Rice biomass retrieval from multitemporal ground-based scatterometer data and RADARSAT-2 images using neural networks

Jia, Mingquan; Tong, Ling; Chen, Yan; Wang, Yong; Zhang, Yuanzhi

doi:10.1117/1.jrs.7.073509

Cited by 30 publications

(17 citation statements)

References 27 publications

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“…In another study, Jia et al, [77] utilized the artificial neural network algorithm to invert the biomass of Paddy fields from the quad-polarized RADARSAT-2 datasets. The network trained with the backscattering data simulated using the Monte Carlo simulation model gave the accuracy as high as 0.989 and RMSE of 0.477 kg/m 2 when the inverted biomass was compared to the measured biomass values.…”

Section: Fresh Biomassmentioning

confidence: 99%

Advances in Radar Remote Sensing of Agricultural Crops: A Review

Sivasankar

Kumar

Srivastava

et al. 2018

International Journal on Advanced Science, Engineering and Information Technology

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There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field. Keywords-biophysical parameters retrieval; crop inventory; synthetic aperture radar (SAR); synthetic aperture radar interferometry (InSAR); SAR polarimetry (PolSAR); polarimetric SAR interferometry (PolInSAR).

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Section: Fresh Biomassmentioning

confidence: 99%

Advances in Radar Remote Sensing of Agricultural Crops: A Review

Sivasankar

Kumar

Srivastava

et al. 2018

International Journal on Advanced Science, Engineering and Information Technology

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“…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%

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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“…The backscatter images, acquired by the C-band SAR onboard ERS-1 with a repeat time of 35 days, were used in numerous SAR-based multi-temporal backscatter analyses for rice mapping case studies in Thailand [22], Indonesia [21], Japan [21,23,24], Vietnam [29], Canada [30,31] and India [25,26,32,33]. Methods developed for ERS-1 SAR data have been advanced and successfully transferred to RADARSAT-1 acquisitions at C-band with horizontal co-polarization and a 24-day repeat cycle [19,[34][35][36][37][38][39].…”

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confidence: 99%

Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data

et al. 2015

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Abstract:Rice is the most important food crop in Asia, and the timely mapping and monitoring of paddy rice fields subsequently emerged as an important task in the context of food security and modelling of greenhouse gas emissions. Rice growth has a distinct influence on Synthetic Aperture Radar (SAR) backscatter images, and time-series analysis of C-band images has been successfully employed to map rice fields. The poor data availability on regional scales is a major drawback of this method. We devised an approach to classify paddy rice with the use of all available Envisat ASAR WSM (Advanced Synthetic Aperture Radar Wide Swath Mode) data for our study area, the Mekong Delta in Vietnam. We used regression-based incidence angle normalization and temporal averaging to combine acquisitions from multiple tracks and years. A crop phenology-based classifier has been applied to this time series to detect single-, double-and triple-cropped rice areas (one to three harvests per year), as well as dates and lengths of growing seasons. Our classification has an overall accuracy of 85.3% and a kappa coefficient of 0.74 compared to a reference dataset and correlates highly with official rice area statistics at the provincial level (R² of 0.98). SAR-based time-series analysis allows accurate mapping and monitoring of rice areas even under adverse atmospheric conditions.

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Rice biomass retrieval from multitemporal ground-based scatterometer data and RADARSAT-2 images using neural networks

Cited by 30 publications

References 27 publications

Advances in Radar Remote Sensing of Agricultural Crops: A Review

Advances in Radar Remote Sensing of Agricultural Crops: A Review

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

Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data

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