Development of Variable Threshold Models for detection of irrigated paddy rice fields and irrigation timing in heterogeneous land cover

Jeong, Seungtaek; Kang, Sinkyu; Jang, Keunchang; Lee, Hoonyol; Hong, Su Young; Ko, Dongwook W.

doi:10.1016/j.agwat.2012.08.012

Cited by 33 publications

(20 citation statements)

References 14 publications

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“…Sun et al [59] proposed 0.17 as the T value for late transplanted rice and Peng et al [7] used 0.21 for double-cropping fields. A method for applying different T values to each pixel according to land cover heterogeneity has also been reported [60]. In the current study, to determine the appropriate T value for the paddy fields in South Korea, the paddy fields were detected by using the threshold method while increasing the T value by 0.01 ( Figure 3a).…”

Section: Detection Of Paddy Fields and Transplanting Datesmentioning

confidence: 89%

“…However, these approaches are not suitable for regions with many mixed pixels such as the current study area because the spectral characteristics of crops can be lost. However, the LSWI-based approach used in this study is highly sensitive to irrigation water, which makes it relatively accurate for paddy detection in mixed land cover regions [60,72]. An additional approach is available for using GOCI-based EVI (or NDVI) and MODIS-based LSWI.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, the thresholds were defined as paddy fields exceeding 90% of the total paddy fields because non-paddy fields increase inversely if the threshold criterion is higher. The most challenging aspect of the approach using thresholds is how to determine an appropriate threshold value that can vary depending on land cover composition or land surface characteristics of the study area [7,60]. This study used thresholds that can ignore small paddy fields in consideration of the coarse spatial resolution of MODIS images.…”

Section: Discussionmentioning

confidence: 99%

“…Xiao et al [46] suggested 0.05 as a global T value. However, other studies have reported that the T value should be changed, according to local land cover characteristics or cropping systems [7,59,60]. Sun et al [59] proposed 0.17 as the T value for late transplanted rice and Peng et al [7] used 0.21 for double-cropping fields.…”

Section: Detection Of Paddy Fields and Transplanting Datesmentioning

confidence: 99%

See 3 more Smart Citations

Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery: A Case Study in South Korea

Jeong

Yeom

2018

Remote Sensing

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The Geostationary Ocean Color Imager (GOCI) of the Communication, Ocean, and Meteorological Satellite (COMS) increases the chance of acquiring images with greater clarity eight times a day and is equipped with spectral bands suitable for monitoring crop yield in the national scale with a spatial resolution of 500 m. The objectives of this study were to classify nationwide paddy fields and to project rice (Oryza sativa) yield and production using the grid-based GRAMI-rice model and GOCI satellite products over South Korea from 2011 to 2014. Solar insolation and temperatures were obtained from COMS and the Korea local analysis and prediction systems for model inputs, respectively. The paddy fields and transplanting dates were estimated by using Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and land cover products. The crop model was calibrated using observed yield data in 11 counties and was applied to 62 counties in South Korea. The overall accuracies of the estimated paddy fields using MODIS data ranged from 89.5% to 90.2%. The simulated rice yields statistically agreed with the observed yields with mean errors of −0.07 to +0.10 ton ha−1, root-mean-square errors of 0.219 to 0.451 ton ha−1, and Nash–Sutcliffe efficiencies of 0.241 to 0.733 in four years, respectively. According to paired t-tests (α = 0.05), the simulated and observed rice yields were not significantly different. These results demonstrate the possible development of a crop information delivery system that can classify land cover, simulate crop yield, and monitor regional crop production on a national scale.

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Section: Detection Of Paddy Fields and Transplanting Datesmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Detection Of Paddy Fields and Transplanting Datesmentioning

confidence: 99%

See 2 more Smart Citations

Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery: A Case Study in South Korea

Jeong

Yeom

2018

Remote Sensing

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“…Bridhikitti and Overcamp (2012) further optimized the algorithm conducted by Xiao et al (2005), Xiao et al (2006) that is based on temporal profiles of vegetation and water content. However, these algorithms exploiting the coarse resolution data are feasible to underestimate or overestimate paddy rice of the small-scale field (Jeong et al, 2012).…”

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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Time‐series remote sensing of rice paddy expansion in the Yellow River Delta: Towards sustainable ecological conservation in the context of water scarcity

Huang

Zhang

2022

Remote Sens Ecol Conserv

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Large river deltas are usually ecologically important wetland habitats, but also fertile agricultural exploitation areas, creating a conflict of long‐running substantial interest between agricultural expansion and wetland conservation. Over the past several years, large‐scale cultivation of water‐consuming rice has been growing rapidly in the semi‐arid climate of the Yellow River Delta (YRD). Timely monitoring of rice cultivation dynamics is of great significance for sustainable ecological conservation of the delta, which has insufficient freshwater resources. This study proposed a stratified metrics‐based method that integrates statistical spectral indices and phenological metrics at different growing stages to improve the accuracy of rice paddy classification in areas where rice and wetlands coexist. We applied the method to time‐series Sentinel‐1/2 images to produce annual rice paddy maps of the YRD from 2016 to 2021. Together with rice paddy data from 2011 to 2015 from Statistical Yearbooks of Dongying Bureau of Statistics, we investigated the expansion dynamics over the past decade and in this paper discuss the advantages and disadvantages of rice cultivation expansion over wetland ecosystem conservation. Rapid expansion of rice cultivation intensifies water conflicts, and adversely affects wetland restoration in the YRD. Considering the important ecological services of rice paddies as alternative habitats, we argue for maintaining a reasonable scale of rice paddies and optimizing their distribution as a potential solution to achieving the overall sustainable conservation of the YRD in the context of water scarcity.

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Development of Variable Threshold Models for detection of irrigated paddy rice fields and irrigation timing in heterogeneous land cover

Cited by 33 publications

References 14 publications

Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery: A Case Study in South Korea

Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery: A Case Study in South Korea

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

Time‐series remote sensing of rice paddy expansion in the Yellow River Delta: Towards sustainable ecological conservation in the context of water scarcity

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