Detection of the Optimal Temporal Windows for Mapping Paddy Rice Under a Double-Cropping System Using Sentinel-2 Imagery DOI Creative Commons
Li Sheng,

Y.F. Lv,

Zhouqiao Ren

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 17(1), P. 57 - 57

Published: Dec. 27, 2024

Accurately mapping paddy rice is crucial for food security, sustainable agricultural management and environmental protection. Recently, Sentinel-2 optical images with a spatial resolution of 10 m repeat cycle five days have demonstrated enormous potential fields. However, the influence temporal selection on still unclear. In this study, optimal windows were detected by considering all possible combinations during growing stages from constructed cloud-free 10-day time series assessing classification performances combination schemes F1_score. The results indicated that two or three phases necessary early-cropping (EP) late-cropping (LP), achieving F1_score aim 0.96. detection single-cropping (SP) requires to can obtain 0.94. Additionally, an automatic workflow has been developed, which does not require any cloud removal but provides complete coverage, suitable regions frequent rain clouds. Through verification in study area Yiwu, China, discrepancies between statistics within 5%, demonstrating rationality efficiency proposed framework.

Language: Английский

Rice cropping sequence mapping in the tropical monsoon zone via agronomic knowledge graphs integrating phenology and remote sensing DOI Creative Commons

Hongzhang Nie,

Ying‐Chi Lin,

Wenfei Luo

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103075 - 103075

Published: Feb. 1, 2025

Language: Английский

Citations

0
Towards automation of national scale cropping pattern mapping by coupling Sentinel-1/2 data: A 10-m map of crop rotation systems for wheat in China DOI
Bingwen Qiu, Zhengrong Li, Peng Yang

et al.

Agricultural Systems, Journal Year: 2025, Volume and Issue: 227, P. 104338 - 104338

Published: April 6, 2025

Language: Английский

Citations

0
A High-Resolution Distribution Dataset of Paddy Rice in India Based on Satellite Data DOI Creative Commons
Xuebing Chen, Ruoque Shen,

Baihong Pan

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(17), P. 3180 - 3180

Published: Aug. 28, 2024

India, as the world’s second-largest rice producer, accounting for 21.7% of global production, plays a crucial role in ensuring food supply stability. However, creating high-resolution maps such those at 10 to 30 m, poses significant challenges due frequent cloudy weather conditions and complexities its agricultural systems. This study used sample-independent mapping method India using synthetic aperture radar (SAR)-based Rice Index (SPRI). We produced m spatial resolution distribution three years (i.e., 2018, 2020, 2022) 23 states covering 98% Indian production. The effectively utilized unique characteristics vertical–horizontal (VH) backscatter coefficient time series Sentinel-1, from ttransplantation maturity stage, combined with cloud-free Sentinel-2 imagery. By calculating SPRI values each field object adaptive parameters, planting locations were accurately identified. On average, user, overall accuracy over all investigated union territories was 84.72%, 82.31%, 84.40%, respectively. Additionally, regional-scale validation based on statistical area district level showed that determination (R2) ranged 0.53 0.95 state, indicating planted reproduced well.

Language: Английский

Citations

3
An orchard mapping index and mapping algorithm coupling orchard phenology and green-holding characteristics from time-series sentinel-2 images DOI
Riqiang Chen, Hao Yang, Wenping Liu

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 226, P. 109437 - 109437

Published: Sept. 9, 2024

Language: Английский

Citations

2
A novel red-edge vegetable index for paddy rice mapping based on Sentinel-1/2 and GF-6 images DOI Creative Commons
Yiliang Wan,

Yueqi Gong,

Feng Xu

et al.

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: Sept. 2, 2024

Accurate paddy rice mapping is crucial for ensuring food security and guiding agricultural production. Vegetation indices are extensively employed to map rice. However, most traditional normalized tend be oversaturated during periods of lush vegetation due normalization errors, resulting in uncertainties mapping. To address this issue, we introduce a novel red-edge index (RERI) study; comprises information from red, near-infrared, bands without normalization. extract single- double-cropping features potential areas, employ GF-6 Sentinel-2 images based on the proposed RERI random forest algorithm. The method validated Dingcheng District Changde city, China, results compared with those three indices. show that yielded highest levels accuracy all metrics, achieving an overall (OA) 92.50% kappa coefficient 0.8875. exhibited F1 scores 92.26% single-cropping rice, 93.00% 92.28% non-rice areas. Our highlight using identification, effectiveness our extraction demonstrated.

Language: Английский

Citations

1
Phenology Index-Based Method for Mapping Winter Wheat and Summer Maize Rotation Cropping Pattern With Sentinel-2 Imagery DOI Creative Commons
Maolin Yang, Bin Guo, Jianlin Wang

et al.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2024, Volume and Issue: 17, P. 13795 - 13808

Published: Jan. 1, 2024

As a common agricultural intensification, the winter wheat and summer maize rotation cropping pattern (wheat–maize) plays crucial role in achieving sustainable food security China. Reliable regional wheat–maize maps are of great importance to ensure sustainability agro-ecosystems. However, conventional previous studies typically depended on vegetation index time-series for detecting wheat–maize, which was challenging rapid mapping. This study proposed simpler phenology index-based method mapping from multitemporal Sentinel-2 data. To better explore performance, two indices [i.e., normalized difference (NDVI) two-band enhanced (EVI2)] mathematical combinations (i.e., multiplication addition) were introduced generate four uncorrelated indices. The obtained using evaluated samples high-precision derived random forest. results showed that resulting achieved high overall accuracy above 94% F1-score over 0.95, as well agreed with forest (overall ≥ 91%, 0.88). In addition, this found EVI2 suited designing difference-based than NDVI; concerning combination approaches, performed addition enhancing spectral differences. Our demonstrated advantages its potential be applied larger regions. We hope will advance our understanding phenology-based methods agriculture

Language: Английский

Citations

0
Detection of the Optimal Temporal Windows for Mapping Paddy Rice Under a Double-Cropping System Using Sentinel-2 Imagery DOI Creative Commons
Li Sheng,

Y.F. Lv,

Zhouqiao Ren

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 17(1), P. 57 - 57

Published: Dec. 27, 2024

Accurately mapping paddy rice is crucial for food security, sustainable agricultural management and environmental protection. Recently, Sentinel-2 optical images with a spatial resolution of 10 m repeat cycle five days have demonstrated enormous potential fields. However, the influence temporal selection on still unclear. In this study, optimal windows were detected by considering all possible combinations during growing stages from constructed cloud-free 10-day time series assessing classification performances combination schemes F1_score. The results indicated that two or three phases necessary early-cropping (EP) late-cropping (LP), achieving F1_score aim 0.96. detection single-cropping (SP) requires to can obtain 0.94. Additionally, an automatic workflow has been developed, which does not require any cloud removal but provides complete coverage, suitable regions frequent rain clouds. Through verification in study area Yiwu, China, discrepancies between statistics within 5%, demonstrating rationality efficiency proposed framework.

Language: Английский

Citations

0