Monitoring Rice Phenology Based on Freeman-Durden Decomposition of Multi-Temporal Radarsat-2 Data

He, Ze; Li, Shihua; Lin, Shuai; Dai, Li

doi:10.1109/igarss.2018.8517621

Cited by 8 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Polarimetric decomposition [32,[48][49][50] Decision trees [40][41][42][43][44] Analysis of histograms [45][46][47] 4. Study area and data 4.1.…”

Section: Rice Mapping Methods Referencesmentioning

confidence: 99%

“…Polarimetric data may provide a reference on the actual scattering mechanism taking place in the fields. Thanks to fully-polarimetric SAR data, in fact, it is possible to analyse the double-bounce enhancement due to still water in flooded agricultural fields [32,[48][49][50]. Once the coherence and covariance matrices are derived from the polarimetric dataset, decompositions like the Freeman and eigenvector decompositions are applied in order to derive meaningful information regarding the physics behind the scattering process.…”

Section: State Of the Art In Flooded Vegetation Detectionmentioning

confidence: 99%

“…Reviewing the scientific literature, we found that a wide variety of SAR sensors have been employed in rice mapping applications [41,48,49,[54][55][56][57][58], such as COSMO-SkyMed (CSK), Sentinel-1, Radarsat-2, TerraSAR-X, PALSAR-2, etc. Notwithstanding its inability to provide fully polarimetric data, in this work we decided to take advantage of Sentinel-1 radar data.…”

Section: Satellite Datamentioning

confidence: 99%

See 2 more Smart Citations

Monitoring Watering Practices on European Rice Paddy Fields with Sentinel-1: Characterizing European Rice in Multitemporal SAR Sequences for Mapping and Traceability Purposes

Marzi¹,

Dell’Acqua²

2021

Preprint

View full text Add to dashboard Cite

Whereas a vast literature exists on satellite-based mapping of rice paddy fields in Asia, where most of the global production takes place, little has been produced so far that focuses on the European context. Detection and mapping methods that work well in the Asian context will not offer the same performances in Europe, where different seasonal cycles, environmental contexts, and rice varieties make distinctive features dissimilar to the Asian case. In this context, water management is a key clue; watering practices are distinctive for rice with respect to other crops, and within rice there exist diverse cultivation practices including organic and non-organic approaches. In this paper, we focus on satellite-observed water management to identify rice paddy fields cultivated with a traditional agricultural approach. Building on established research results, and guided by the output of experiments on real-world cases, a new method for analysing time series of Sentinel-1 data has been developed, which can identify traditional rice fields with a high degree of reliability. This work is a part of a broader initiative to build space-based tools for collecting additional pieces of evidence to support food chain traceability; the whole system will consider various parameters, whose analysis procedures are still at their early stages of development.

show abstract

“…Polarimetric decomposition [32,[48][49][50] Decision trees [40][41][42][43][44] Analysis of histograms [45][46][47] 4. Study area and data 4.1.…”

Section: Rice Mapping Methods Referencesmentioning

confidence: 99%

Section: State Of the Art In Flooded Vegetation Detectionmentioning

confidence: 99%

Section: Satellite Datamentioning

confidence: 99%

See 1 more Smart Citation

Monitoring Watering Practices on European Rice Paddy Fields with Sentinel-1: Characterizing European Rice in Multitemporal SAR Sequences for Mapping and Traceability Purposes

Marzi¹,

Dell’Acqua²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Another widely used rice mapping approach is based on polarimetry, which may provide clues on the actual scattering mechanism taking place in each field. Fully polarimetric SAR data permit analysis of the double-bounce enhancement due to still water in flooded agricultural fields [33,[49][50][51]. Once the coherence and covariance matrices are derived from the polarimetric dataset, decompositions such as the Freeman and eigenvector can be applied to derive meaningful information on the scattering process.…”

Section: State Of the Art In Flooded Vegetation Detectionmentioning

confidence: 99%

“…Reviewing the scientific literature, we found that a wide variety of SAR sensors have been employed in rice mapping applications [42,49,50,[54][55][56][57][58], such as COSMO-SkyMed (CSK), Sentinel-1, Radarsat-2, TerraSAR-X, PALSAR-2, etc. Notwithstanding its inability to provide fully polarimetric data, in this work we decided to take advantage of Sentinel-1 radar data.…”

