice Detection Algorithm and Threshold Selectionmentioning

confidence: 94%

“…Multitemporal X-band SAR data clearly identifies newly transplanted rice seedlings and hence has the potential to detect transplanting dates [34]. The analysis of the relationship between TSX-derived dates and the actual flooding/transplanting dates showed a very strong correlation with an R 2 of 0.87 (Table 7).…”

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

confidence: 94%

“…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%

“…However, unusual weather such as intense localized events (rainfall or dense cloud cover) has been shown to affect high-frequency bands such as the X-band (2.4 to 3.8 cm) [33]. Despite this, SAR remains a reliable sensor for providing stable data quality and images at a chosen time [34].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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 Detection Algorithm and Threshold Selectionmentioning

confidence: 94%

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

confidence: 94%

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

confidence: 95%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…SAR sensors have good potential for crop monitoring because they are not affected by sky conditions [8][9][10]. Drone-based remote sensors can play unique roles due to their timely and flexible operation and super-high spatial resolution (~10 cm) [11,12].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Sunlight Conditions on the Consistency of Vegetation Indices in Croplands—Effective Usage of Vegetation Indices from Continuous Ground-Based Spectral Measurements

et al. 2015

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A ground-based network of spectral observations is useful for ecosystem monitoring and validation of satellite data. However, these observations contain inherent uncertainties due to the change of sunlight conditions. This study investigated the impact of changing solar zenith angles and diffuse/direct light conditions on the consistency of vegetation indices (normalized difference vegetation index (NDVI) and green-red vegetation index (GRVI)) derived from ground-based spectral measurements in three different types of cropland (paddy field, upland field, cultivated grassland) in Japan. In general, the vegetation indices decreased with decreasing solar zenith angle. This response was affected significantly by the growth stage and diffuse/direct light conditions. The decreasing response of the NDVI to the decreasing solar zenith angle was high during the middle growth stage (0.4 < NDVI < 0.8). On the other hand, a similar response of the GRVI was evident except in the early growth stage (GRVI < 0). The response of OPEN ACCESSRemote Sens. 2015, 7 14080 vegetation indices to the solar zenith angle was evident under clear sky conditions but almost negligible under cloudy sky conditions. At large solar zenith angles, neither the NDVI nor the GRVI were affected by diffuse/direct light conditions in any growth stage. These experimental results were supported well by the results of simulations based on a physically-based canopy reflectance model (PROSAIL). Systematic selection of the data from continuous diurnal spectral measurements in consideration of the solar light conditions would be effective for accurate and consistent assessment of the canopy structure and functioning.

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Examining the Outcome of Coupling Machine Learning with Dual Polarimetric SAR for Rice Growth Mapping

Trisasongko

Panuju

Griffin

et al. 2022

Agriculture, Livestock Production and Aquaculture

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

Cited by 36 publications

References 41 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

The Impact of Sunlight Conditions on the Consistency of Vegetation Indices in Croplands—Effective Usage of Vegetation Indices from Continuous Ground-Based Spectral Measurements

Examining the Outcome of Coupling Machine Learning with Dual Polarimetric SAR for Rice Growth Mapping

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