Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines

Son, N. T.; Chen, Chi Farn; Chen, Cheng Ru; Minh, Võ Quang

doi:10.1080/10106049.2017.1289555

Cited by 58 publications

(57 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The overall classification accuracy for the maize field was higher with RF in all the datasets compared with SVM. However, they were not statistically significant, as indicated by the McNemar test (Table 6); similar results elsewhere had shown that RF usually outperforms SVM when applied in the different land-cover mapping [67].…”

Section: Discussionsupporting

confidence: 65%

Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa

et al. 2020

View full text Add to dashboard Cite

A timely and accurate crop type mapping is very significant, and a prerequisite for agricultural regions and ensuring global food security. The combination of remotely sensed optical and radar datasets presents an opportunity for acquiring crop information at relative spatial resolution and temporal resolution adequately to capture the growth profiles of various crop species. In this paper, we employed Sentinel-1A (S-1) and Sentinel-2A (S-2) data acquired between the end of June and early September 2016, on a semi-arid area in northern Nigeria. A different set of (VV and VH) SAR and optical (SI and SB) images, illustrating crop phenological development stage, were employed as inputs to the two machines learning Random Forest (RF) and Support Vector Machine (SVM) algorithms to automatically map maize fields. Significant increases in overall classification were shown when the multi-temporal spectral indices (SI) and spectral band (SB) datasets were added with the different integration of SAR datasets (i.e., VV and VH). The best overall accuracy (OA) for maize (96.93%) was derived by using RF classification algorithms with SI-SB-SAR datasets, although the SI datasets for RF and SB datasets for SVM also produced high overall maize classification accuracies, of 97.04% and 97.44%. The outcomes indicate the robustness of the RF or SVM methods to produce high-resolution maps of maize for subsequent application from agronomists, policy planners, and the government, because such information is lacking in our study area.

show abstract

Section: Discussionsupporting

confidence: 65%

Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Previous studies in crop mapping have demonstrated that, compared with state of the art classifiers i.e., Support Vector Machines with Gaussian kernel, the RF does not only produce high accuracy, but also features with fast computation and easy parameters tuning [8,22]. Several studies have investigated the use of the RF classifier for rice mapping with SAR datasets [22][23][24]. The XGBoost is a scalable and flexible gradient-boosting classification method, which uses more regularized model formalization to control over-fitting, which gives it better performance.…”

Section: Feature Importance Evaluationmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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%.

show abstract

“…With the advantages of small samples and nonlinearity, SVR is widely used in medical [30,31], energy consumption [32][33][34], transportation [35][36][37], environmental protection [38][39][40], agriculture [41,42], and so on. Concerning crop and biomass applications, previous studies have used multitemporal Sentinel-1A for rice crop classification [43] or to estimate the rice height and dry biomass [44]. The nonlinear fitting of SVR may derive better results than the linear ones.…”

Section: Introductionmentioning

confidence: 99%

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Shiu

Chuang

2019

Remote Sensing

View full text Add to dashboard Cite

Precisely estimating the yield of paddy rice is crucial for national food security and development evaluation. Rice yield estimation based on satellite imagery is usually performed with global regression models; however, estimation errors may occur because the spatial variation is not considered. Therefore, this study proposed an approach estimating paddy rice yield based on global and local regression models. In our study area, the overall per-field data might not available because it took lots of time and manpower as well as resources. Therefore, we gathered and accumulated 26 to 63 ground survey sample fields, accounting for about 0.05% of the total cultivated areas, as the training samples for our regression models. To demonstrate whether the spatial autocorrelation or spatial heterogeneity exists and dominates the estimation, global models including the ordinary least squares (OLS), support vector regression (SVR), and the local model geographically weighted regression (GWR) were used to build the yield estimation models. We obtained the representative independent variables, including 4 original bands, 11 vegetation indices, and 32 texture indices, from SPOT-7 multispectral satellite imagery. To determine the optimal variable combination, feature selection based on the Pearson correlation was used for all of the regression models. The case study in Central Taiwan rendered that the error rate was between 0.06% and 13.22%. Through feature selection, the GWR model’s performance was more relatively stable than the OLS model and nonlinear SVR model for yield estimation. Where the GWR model considers the spatial autocorrelation and spatial heterogeneity of the relationships between the yield and the independent variables, the OLS and nonlinear SVR models lack this feature; this led to the rice yield estimation of GWR in this study be more stable than those of the other two models.

show abstract

Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines

Cited by 58 publications

References 53 publications

Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa

Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa

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

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Contact Info

Product

Resources

About