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Lin Zhang
College of Land Science and Technology, China Agricultural University, Beijing 100083, China

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Journal article
Published: 14 April 2021 in Remote Sensing
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Sentinel-2 images have been widely used in studying land surface phenomena and processes, but they inevitably suffer from cloud contamination. To solve this critical optical data availability issue, it is ideal to fuse Sentinel-1 and Sentinel-2 images to create fused, cloud-free Sentinel-2-like images for facilitating land surface applications. In this paper, we propose a new data fusion model, the Multi-channels Conditional Generative Adversarial Network (MCcGAN), based on the conditional generative adversarial network, which is able to convert images from Domain A to Domain B. With the model, we were able to generate fused, cloud-free Sentinel-2-like images for a target date by using a pair of reference Sentinel-1/Sentinel-2 images and target-date Sentinel-1 images as inputs. In order to demonstrate the superiority of our method, we also compared it with other state-of-the-art methods using the same data. To make the evaluation more objective and reliable, we calculated the root-mean-square-error (RSME), R2, Kling–Gupta efficiency (KGE), structural similarity index (SSIM), spectral angle mapper (SAM), and peak signal-to-noise ratio (PSNR) of the simulated Sentinel-2 images generated by different methods. The results show that the simulated Sentinel-2 images generated by the MCcGAN have a higher quality and accuracy than those produced via the previous methods.

ACS Style

Quan Xiong; Liping Di; Quanlong Feng; Diyou Liu; Wei Liu; XuLi Zan; Lin Zhang; Dehai Zhu; Zhe Liu; Xiaochuang Yao; Xiaodong Zhang. Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network. Remote Sensing 2021, 13, 1512 .

AMA Style

Quan Xiong, Liping Di, Quanlong Feng, Diyou Liu, Wei Liu, XuLi Zan, Lin Zhang, Dehai Zhu, Zhe Liu, Xiaochuang Yao, Xiaodong Zhang. Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network. Remote Sensing. 2021; 13 (8):1512.

Chicago/Turabian Style

Quan Xiong; Liping Di; Quanlong Feng; Diyou Liu; Wei Liu; XuLi Zan; Lin Zhang; Dehai Zhu; Zhe Liu; Xiaochuang Yao; Xiaodong Zhang. 2021. "Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network." Remote Sensing 13, no. 8: 1512.

Journal article
Published: 29 October 2020 in ISPRS International Journal of Geo-Information
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The accurate and timely access to the spatial distribution information of crops is of great importance for agricultural production management. Although widely used, supervised classification mapping requires a large number of field samples, and is consequently costly in terms of time and money. In order to reduce the need for sample size, this paper proposes an unsupervised classification method based on principal components isometric binning (PCIB). In particular, principal component analysis (PCA) dimensionality reduction is applied to the classification features, followed by the division of the top k principal components into equidistant bins. Bins of the same category are subsequently merged as a class label. Multitemporal Gaofen 1 satellite (GF-1) remote sensing images were collected over the southwest of Hulin City and Luobei County of Hegang City, Heilongjiang Province, China in order to map crop types in 2016 and 2017. Our proposed method was compared with commonly used classifiers (random forest, K-means and Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA)). Results demonstrate PCIB and random forest to have the highest classification accuracies, reaching 82% in 2016 in the southwest of Hulin City. In Luobei County in 2016, the accuracies of PCIB and random forest were determined as 81% and 82%, respectively. It can be concluded that the overall accuracy of our proposed method meets the basic requirements of classification accuracy. Despite exhibiting a lower accuracy than that of random forest, PCIB does not require a large field sample size, thus making it more suitable for large-scale crop mapping.

ACS Style

Zhe Ma; Zhe Liu; Yuanyuan Zhao; Lin Zhang; Diyou Liu; Tianwei Ren; Xiaodong Zhang; Shaoming Li. An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning. ISPRS International Journal of Geo-Information 2020, 9, 648 .

AMA Style

Zhe Ma, Zhe Liu, Yuanyuan Zhao, Lin Zhang, Diyou Liu, Tianwei Ren, Xiaodong Zhang, Shaoming Li. An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning. ISPRS International Journal of Geo-Information. 2020; 9 (11):648.

Chicago/Turabian Style

Zhe Ma; Zhe Liu; Yuanyuan Zhao; Lin Zhang; Diyou Liu; Tianwei Ren; Xiaodong Zhang; Shaoming Li. 2020. "An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning." ISPRS International Journal of Geo-Information 9, no. 11: 648.

