Estimating winter wheat area based on an SVM and the variable fuzzy set method

Hao, Zhen; Zhao, Hongli; Zhang, Chi; Wang, Hao; Jiang, Yunzhong; Yi, Zhenyan

doi:10.1080/2150704x.2018.1552811

Cited by 10 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These crop maps can assist decision-makers and end-users in identifying the crop areas and estimating the biomass production, irrigation needs, water productivity, and scheduling management strategies [7]. Winter crops are one of the major grain crops in China, as well as all around the world [8][9][10]. Obtaining a timely and precise map of the winter crop plantings is critical for China and the rest of the globe [7,11].…”

Section: Introductionmentioning

confidence: 99%

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

et al. 2019

View full text Add to dashboard Cite

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows-a period of low NDVI values and a period of high NDVI values-for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km 2 , with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.Resolution Imaging Spectroradiometer (MODIS) Land Cover Collections [12], and the 1 km Global Land Cover 2000 (GLC2000) [13]). However, more specific information on winter crops is still limited in existing land-cover products [2,14]. On the other hand, the mapping of winter crop planting areas using the traditional agricultural census approach requires a good deal of manpower, money, and time [15]. Further, the timeliness of the traditional approach is poor. For example, the Chinese government generally publishes this information at least six months after the crops have been harvested. Furthermore, the quality of the survey data tends to suffer from the influence of human errors [3].Remote sensing techniques provide a very effective means to map crops [2,11,16,17], due to their fast responses, periodic observations, wide field of view, and low cost [18][19][20]. However, there are still some challenges associated with large-scale (e.g., provincial-or national-scale) winter crop mapping by remote sensing. MODIS data was the major data source for large-scale crop mapping in previous studies, due to its high revisit...

show abstract

Section: Introductionmentioning

confidence: 99%

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In addition, in order to prove the reliability and applicability of the two methods used in this study. Other methods, such as regression tree [30,63], support vector machine [64,65] classifier, FastFCN [66], DeeplabV3+ [67] semantic segmentation network in deep learning, were also used to evaluate the accuracy of winter wheat identification at jointing-heading period, and the results are shown in Tables 6 and 7. The results showed that the random forest classifier had the highest accuracy compared with other classifiers, and the performance of the U-Net network in winter wheat semantic segmentation under a small sample data was better than other networks.…”

Section: The Superiority Of Classification Methodsmentioning

confidence: 99%

The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing

et al. 2022

View full text Add to dashboard Cite

The aim of this study was to explore the differences in the accuracy of winter wheat identification using remote sensing data at different growth stages using the same methods. Part of northern Henan Province, China was taken as the study area, and the winter wheat growth cycle was divided into five periods (seeding‒tillering, overwintering, reviving, jointing‒heading, and flowering‒maturing) based on monitoring data obtained from agrometeorological stations. With the help of the Google Earth Engine (GEE) platform, the separability between winter wheat and other land cover types was analyzed and compared using the Jeffries‒Matusita (J‒M) distance method. Spectral features, vegetation index, water index, building index, texture features, and terrain features were generated from Sentinel-2 remote sensing images at different growth periods, and then were used to establish a random forest classification and extraction model. A deep U-Net semantic segmentation model based on the red, green, blue, and near-infrared bands of Sentinel-2 imagery was also established. By combining models with field data, the identification of winter wheat was carried out and the difference between the accuracy of the identification in the five growth periods was analyzed. The experimental results show that, using the random forest classification method, the best separability between winter wheat and the other land cover types was achieved during the jointing‒heading period: the overall identification accuracy for the winter wheat was then highest at 96.90% and the kappa coefficient was 0.96. Using the deep-learning classification method, it was also found that the semantic segmentation accuracy of winter wheat and the model performance were best during the jointing‒heading period: a precision, recall, F1 score, accuracy, and IoU of 0.94, 0.93, 0.93, and 0.88, respectively, were achieved for this period. Based on municipal statistical data for winter wheat, the accuracy of the extraction of the winter wheat area using the two methods was 96.72% and 88.44%, respectively. Both methods show that the jointing‒heading period is the best period for identifying winter wheat using remote sensing and that the identification made during this period is reliable. The results of this study provide a scientific basis for accurately obtaining the area planted with winter wheat and for further studies into winter wheat growth monitoring and yield estimation.

show abstract

“…3. Due to the low revisit frequency of the high-resolution remote sensors, such as Landsat 8 OLI (16 days) [7] and GF-1 PMS (41 days) [17], MODIS data with high revisit frequency were used in this work. The time-series MODIS EVI data were masked by the maize distribution in the reference area, and the time-series EVI maize data were obtained.…”

Section: Methods 1) Flowchart Of the Proposed Methodsmentioning

confidence: 99%

Developing a Method to Estimate Maize Area in North and Northeast of China Combining Crop Phenology Information and Time-Series MODIS EVI

et al. 2019

View full text Add to dashboard Cite

Remote sensing (RS) is a convenient technology to estimate the regional cultivation areas of crops. However, the accurate estimation of maize areas using RS over a broad region is a significant challenge due to the large phenology differences and insufficient prior knowledge in space. To address this issue, a new method was developed in this work. In this method, the correlation (r) and root mean standard error (RMSE) between the time-series moderate resolution imaging spectroradiometer enhanced vegetation index (MODIS EVI) and the standard EVI curve of maize from a reference area are computed. Pixels with a high value of r and a low value of RMSE were identified as maize areas. The phenology information observed at agro-meteorological stations was also used to recognize maize pixels from the pixel-level phenology derived from time-series MODIS EVI. The proposed method provides an accurate characterization of the phenology differences over the study area by making use of the planting and maturity dates only. In addition, the few location-dependent parameters make the recognition of maize planting areas over large regions easier than previous studies. The proposed method was implemented over the Northeast China Plain (NECP) and North China Plain (NCP). The derived results were compared with official statistical results, and a close agreement was observed. At the city level, the satellite-derived estimates agreed well with the statistics with the R 2 (RMSE) of 0.86(110.97 k hm 2 ) in the NECP and 0.76(68.74 k hm 2 ) in the NCP. At the county level, the R 2 (RMSE) is 0.82(25.47 k hm 2 ) in the NECP and 0.75(5.93 k hm 2 ) in the NCP. At both temporal levels, the R 2 (RMSE) results obtained in this work are higher(lower) than those published in other studies. The obtained results indicate that the proposed method is effective in maize area estimation over broad regions.

show abstract

Estimating winter wheat area based on an SVM and the variable fuzzy set method

Cited by 10 publications

References 11 publications

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing

Developing a Method to Estimate Maize Area in North and Northeast of China Combining Crop Phenology Information and Time-Series MODIS EVI

Contact Info

Product

Resources

About