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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), R
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 StyleQuan 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 StyleQuan 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.
Typhoon disaster information is characterized by multiple sources, complexity and diversity, and different users of this content have different individual concerns. The expression methods of typhoon disaster information considered in previous research have been relatively simple, which cannot meet the personalized service needs of different users. In this paper, according to the diverse content needs of different users in distinct stages of a typhoon disaster, a typhoon disaster information expression method with a multi-user, multi-stage, multi-channel and multi-element combined mode is investigated. First, the audience and disaster stages are divided via demand analysis, and the demand content is summarized according to the users, stages, and release channels. Similar information is then integrated into the same theme, and it is also determined how information is expressed. Then, the analytic hierarchy process (AHP) is used to filter out the important information in each theme. The theme template is then designed according to the characteristics of particular release channels. Finally, a prototype system is developed, and Typhoon Lekima, which impacted China in 2019, is considered as a real case for analysis. The results show that the proposed method can effectively support different users to obtain disaster characteristics at distinct stages of typhoon disasters, evaluate disaster conditions, assist scientific decision-making, and enhance public awareness of risk prevention.
Cong Xiao; Xiaodong Zhang; Ziyao Xing; Keke Han; Zhe Liu; Junming Liu. Investigation of the Expression Method of Theme-Typhoon Disaster Information. ISPRS International Journal of Geo-Information 2021, 10, 109 .
AMA StyleCong Xiao, Xiaodong Zhang, Ziyao Xing, Keke Han, Zhe Liu, Junming Liu. Investigation of the Expression Method of Theme-Typhoon Disaster Information. ISPRS International Journal of Geo-Information. 2021; 10 (3):109.
Chicago/Turabian StyleCong Xiao; Xiaodong Zhang; Ziyao Xing; Keke Han; Zhe Liu; Junming Liu. 2021. "Investigation of the Expression Method of Theme-Typhoon Disaster Information." ISPRS International Journal of Geo-Information 10, no. 3: 109.
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.
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 StyleZhe 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 StyleZhe 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.
Recently, increasing amounts of multi-source geospatial data (raster data of satellites and textual data of meteorological stations) have been generated, which can play a cooperative and important role in many research works. Efficiently storing, organizing and managing these data is essential for their subsequent application. HBase, as a distributed storage database, is increasingly popular for the storage of unstructured data. The design of the row key of HBase is crucial to improving its efficiency, but large numbers of researchers in the geospatial area do not conduct much research on this topic. According the HBase Official Reference Guide, row keys should be kept as short as is reasonable while remaining useful for the required data access. In this paper, we propose a new row key encoding method instead of conventional stereotypes. We adopted an existing hierarchical spatio-temporal grid framework as the row key of the HBase to manage these geospatial data, with the difference that we utilized the obscure but short American Standard Code for Information Interchange (ASCII) to achieve the structure of the grid rather than the original grid code, which can be easily understood by humans but is very long. In order to demonstrate the advantage of the proposed method, we stored the daily meteorological data of 831 meteorological stations in China from 1985 to 2019 in HBase; the experimental result showed that the proposed method can not only maintain an equivalent query speed but can shorten the row key and save storage resources by 20.69% compared with the original grid codes. Meanwhile, we also utilized GF-1 imagery to test whether these improved row keys could support the storage and querying of raster data. We downloaded and stored a part of the GF-1 imagery in Henan province, China from 2017 to 2018; the total data volume reached about 500 GB. Then, we succeeded in calculating the daily normalized difference vegetation index (NDVI) value in Henan province from 2017 to 2018 within 54 min. Therefore, the experiment demonstrated that the improved row keys can also be applied to store raster data when using HBase.
Quan Xiong; Xiaodong Zhang; Wei Liu; Sijing Ye; Zhenbo Du; Diyou Liu; Dehai Zhu; Zhe Liu; Xiaochuang Yao. An Efficient Row Key Encoding Method with ASCII Code for Storing Geospatial Big Data in HBase. ISPRS International Journal of Geo-Information 2020, 9, 625 .