Journal article
Published: 03 July 2020 in Remote Sensing
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Accurate and timely access to the production area of crop seeds allows the seed market and secure seed supply to be monitored. Seed maize and common maize production fields typically share similar phenological development profiles with differences in the planting patterns, which makes it challenging to separate these fields from decametric-resolution satellite images. In this research, we proposed a method to identify seed maize production fields as early as possible in the growing season using a time series of remote sensing images in the Liangzhou district of Gansu province, China. We collected Sentinel-2 and GaoFen-1 (GF-1) images captured from March to September. The feature space for classification consists of four original bands, namely red, green, blue, and near-infrared (nir), and eight vegetation indexes. We analyzed the timeliness of seed maize identification using Sentinel-2 time series of different time spans and identified the earliest time frame for reasonable classification accuracy. Then, the earliest time series that met the requirements of regulatory accuracy were compared and analyzed. Four machine/deep learning algorithms were tested, including K-nearest neighbor (KNN), support vector classification (SVC), random forest (RF), and long short-term memory (LSTM). The results showed that using Sentinel-2 images from March to June, the RF and LSTM algorithms achieve over 88% accuracy, with the LSTM performing the best (90%). In contrast, the accuracy of KNN and SVC was between 82% and 86%. At the end of June, seed maize mapping can be carried out in the experimental area, and the precision can meet the basic requirements of monitoring for the seed industry. The classification using GF-1 images were less accurate and reliable; the accuracy was 85% using images from March to June. To achieve near real-time identification of seed maize fields early in the growing season, we adopted an automated sample generation approach for the current season using only historical samples based on clustering analysis. The classification accuracy using new samples extracted from historical mapping reached 74% by the end of the season (September) and 63% by the end of July. This research provides important insights into the classification of crop fields cultivated with the same crop but different planting patterns using remote sensing images. The approach proposed by this study enables near-real time identification of seed maize production fields within the growing season, which could effectively support large-scale monitoring of the seed supply industry.

ACS Style

Tianwei Ren; Zhe Liu; Lin Zhang; Diyou Liu; Xiaojie Xi; Yanghui Kang; Yuanyuan Zhao; Chao Zhang; Shaoming Li; Xiaodong Zhang. Early Identification of Seed Maize and Common Maize Production Fields Using Sentinel-2 Images. Remote Sensing 2020, 12, 2140 .

AMA Style

Tianwei Ren, Zhe Liu, Lin Zhang, Diyou Liu, Xiaojie Xi, Yanghui Kang, Yuanyuan Zhao, Chao Zhang, Shaoming Li, Xiaodong Zhang. Early Identification of Seed Maize and Common Maize Production Fields Using Sentinel-2 Images. Remote Sensing. 2020; 12 (13):2140.

Chicago/Turabian Style

Tianwei Ren; Zhe Liu; Lin Zhang; Diyou Liu; Xiaojie Xi; Yanghui Kang; Yuanyuan Zhao; Chao Zhang; Shaoming Li; Xiaodong Zhang. 2020. "Early Identification of Seed Maize and Common Maize Production Fields Using Sentinel-2 Images." Remote Sensing 12, no. 13: 2140.

Journal article
Published: 22 January 2020 in Remote Sensing
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Seed maize and common maize plots have different planting patterns and variety types. Identification of seed maize is the basis for seed maize growth monitoring, seed quality and common maize seed supply. In this paper, a random forest (RF) classifier is used to develop an approach for seed maize fields’ identification, using the time series vegetation indexes (VIs) calculated from multispectral data acquired from Landsat 8 and Gaofen 1 satellite (GF-1), field sample data, and texture features of Gaofen 2 satellite (GF-2) panchromatic data. Huocheng and Hutubi County in the Xinjiang Uygur Autonomous Region of China were chosen as study area. The results show that RF performs well with the combination of six VIs (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), triangle vegetation index (TVI), ratio vegetation index (RVI), normalized difference water index (NDWI) and difference vegetation index (DVI)) and texture features based on a grey-level co-occurrence matrix. The classification based on “spectrum + texture” information has higher overall, user and producer accuracies than that of spectral information alone. Using the “spectrum + texture” method, the overall accuracy of classification in Huocheng County is 95.90%, the Kappa coefficient is 0.92, and the producer accuracy for seed maize fields is 93.91%. The overall accuracy of the classification in Hutubi County is 97.79%, the Kappa coefficient is 0.95, and the producer accuracy for seed maize fields is 97.65%. Therefore, RF classifier inputted with high-resolution remote-sensing image features can distinguish two kinds of planting patterns (seed and common) and varieties types (inbred and hybrid) of maize and can be used to identify and map a wide range of seed maize fields. However, this method requires a large amount of sample data, so how to effectively use and improve it in areas lacking samples needs further research.

