Pixel-level rice planting information monitoring in Fujin City based on time-series SAR imagery

Pang, Jiatai; Zhang, Rui; Yu, Bin; Liao, Mingjie; Lv, Jichao; Xie, Lingxiao; Li, Song; Zhan, Junyu

doi:10.1016/j.jag.2021.102551

Cited by 12 publications

(7 citation statements)

References 24 publications

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“…can be obtained by band operation for rice identification [ 21 , 22 ]. Existing studies have indicated that during the rice growth cycle, the variation of the rice vegetation index is greater than that of other land types [ 23 – 26 ]. The overall change trend is as follows: (1) In the transplanting stage, the seedlings are short and the vegetation index is low.…”

Section: Introductionmentioning

confidence: 99%

“…Although noise filtering (wavelet transform, Savitzky-Golay time series filtering) [ 40 ], super pixel segmentation [ 41 ] can be used to reduce the influence of SAR noise, but it still influences the accurate rice identification to some extent [ 10 , 12 , 42 ]. The limitations also include the obvious geometric distortions on SAR images, especially in areas with large terrain fluctuations, presenting as foreshortening, layover, and shadow, which affect the extraction accuracy of rice [ 23 , 43 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

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Small- and medium-sized rice fields identification in hilly areas using all available sentinel-1/2 images

Wang,

Ma,

Gao

et al. 2024

Plant Methods

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Background Mastering the spatial distribution and planting area of paddy can provide a scientific basis for monitoring rice production, and planning grain production layout. Previous remote sensing studies on paddy concentrated in the plain areas with large-sized fields, ignored the fact that rice is also widely planted in vast hilly regions. In addition, the land cover types here are diverse, rice fields are characterized by a scattered and fragmented distribution with small- or medium-sized, which pose difficulties for high-precision rice recognition. Methods In the paper, we proposed a solution based on Sentinel-1 SAR, Sentinel-2 MSI, DEM, and rice calendar data to focus on the rice fields identification in hilly areas. This solution mainly included the construction of rice feature dataset at four crucial phenological periods, the generation of rice standard spectral curve, and the proposal of spectral similarity algorithm for rice identification. Results The solution, integrating topographical and rice phenological characteristics, manifested its effectiveness with overall accuracy exceeding 0.85. Comparing the results with UAV, it presented that rice fields with an area exceeding 400 m2 (equivalent to 4 pixels) exhibited a recognition success rate of over 79%, which reached to 89% for fields exceeding 800 m2. Conclusions The study illustrated that the proposed solution, integrating topographical and rice phenological characteristics, has the capability for charting various rice field sizes with fragmented and dispersed distribution. It also revealed that the synergy of Sentinel-1 SAR and Sentinel-2 MSI data significantly enhanced the recognition ability of rice paddy fields ranging from 400 m2 to 2000 m2.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Small- and medium-sized rice fields identification in hilly areas using all available sentinel-1/2 images

Wang,

Ma,

Gao

et al. 2024

Plant Methods

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“…Synthetic Aperture Radar (SAR) stands out among different types of remote sensing payloads due to its unparalleled advantages in monitoring crops in regions affected by weather conditions. Moreover, SAR exhibits excellent sensitivity to differences in crop canopy structure, making it a proven method for measuring plant height (McNairn and Shang, 2016;Pang et al, 2021;Steele-Dunne et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Exploring the effects of crop growth differences on radar vegetation index response and crop height estimation using dynamic monitoring model

Wang,

Liu,

Sheng

et al. 2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Synthetic aperture radar (SAR) has emerged as a promising technology for monitoring crop plant height due to its ability to capture the geometric properties of crops. Radar vegetation index (RVI) has been extensively utilized for qualitative and quantitative remote sensing monitoring of vegetation growth dynamics. However, the combination of crop, growing environment, and temporal dynamics makes crop monitoring data a complex task. Despite the relatively simple underlying mechanisms of this phenomenon, there is still a need for more research to identify specific vegetation structures that correspond to changes in the response of vegetation indices. Building upon this premise, this study utilized a dynamic monitoring model to conduct dynamic monitoring of plant height for three common crops: rice, wheat, and maize. The findings revealed that (1) models developed for specific spatial and temporal scales of particular crop varieties may not accurately predict crop growth in different regions or with different varieties in a timely manner, due to growth variations; (2) these models maintain accuracy over a range of plant heights, such as rice at around 70cm, wheat at around 50cm, and maize at around 150cm; (3) among the three crops, planting density was identified as the main factor influencing the differences in RVI response. This research contributes to our comprehension of the dynamic response of RVI to different growth conditions in crops, and offers valuable insights and references for agricultural monitoring.

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“…Many studies have used single-temporal SAR backscatter characteristics to monitor rice (Gao et al, 2019), whereas single-temporal SAR data usually results in low identification accuracy due to missing key phenological stage information (Lopez-Sanchez et al, 2012;Li et al, 2014). Compared with single-temporal SAR data, multi-temporal or time-series SAR data can capture phenological information about rice in the whole growth cycle, thereby contributing to improving rice identification accuracy (Yang et al, 2017(Yang et al, , 2018Csorba et al, 2019;Chandra Paul et al, 2020;Pang et al, 2021;Zhan et al, 2021). At present, most studies on rice identification using SAR have focused on the flat terrain areas where rice fields are concentrated and large-sized, such as the Mekong Delta (Bouvet and Le Toan, 2011;Clauss et al, 2018), Bangladesh (Panigrahy et al, 2012), Vijayawada in India (Mandal et al, 2020), and Northeast China and the Middle and Lower Yangtze Valley Plain (Zhan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A new rice identification algorithm under complex terrain combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization synthetic aperture radar

Wang²,

Sun³

et al. 2023

Front. Ecol. Evol.

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Accurate mapping of rice-growing areas is essential to ascertain the spatial distribution of rice fields, and ensure food security. It is a challenging task to timely and accurate identify rice under the complex terrain due to its diversified land cover, small- or middle-sized rice fields with fragmented distribution. In this paper, the time series VV and VH backscatter coefficient datasets were first constructed based on 411 sentinel-1 synthetic aperture radar (SAR) images in Chongqing city with complex terrain. Then, the rice multi-characteristic parameters, including SAR backscatter features, composite features, rice phenological parameters, texture features and topographic features, were generated. On this basis, the homogeneous image objects were produced. Furthermore, a rice identification algorithm combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization SAR (MPHO-DPSAR) was established. The research demonstrated that the MPHO-DPSAR algorithm can achieve accurate mapping of small and medium-sized and fragmented rice fields in regions under complex terrain according to the accuracy evaluation at three levels and the comparison with other three classical rice identification methods. The suitability and limitations of proposed MPHO-DPSAR algorithm were also discussed from the aspects of SAR data temporal and spatial resolution, rice phenology, and surface landscape complexity.

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Pixel-level rice planting information monitoring in Fujin City based on time-series SAR imagery

Cited by 12 publications

References 24 publications

Small- and medium-sized rice fields identification in hilly areas using all available sentinel-1/2 images

Small- and medium-sized rice fields identification in hilly areas using all available sentinel-1/2 images

Exploring the effects of crop growth differences on radar vegetation index response and crop height estimation using dynamic monitoring model

A new rice identification algorithm under complex terrain combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization synthetic aperture radar

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