AMA StyleQuan Xiong, Xiaodong Zhang, Wei Liu, Sijing Ye, Zhenbo Du, Diyou Liu, Dehai Zhu, Zhe Liu, Xiaochuang Yao. An Efficient Row Key Encoding Method with ASCII Code for Storing Geospatial Big Data in HBase. ISPRS International Journal of Geo-Information. 2020; 9 (11):625.
Chicago/Turabian StyleQuan Xiong; Xiaodong Zhang; Wei Liu; Sijing Ye; Zhenbo Du; Diyou Liu; Dehai Zhu; Zhe Liu; Xiaochuang Yao. 2020. "An Efficient Row Key Encoding Method with ASCII Code for Storing Geospatial Big Data in HBase." ISPRS International Journal of Geo-Information 9, no. 11: 625.
The tassel development status and its branch number in maize flowering stage are the key phenotypic traits to determine the growth process, pollen quantity of different maize varieties, and detasseling arrangement for seed maize production fields. Rapid and accurate detection of tassels is of great significance for maize breeding and seed production. However, due to the complex planting environment in the field, such as unsynchronized growth stage and tassels vary in size and shape caused by varieties, the detection of maize tassel remains challenging problem, and the existing methods also cannot distinguish the early tassels. In this study, based on the time series unmanned aerial vehicle (UAV) RGB images with maize flowering stage, we proposed an algorithm for automatic detection of maize tassels which is suitable for complex scenes by using random forest (RF) and VGG16. First, the RF was used to segment UAV images into tassel regions and non-tassel regions, and then extracted the potential tassel region proposals by morphological method; afterwards, false positives were removed through VGG16 network with the ratio of training set to validation set was 7:3. To demonstrate the performance of the proposed method, 50 plots were selected from UAV images randomly. The precision, recall rate and F1-score were 0.904, 0.979 and 0.94 respectively; 50 plots were divided into early, middle and late tasseling stages according to the proportion of tasseling plants and the morphology of tassels. The result of tassels detection was late tasseling stage > middle tasseling stage > early tasseling stage, and the corresponding F1-score were 0.962, 0.914 and 0.863, respectively. It was found that the model error mainly comes from the recognition of leaves vein and reflective leaves as tassels. Finally, to show the morphological characteristics of tassel directly, we proposed an endpoint detection method based on the tassel skeleton, and further extracted the tassel branch number. The method proposed in this paper can well detect tassels of different development stages, and support large scale tassels detection and branch number extraction.
XuLi Zan; Xinlu Zhang; Ziyao Xing; Wei Liu; Xiaodong Zhang; Wei Su; Zhe Liu; Yuanyuan Zhao; Shaoming Li. Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16. Remote Sensing 2020, 12, 3049 .
AMA StyleXuLi Zan, Xinlu Zhang, Ziyao Xing, Wei Liu, Xiaodong Zhang, Wei Su, Zhe Liu, Yuanyuan Zhao, Shaoming Li. Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16. Remote Sensing. 2020; 12 (18):3049.
Chicago/Turabian StyleXuLi Zan; Xinlu Zhang; Ziyao Xing; Wei Liu; Xiaodong Zhang; Wei Su; Zhe Liu; Yuanyuan Zhao; Shaoming Li. 2020. "Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16." Remote Sensing 12, no. 18: 3049.
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.
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 StyleTianwei 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 StyleTianwei 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.