ACS Style

Lin Zhang; Zhe Liu; Tianwei Ren; Diyou Liu; Zhe Ma; Liang Tong; Chao Zhang; Tianying Zhou; Xiaodong Zhang; Shaoming Li. Identification of Seed Maize Fields With High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier. Remote Sensing 2020, 12, 362 .

AMA Style

Lin Zhang, Zhe Liu, Tianwei Ren, Diyou Liu, Zhe Ma, Liang Tong, Chao Zhang, Tianying Zhou, Xiaodong Zhang, Shaoming Li. Identification of Seed Maize Fields With High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier. Remote Sensing. 2020; 12 (3):362.

Chicago/Turabian Style

Lin Zhang; Zhe Liu; Tianwei Ren; Diyou Liu; Zhe Ma; Liang Tong; Chao Zhang; Tianying Zhou; Xiaodong Zhang; Shaoming Li. 2020. "Identification of Seed Maize Fields With High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier." Remote Sensing 12, no. 3: 362.

Journal article
Published: 22 November 2019 in Sustainability
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Currently, high-temperature risk assessments of crops at the regional scale are usually conducted by comparing the observed air temperature at ground stations or via the remote sensing inversion of canopy temperature (such as MODIS (moderate-resolution imaging spectroradiometer) land surface temperature (LST)) with the threshold temperature of the crop. Since this threshold is based on the absolute temperature value, it is difficult to account for changes in environmental conditions and crop canopy information between different regions and different years in the evaluation model. In this study, MODIS LST products were used to establish an evaluation model (spatiotemporal deviation mean (STDM)) and a classification method to determine maize-growing areas at risk of high temperatures at the regional scale. The study area was the Huang-Huai-Hai River plain of China where maize is grown and high temperatures occur frequently. The spatiotemporal distribution of the high-temperature risk of summer maize was determined in the study area from 2003 to 2018. The results demonstrate the applicability of the model at the regional scale. The distribution of high-temperature risk in the Huang-Huai-Hai region was consistent with the actual temperature measurements. The temperatures in the northwestern, southwestern, and southern parts were relatively high and the area was classified as a stable zone. Shijiazhuang, Jiaozuo, Weinan, Xi’an, and Xianyang city were located in a zone of increasing high temperatures. The regions with a stable high-temperature risk were Xiangfan, Yuncheng, and Luoyang city. Areas of decreasing high temperatures were Handan, Xingtai, Bozhou, Fuyang, Nanyang, Linfen, and Pingdingshan city. Areas that need to focus on preventing high-temperature risks include Luoyang, Yuncheng, Xianyang, Weinan, and Xi’an city. This study provides a new method for the detailed evaluation of regional high-temperature risk and data support.

ACS Style

Xinlei Hu; Zuliang Zhao; Lin Zhang; Zhe Liu; Shaoming Li; Xiaodong Zhang. A High-Temperature Risk Assessment Model for Maize Based on MODIS LST. Sustainability 2019, 11, 6601 .

AMA Style

Xinlei Hu, Zuliang Zhao, Lin Zhang, Zhe Liu, Shaoming Li, Xiaodong Zhang. A High-Temperature Risk Assessment Model for Maize Based on MODIS LST. Sustainability. 2019; 11 (23):6601.

Chicago/Turabian Style

Xinlei Hu; Zuliang Zhao; Lin Zhang; Zhe Liu; Shaoming Li; Xiaodong Zhang. 2019. "A High-Temperature Risk Assessment Model for Maize Based on MODIS LST." Sustainability 11, no. 23: 6601.

