Flooded Area Extraction of Rice Paddy Field in Indonesia Using Sentinel-1 Sar Data

Wakabayashi, Hiroyuki; Motohashi, Kazushige; Kitagami, Tooru; Tjahjono, Budi; Dewayani, S.; Hidayat, Dony; Hongo, Chuki

doi:10.5194/isprs-archives-xlii-3-w7-73-2019

Cited by 18 publications

(15 citation statements)

References 7 publications

(7 reference statements)

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“…A threshold for the backscattering coefficient to divide flood and non-flood areas was successfully applied to SAR data over various frequencies (Henry et al, 2006, Martinis et al, 2015, and Ohki et al, 2016. We selected fifty rice objects and fifty non-rice objects which are evenly distributed over the study area, calculated their minimum VH backscatter intensity during the time window, and applied linear discriminant analysis (LDA) to separate rice and non-rice objects using gamma-naught, which ultimately provides a threshold that separates the two classes [37]. A separation line between two classes can be established and the threshold can be obtained from the LDA with a threshold of −20.1 dB.…”

Section: Flood Signal Detectionmentioning

confidence: 99%

Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm—A Implementation in Hangjiahu Plain in China Using GEE Platform

Xiao

2021

Remote Sensing

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In tropical/subtropical monsoon regions, accurate rice mapping is hampered by the following factors: (1) The frequent occurrence of clouds in such areas during the rice-growing season interferes strongly with optical remote sensing observations; (2) The agro-landscape in such regions is fragmented and scattered. Rice maps produced using low spatial resolution data cannot well delineate the detailed distribution of rice, while pixel-based mapping using medium and high resolutions has significant salt-and-pepper noise. (3) The cropping system is complex, and rice has a rotation schedule with other crops. Therefore, the Phenology-, Object- and Double Source-based (PODS) paddy rice mapping algorithm is implemented, which consists of three steps: (1) object extraction from multi-temporal 10-m Sentinel-2 images where the extracted objects (fields) are the basic classification units; (2) specifying the phenological stage of transplanting from Savitzky–Golay filtered enhanced vegetation index (EVI) time series using the PhenoRice algorithm; and (3) the identification of rice objects based on flood signal detection from time-series microwave and optical signals of the Sentinel-1/2. This study evaluated the potential of the combined use of the Sentinel-1/2 mission on paddy rice mapping in monsoon regions with the Hangzhou-Jiaxin-Huzhou (HJH) plain in China as the case study. A cloud computing approach was used to process the available Sentinel-1/2 imagery from 2019 and MODIS images from 2018 to 2020 in the HJH plain on the Google Earth Engine (GEE) platform. An accuracy assessment showed that the resultant object-based paddy rice map has a high accuracy with a producer (user) accuracy of 0.937 (0.926). The resultant 10-m paddy rice map is expected to provide unprecedented detail, spatial distribution, and landscape patterns for paddy rice fields in monsoon regions.

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Section: Flood Signal Detectionmentioning

confidence: 99%

Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm—A Implementation in Hangjiahu Plain in China Using GEE Platform

Xiao

2021

Remote Sensing

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“…In addition to the optical MODIS-based LSWI/EVI relationship approach, we also applied the minimum value of VH data in the transplanting stage to identify flooding signals, as suggested in previous studies (Clauss et al, 2018b). VH has a higher sensitivity in paddy rice growth stages than VV polarization (Inoue et al, 2020;Nguyen et al, 2016;Wakabayashi et al, 2019). However, the minimum value of VH in different regions is different because Sentinel-1 data are affected by the incidence angle (ranging from approximately 30° to 45°) (Figure S5) (Phung et al, 2020;Singha et al, 2019;Zhang et al, 2018).…”

Section: Algorithm For Identifying Paddy Rice Fieldsmentioning

confidence: 99%

AsiaRiceMap10m: high-resolution annual paddy rice maps for Southeast and Northeast Asia from 2017 to 2019

Han

Zhang

Luo

et al. 2021

Preprint

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Abstract. An accurate paddy rice map is crucial for food security, particularly for Southeast and Northeast Asia. The MODIS satellite data is useful for mapping paddy rice at continental scales but has a problem of mixed-pixel due to coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time-series Sentinel-1 and MODIS data. We demonstrated the method in generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (AsiaRiceMap10m). The resultant paddy rice maps were compared with available agricultural statistics at subnational levels and existing rice maps for some countries. The results show that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranges from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R2 = 0.93) and Northeast China (R2 = 0.99). The spatial distribution of the 2017–2019 composite paddy rice map is consistent with the rice map from the International Rice Research Institute. The paddy rice planting area may be underestimated in the region where the flooding signal is not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.17632/j34b3jsvr9.1 (Han et al., 2021a). Find small examples here (https://doi.org/10.17632/cnc3tkbwcm.1) (Han et al., 2021b).

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“…Wakabayashi et al. (2019) assessed Sentinel‐1 backscatter expressed in gamma naught to reduce the impact of incidence angle. This research used a thresholding technique to detect flooded rice fields in Indonesia with very high accuracies of 98% reported.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Stroppiana et al (2019) applied a region-growing algorithm to detect flooded rice fields using VV Sentinel-1 sigma-naught backscatter, achieving an accuracy greater than 70% using optical data as the comparison. Wakabayashi et al (2019) assessed Sentinel-1 backscatter expressed in gamma naught to reduce the impact of incidence angle. This research used a thresholding technique to detect flooded rice fields in Indonesia with very high accuracies of 98% reported.…”

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confidence: 99%

Rice Inundation Assessment Using Polarimetric UAVSAR Data

Huang

Runkle

Isbell³

et al. 2021

Earth and Space Science

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Rice cultivation practices in the United States typically include managing irrigation water for inundated conditions. In the Arkansas Delta region, for example, rice fields can require 5-20 cm of inundation for seeding, with intermittent or continuous flooding during growth phases until drainage near harvest, and between rotations for wildfowl habitat or land maintenance (Reba et al., 2020). Irrigation practices, access to water, regulatory policies, and infrastructure vary across the Midsouth USA resulting in many unique combinations of landforms, irrigation regimes, and water sources. When using flood irrigation, producers have challenges managing and measuring water quantities at the field or paddy level. One well or well outlet can irrigate multiple fields, water is gravity-driven from field to field, underground pipe and plumbing infrastructure can use a combination of ground and surface water, and surface poly-pipe can move water from field to field. In Arkansas, total water use throughout the growing season can range from 382 mm on zero grade (fields without of slope) to 1,034 mm for contour levees (Smith et al., 2007). Regardless of the approach to irrigation, the ground water supply is depleting faster than recharge (Kresse et al., 2014), and public private partnerships are investigating tradeoffs across irrigation regimes, greenhouse gas emissions, water quantity, and management practices (Nalley et al., 2015; Reba et al., 2013; Reba & Massey, 2020). To scale these field experiments the science community and public and private sectors can benefit from synoptic, large area metrics on rice field inundation conditions. Traditionally, optical satellite remote sensing tools have focused on identifying surface or open water conditions. These sensors and techniques struggle when postharvest residues are present or once crop emergence

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Flooded Area Extraction of Rice Paddy Field in Indonesia Using Sentinel-1 Sar Data

Cited by 18 publications

References 7 publications

Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm—A Implementation in Hangjiahu Plain in China Using GEE Platform

Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm—A Implementation in Hangjiahu Plain in China Using GEE Platform

AsiaRiceMap10m: high-resolution annual paddy rice maps for Southeast and Northeast Asia from 2017 to 2019

Rice Inundation Assessment Using Polarimetric UAVSAR Data

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