A study of the use of COSMO-SkyMed SAR PingPong polarimetric mode for rice growth monitoring

Corcione, Valeria; Nunziata, Ferdinando; Mascolo, Lucio; Migliaccio, Maurizio

doi:10.1080/01431161.2015.1131902

Cited by 26 publications

(15 citation statements)

References 17 publications

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“…There were three mapping scenarios: (1) VH polarization alone (blue); (2) VV polarization alone (green); (3) joint VH + VV polarization (red). harvesting stages of FSPR February 21,2018 harvesting stages of sugarcane In terms of polarizations, although the VV polarization acquired at 3 April, 2017 obtained the highest importance score with XGBoost classifier, in general, there was no significant difference in importance scores for VH and VV polarizations. Therefore, it was difficult to distinguish which polarization was better for sugarcane identification at this step.…”

Section: Incremental Classification Accuracymentioning

confidence: 92%

“…Monitoring the stages of rice growth has been a key application of SAR in tropical regions since the 1990s, particularly in Asian countries. Most of this monitoring has focused on rice mapping by employing the European Remote Sensing satellites (ERS) 1 and 2 [16,17], Radarsat [18], Envisat ASAR (Advanced Synthetic Aperture Radar) [19], TerraSAR-X [20], COSMO-SkyMed [21] and recently Sentinel-1 (S1A) [22][23][24][25][26][27]. To date, however, there have been few studies that have utilized SAR data for sugarcane mapping.…”

Section: Introductionmentioning

confidence: 99%

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Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China

Jiang

Jing

et al. 2019

Remote Sensing

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More than 90% of the sugar production in China comes from sugarcane, which is widely grown in South China. Optical image time series have proven to be efficient for sugarcane mapping. There are, however, two limitations associated with previous research: one is that the critical observations during the sugarcane growing season are limited due to frequent cloudy weather in South China; the other is that the classification method requires imagery time series covering the entire growing season, which reduces the time efficiency. The Sentinel-1A (S1A) synthetic aperture radar (SAR) data featuring relatively high spatial-temporal resolution provides an ideal data source for all-weather observations. In this study, we attempted to develop a method for the early season mapping of sugarcane. First, we proposed a framework consisting of two procedures: initial sugarcane mapping using the S1A SAR imagery time series, followed by non-vegetation removal using Sentinel-2 optical imagery. Second, we tested the framework using an incremental classification strategy based on S1A imagery covering the entire 2017–2018 sugarcane season. The study area was in Suixi and Leizhou counties of Zhanjiang city, China. Results indicated that an acceptable accuracy, in terms of Kappa coefficient, can be achieved to a level above 0.902 using time series three months before sugarcane harvest. In general, sugarcane mapping utilizing the combination of VH + VV as well as VH polarization alone outperformed mapping using VV alone. Although the XGBoost classifier with VH + VV polarization achieved a maximum accuracy that was slightly lower than the random forest (RF) classifier, the XGBoost shows promising performance in that it was more robust to overfitting with noisy VV time series and the computation speed was 7.7 times faster than RF classifier. The total sugarcane areas in Suixi and Leizhou for the 2017–2018 harvest year estimated by this study were approximately 598.95 km2 and 497.65 km2, respectively. The relative accuracy of the total sugarcane mapping area was approximately 86.3%.

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Section: Incremental Classification Accuracymentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China

Jiang

Jing

et al. 2019

Remote Sensing

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“…This criterion is considered in the analyses of the ground measurements and radar images. Rice phenology can be divided into three main phases: vegetative, reproductive, and maturity, according to the widely-used Biologische Bundesanstalt, Bundessortenamt und CHemische scale (a German scale used to identify the phenological development stages of cereals, BBCH) [13,21,38]. The principal phases and corresponding numerical ranges are shown in Table 1.…”

Section: Rice Phenologymentioning

confidence: 99%

“…Conventional ground-based rice phenology monitoring provides accurate in situ information if properly designed and executed. The monitoring is, unfortunately, linked to enormous costs of time, money, and man-power [12,13], and is not practical at a large spatial extent and for long-term monitoring and analysis. During the past decades, a series is caused by changes of the electromagnetic interactions between radar waves and rice scattering components as the plant grows and different polarization responses to the changes [20].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets

Yong

et al. 2018

Remote Sensing

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Accurate estimation and monitoring of rice phenology is necessary for the management and yield prediction of rice. The radar backscattering coefficient, one of the most direct and accessible parameters has been proved to be capable of retrieving rice growth parameters. This paper aims to investigate the possibility of monitoring the rice phenology (i.e., transplanting, vegetative, reproductive, and maturity) using the backscattering coefficients or their simple combinations of multi-temporal RADARSAT-2 datasets only. Four RADARSAT-2 datasets were analyzed at 30 sample plots in Meishan City, Sichuan Province, China. By exploiting the relationships of the backscattering coefficients and their combinations versus the phenology of rice, HH/VV, VV/VH, and HH/VH ratios were found to have the greatest potential for phenology monitoring. A decision tree classifier was applied to distinguish the four phenological phases, and the classifier was effective. The validation of the classifier indicated an overall accuracy level of 86.2%. Most of the errors occurred in the vegetative and reproductive phases. The corresponding errors were 21.4% and 16.7%, respectively.

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“…A detailed review of the Italian funded COnstellation of small Satellites for Mediterranean basin Observation (COSMO-SkyMed) mission by Covello et al (2010) illustrates the benefits of this system in providing commercial products and services for agricultural applications alongside environmental risk management. COSMO-SkyMed imagery have been successfully used in the retrieval and monitoring of vegetation parameters over agricultural land (Santi et al, 2012), land use discrimination and land change detection analysis (Shu-cheng et al, 2011), risk management applications (Battazza et al, 2012), and rice crop growth monitoring (Corcione et al, 2016). Kramer and Cracknell (2008) discussed the potential use of different spaceborne small satellite sensors, such as the Chinese Environment and Disaster Monitoring Satellites constellation (HJ-1), for remote sensing applications.…”

Section: Radar Based Satellite Sensors For Agriculturementioning

confidence: 99%

Applications of satellite ‘hyper-sensing’ in Chinese agriculture: Challenges and opportunities

Onojeghuo

Blackburn

Huang

et al. 2018

International Journal of Applied Earth Observation and Geoinfor

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Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of 'hyper-sensing' (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite 'hyper-sensing' to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite 'hyper-sensing' in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and ongoing projects in China that are developing 'hyper-sensing' approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China.

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A study of the use of COSMO-SkyMed SAR PingPong polarimetric mode for rice growth monitoring

Cited by 26 publications

References 17 publications

Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China

Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China

Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets

Applications of satellite ‘hyper-sensing’ in Chinese agriculture: Challenges and opportunities

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