Journal article
Published: 16 September 2019 in Sustainability
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Accurate, year-by-year crop distribution information is a key element in agricultural production regulation and global change governance. However, due to the high sampling costs and insufficient use of historical samples, a supervised classifying method for sampling every year is unsustainable for mapping crop types over time. Therefore, this paper proposes a method for the generation and screening of new samples for 2018 based on historical crop samples, and then it builds a crop mapping model for that current season. Pixels with the same crop type in the historical year (2013–2017) were extracted as potential samples, and their spectral features and spatial information in the current year (2018) were used to generate new samples based on clustering screening. The research result shows that when the clustering number is different, the number and structure of new generated sample also changes. The sample structure generated in Luobei County was not balanced, with the ‘other crop’ representing less than 3.97%, but the structure of southwest Hulin City was more balanced. Based on the newly generated samples and the ground reference data of classified year, the classification models were constructed. The average classification accuracies of Luobei County in 2018 based on new generated samples and field samples were 69.35% and 77.59%, respectively, while those of southwest Hulin City were 80.44% and 82.94%, respectively. Combined with historical samples and the spectral information of the current year, this study proposes a method to generate new samples. It can overcome the problem of crop samples only being collected in the field due to the difficulty of visual interpretation, effectively improve the use of historical data, and also provide a new idea for sustainable crop mapping in many regions lacking seasonal field samples.

ACS Style

Lin Zhang; Zhe Liu; Diyou Liu; Quan Xiong; Ning Yang; Tianwei Ren; Chao Zhang; Xiaodong Zhang; Shaoming Li. Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China. Sustainability 2019, 11, 5052 .

AMA Style

Lin Zhang, Zhe Liu, Diyou Liu, Quan Xiong, Ning Yang, Tianwei Ren, Chao Zhang, Xiaodong Zhang, Shaoming Li. Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China. Sustainability. 2019; 11 (18):5052.

Chicago/Turabian Style

Lin Zhang; Zhe Liu; Diyou Liu; Quan Xiong; Ning Yang; Tianwei Ren; Chao Zhang; Xiaodong Zhang; Shaoming Li. 2019. "Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China." Sustainability 11, no. 18: 5052.

Journal article
Published: 25 June 2019 in Remote Sensing
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Large-scale crop mapping provides important information in agricultural applications. However, it is a challenging task due to the inconsistent availability of remote sensing data caused by the irregular time series and limited coverage of the images, together with the low spatial resolution of the classification results. In this study, we proposed a new efficient method based on grids to address the inconsistent availability of the high-medium resolution images for large-scale crop classification. First, we proposed a method to block the remote sensing data into grids to solve the problem of temporal inconsistency. Then, a parallel computing technique was introduced to improve the calculation efficiency on the grid scale. Experiments were designed to evaluate the applicability of this method for different high-medium spatial resolution remote sensing images and different machine learning algorithms and to compare the results with the widely used nonparallel method. The computational experiments showed that the proposed method was successful at identifying large-scale crop distribution using common high-medium resolution remote sensing images (GF-1 WFV images and Sentinel-2) and common machine learning classifiers (the random forest algorithm and support vector machine). Finally, we mapped the croplands in Heilongjiang Province in 2015, 2016, 2017, which used a random forest classifier with the time series GF-1 WFV images spectral features, the enhanced vegetation index (EVI) and normalized difference water index (NDWI). Ultimately, the accuracy was assessed using a confusion matrix. The results showed that the classification accuracy reached 88%, 82%, and 85% in 2015, 2016, and 2017, respectively. In addition, with the help of parallel computing, the calculation speed was significantly improved by at least seven-fold. This indicates that using the grid framework to block the data for classification is feasible for crop mapping in large areas and has great application potential in the future.

ACS Style

Ning Yang; Diyou Liu; Quanlong Feng; Quan Xiong; Lin Zhang; Tianwei Ren; Yuanyuan Zhao; Dehai Zhu; Jianxi Huang. Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids. Remote Sensing 2019, 11, 1500 .

AMA Style

Ning Yang, Diyou Liu, Quanlong Feng, Quan Xiong, Lin Zhang, Tianwei Ren, Yuanyuan Zhao, Dehai Zhu, Jianxi Huang. Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids. Remote Sensing. 2019; 11 (12):1500.

Chicago/Turabian Style

Ning Yang; Diyou Liu; Quanlong Feng; Quan Xiong; Lin Zhang; Tianwei Ren; Yuanyuan Zhao; Dehai Zhu; Jianxi Huang. 2019. "Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids." Remote Sensing 11, no. 12: 1500.