Section: Satellite Datamentioning

confidence: 99%

Mapping European Rice Paddy Fields Using Yearly Sequences of Spaceborne Radar Reflectivity: A Case Study in Italy

Marzi

Dell’Acqua

2021

Earth

View full text Add to dashboard Cite

Although a vast literature exists on satellite-based mapping of rice paddy fields in Asia, where most of the global production takes place, little has been produced so far that focuses on the European context. Detection and mapping methods that work well in the Asian context will not offer the same performance in Europe, where different seasonal cycles, environmental contexts, and rice varieties make distinctive features dissimilar to the Asian case. In this context, water management is a key clue; watering practices are distinctive for rice with respect to other crops, and within rice there exist diverse cultivation practices including organic and non-organic approaches. In this paper, we focus on satellite-observed water management to identify rice paddy fields cultivated with a traditional agricultural approach. Building on established research results, and guided by the output of experiments on real-world cases, a new method for analyzing time-series of Sentinel-1 data has been developed, which can identify traditional rice fields with a high degree of reliability. Typical watering practices for traditional rice cultivation leave distinctive marks on the yearly sequence of spaceborne radar reflectivity that are identified by the proposed classifier. The method is tested on a small sample of rice paddy fields, built by direct collection of ground reference information. Automated setting of parameters was sufficient to achieve accuracy values beyond 90%, and scanning of a range of values led to touch full score on an independent test set. This work is a part of a broader initiative to build space-based tools for collecting additional pieces of evidence to support food chain traceability; the whole system will consider various parameters, whose analysis procedures are still at their early stages of development.

show abstract

Automatic Identification Algorithm of the Rice Tiller Period Based on PCA and SVM

2021

View full text Add to dashboard Cite

The tillering period of rice is the crucial phenological period for the cultivation of high-quality and high-yield rice. Currently, manual observation is mainly used for identification, but it is time-consuming, laborious, and prone to mistakes. To efficiently and accurately identify the start date of rice tillering in the monitoring area, this paper proposed a new algorithm called rice tiller period recognition combining principal component analysis (PCA) and a support vector machine (SVM) (RTR-CPS). This algorithm characterizes the problem of identifying the rice tiller stage as a binary classification problem of rice either entering or not entering the tiller stage. To improve the image segmentation quality of traditional visual segmentation methods, the algorithm was designed to extract five image feature values to describe the rice tiller stage in a multifeatured way, reducing the impact of single feature value bias on the identification of the rice tiller stage. To improve the performance of the rice tiller stage recognition model, the algorithm selects ideal principal features in limited sample data by the PCA algorithm and optimizes SVM classification model hyperparameters by combining 5-fold cross-validation. The experimental results showed that the accuracy of the algorithm for identifying the tillering date of potted rice was as high as 97.76%, which is significantly higher than other competitive methods, and the maximum error between the detection results and manual observation of potted rice tillering period was no more than 2 days. The rice tillering stage recognition model was applied to the field, and the images of field rice planted by two different methods were tested, which verified that the algorithm proposed in this paper is generalizable. These results fully demonstrate the feasibility and superiority of the algorithm in this paper.

show abstract

Monitoring Rice Phenology Based on Freeman-Durden Decomposition of Multi-Temporal Radarsat-2 Data

Cited by 8 publications

References 13 publications

Monitoring Watering Practices on European Rice Paddy Fields with Sentinel-1: Characterizing European Rice in Multitemporal SAR Sequences for Mapping and Traceability Purposes

Monitoring Watering Practices on European Rice Paddy Fields with Sentinel-1: Characterizing European Rice in Multitemporal SAR Sequences for Mapping and Traceability Purposes

Mapping European Rice Paddy Fields Using Yearly Sequences of Spaceborne Radar Reflectivity: A Case Study in Italy

Automatic Identification Algorithm of the Rice Tiller Period Based on PCA and SVM

Contact Info

Product

Resources

About