Comparison of SAR and optical sensor data for monitoring of rice plant around Hiroshima

Oguro, Yanao; Suga, Yuki; Takeuchi, Shoji; Ogawa, Midori; Konishi, Tomohisa; Tsuchiya, K.

doi:10.1016/s0273-1177(01)00345-3

Cited by 37 publications

(23 citation statements)

References 3 publications

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“…Generally, land use and land cover types used for cross-comparison analysis between different sensors cover a wide range of natural vegetation [3,7], agricultural crops [8,9], burned areas and severity of forest fire [10,11] and bare soil [12,13]. Likewise, cross-comparison among satellite sensor systems covers almost all the existing airborne and spaceborne sensors, including optical and Synthetic Aperture Radar (SAR) [5,10,14].…”

Section: Introductionmentioning

confidence: 99%

“…Likewise, cross-comparison among satellite sensor systems covers almost all the existing airborne and spaceborne sensors, including optical and Synthetic Aperture Radar (SAR) [5,10,14]. Different research activities all over the world confirmed that many sensors are highly linearly related or vary slightly, which makes them useful for complementary data: SPOT-1 High-Resolution Visible (HRV) and Thematic Mapper (TM) [12], Japanese Earth Resources Observation Satellite (JERS)-1 Optical Sensor (OPS) and SPOT-2 HRV [13], Advanced Earth Observing Satellite (ADEOS)-1 Advanced Visible and Near Infrared Radiometer (AVNIR) and TM [7], Wide Field Sensor (WiFS) and Linear Imaging Self-scanning Sensor (LISS)-III [11], Radarsat-1, TM and SPOT-2/4 HRV [8], Radarsat-1 and TM [1], Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) and Enhanced Thematic Mapper Plus (ETM+) [10] and Multispectral Scanner (MSS), TM and ETM+ [9,15,16]. These sensors normally have analogous spatial and spectral characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors

2013

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Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are currently operational for routine Earth observation. There are substantial differences between instruments onboard both satellites. The enhancements achieved with Landsat-8 refer to the scanning technology (replacing of whisk-broom scanners with two separate push-broom OLI and TIRS scanners), an extended number of spectral bands (two additional bands provided) and narrower bandwidths. Therefore, cross-comparative analysis is very necessary for the combined use of multi-decadal Landsat imagery. In this study, 3,311 independent sample points of four major land cover types (primary forest, unplanted cropland, swidden cultivation and water body) were used to compare the spectral bands of ETM+ and OLI. Eight sample plots with different land cover types were manually selected for comparison with the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), the Land Surface Water Index (LSWI) and the Normalized Burn Ratio (NBR). These indices were calculated with six pairs of ETM+ and OLI cloud-free images, which were acquired over the border area of Myanmar, Laos and Thailand just two days apart, when Landsat-8 achieved operational obit. Comparative results showed that: (1) the average surface reflectance of each band differed slightly, but with a high degree of similarities between both sensors. In comparison with ETM+, the OLI had higher values for the near-infrared band for vegetative land cover types, but lower values for non-vegetative types. The new sensor had lower values for the shortwave infrared (2.11-2.29 µm) band for

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors

2013

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“…For example, the misclassified non-boundary pixels accounted for 9.5% of the error in ASD1810 at an error level of 40% (Figure 3). These errors derived from non-boundary pixels can be treated as "salt and pepper", a common issue when implementing classification algorithms to obtain crop maps [60,61]. Therefore, errors produced by non-boundary pixels were considered satisfactory in obtaining the thematic maps used in this study.…”

Section: Error Simulation Issuesmentioning

confidence: 99%

The Impact of Mapping Error on the Performance of Upscaling Agricultural Maps

et al. 2017

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Aggregation methods are the most common way of upscaling land cover maps. To analyze the impact of land cover mapping error on upscaling agricultural maps, we utilized the Cropland Data Layer (CDL) data with corresponding confidence level data and simulated eight levels of error using a Monte Carlo simulation for two Agriculture Statistic Districts (ASD) in the U.S.A. The results of the simulations were used as base maps for subsequent upscaling, utilizing the majority rule based aggregation method. The results show that increasing error level resulted in higher proportional errors for each crop in both study areas. As a result of increasing error level, landscape characteristics of the base map also changed greatly resulting in higher proportional error in the upscaled maps. Furthermore, the proportional error is sensitive to the crop area proportion in the base map and decreases as the crop proportion increases. These findings indicate that three factors, the error level of the thematic map, the change in landscape pattern/characteristics of the thematic map, and the objective of the project, should be considered before performing any upscaling. The first two factors can be estimated by using pre-existing land cover maps with relatively high accuracy. The third factor is dependent on the project requirements (e.g., landscape characteristics, proportions of cover types, and use of the upscaled map). Overall, improving our understanding of the impacts of land cover mapping error is necessary to the proper design for upscaling and obtaining the optimal upscaled map.

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“…For pixel-based classification, the "salt and pepper" is a common issue caused from the spectral heterogeneity (Oguro et al, 2001). In TSPSM, the TSP, P ij , possesses the spatial traits to quantify the relationship between the center pixel and surrounding pixels, which can reduce the spectral heterogeneity with a similar function of a spatial filter (Ji, Ma, Twibell, & Underhill, 2006;Switzer, 1980;Zhu et al, 2010).…”

Section: Accuracy Assessmentmentioning

confidence: 99%

“…However, applicability of those approaches has limit for large-scale operation, because that it is difficult to get sufficient images for crop identification (Cheng, Shen, Zhang, Yuan, & Zeng, 2014;Jin et al, 2013;Ju & Roy, 2008;Zhu, Chen, Gao, Chen, & Masek, 2010). To avoid the effect of cloud contamination, synthetic aperture radar images were used for mapping rice planting area (Oguro, Suga, Takeuchi, Ogawa, & Konishi, 2001;Shao et al, 2001;Zhang, Wang, Wu, Qi, & Salas, 2009), which could solve the issue of the cloud contamination in optical images. However, its application has been limited by data availabilities of few satellites in-orbit operation (Shao et al, 2001;Zhang et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A highly efficient temporal-spatial probability synthesized model from multi-temporal remote sensing for paddy rice identification

Sun

Zhang

Zhu

et al. 2017

European Journal of Remote Sensing

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This article develops a temporal-spatial probability synthesized model (TSPSM), in which a metric describing the characteristic of temporal and spatial information is defined to map paddy rice distribution. The purpose is to reduce the effect of cloud contamination on classification. The error matrix and Kappa were used as accuracy measurement. Results showed that TSPSM obtained higher accuracy with significant difference from error matrices of the other two conventional methods, post comparison classification with post-classification comparison and majority voting. Moreover, smaller window was suitable for the area with higher fragmentation, while the larger was suitable for the area with lower fragmentation. It was concluded that TSPSM could help to improve the potentials of temporal optical image to map crops.

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Comparison of SAR and optical sensor data for monitoring of rice plant around Hiroshima

Cited by 37 publications

References 3 publications

Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors

Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors

The Impact of Mapping Error on the Performance of Upscaling Agricultural Maps

A highly efficient temporal-spatial probability synthesized model from multi-temporal remote sensing for paddy rice identification

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