Monitoring agricultural drought is important to food security and the sustainable development of human society. In order to improve the accuracy of soil moisture and winter wheat yield estimation, drought monitoring effects of optical drought index data, meteorological drought data, and passive microwave soil moisture data were explored during individual and whole growth periods of winter wheat in 2003–2011, taking Henan Province of China as the research area. The model of drought indices and relative meteorological yield of winter wheat in individual and whole growth periods was constructed based on multiple linear regression. Results showed a higher correlation between Moderate-Resolution Imaging Spectroradiometer (MODIS) drought indices and 10 cm relative soil moisture (RSM10) than 20 cm (RSM20) and 50 cm (RSM50). In the whole growth period, the correlation coefficient (R) between vegetation supply water index (VSWI) and RSM10 had the highest correlation (R = −0.206), while in individual growth periods, the vegetation temperature condition index (VTCI) was superior to the vegetation health index (VHI) and VSWI. Among the meteorological drought indices, the 10-day, 20-day, and 30-day standard precipitation evapotranspiration indices (SPEI1, SPEI2, and SPEI3) were all most relevant to RSM10 during individual and whole growth periods. RSM50 and SPEI3 had a higher correlation, indicating that deep soil moisture was more related to drought on a long time scale. The relationship between Advanced Microwave Scanning Radiometer for EOS soil moisture (AMSR-E SM) and VTCI was stable and significantly positive in individual and whole growth periods, which was better compared to VHI and VSWI. Compared with the drought indices and the relative meteorological yield in the city, VHI had the best monitoring effect during individual and whole growth periods. Results also showed that drought occurring at the jointing–heading stage can reduce winter wheat yield, while a certain degree of drought occurring at the heading–milk ripening stage can increase the yield. In the whole growth period, the combination of SPEI1, SPEI2, and VHI had the best performance, with a coefficient of determination (R2) of 0.282 with the combination of drought indices as the independent variables and relative meteorological yield as the dependent variable. In the individual growth period, the model in the later growth period of winter wheat performed well, especially in the returning green–jointing stage (R2 = 0.212). Results show that the combination of multiple linear drought indices in the whole growth period and the model in the returning green–jointing period could improve the accuracy of winter wheat yield estimation. This study is helpful for effective agricultural drought monitoring of winter wheat in Henan Province.
Yuan Li; Yi Dong; DongQin Yin; Diyou Liu; Pengxin Wang; Jianxi Huang; Zhe Liu; Hongshuo Wang. Evaluation of Drought Monitoring Effect of Winter Wheat in Henan Province of China Based on Multi-Source Data. Sustainability 2020, 12, 2801 .
AMA StyleYuan Li, Yi Dong, DongQin Yin, Diyou Liu, Pengxin Wang, Jianxi Huang, Zhe Liu, Hongshuo Wang. Evaluation of Drought Monitoring Effect of Winter Wheat in Henan Province of China Based on Multi-Source Data. Sustainability. 2020; 12 (7):2801.
Chicago/Turabian StyleYuan Li; Yi Dong; DongQin Yin; Diyou Liu; Pengxin Wang; Jianxi Huang; Zhe Liu; Hongshuo Wang. 2020. "Evaluation of Drought Monitoring Effect of Winter Wheat in Henan Province of China Based on Multi-Source Data." Sustainability 12, no. 7: 2801.
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.
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 StyleLin 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 StyleLin 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.
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.
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 StyleXinlei 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 StyleXinlei 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.
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.
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 StyleLin 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 StyleLin 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.
Phenotyping provides important support for corn breeding. Unfortunately, the rapid detection of phenotypes has been the major limiting factor in estimating and predicting the outcomes of breeding programs. This study was focused on the potential of phenotyping to support corn breeding using unmanned aerial vehicle (UAV) images, aiming at mining and deepening UAV techniques for comparing phenotypes and screening new corn varieties. Two geometric traits (plant height, canopy leaf area index (LAI)) and one lodging resistance trait (lodging area) were estimated in this study. It was found that stereoscopic and photogrammetric methods were promising ways to calculate a digital surface model (DSM) for estimating corn plant height from UAV images, with R2 = 0.7833 (p < 0.001) and a root mean square error (RMSE) = 0.1677. In addition to a height estimation, the height variation was analyzed for depicting and validating the corn canopy uniformity stability for different varieties. For the lodging area estimation, the normalized DSM (nDSM) method was more promising than the gray-level co-occurrence matrix (GLCM) textural features method. The estimation error using the nDSM ranged from 0.8% to 5.3%, and the estimation error using the GLCM ranged from 10.0% to 16.2%. Associations between the height estimation and lodging area estimation were done to find the corn varieties with optimal plant heights and lodging resistance. For the LAI estimation, the physical radiative transfer PROSAIL model offered both an accurate and robust estimation performance both at the middle (R2 = 0.7490, RMSE = 0.3443) and later growing stages (R2 = 0.7450, RMSE = 0.3154). What was more exciting was that the estimated sequential time series LAIs revealed a corn variety with poor resistance to lodging in a study area of Baogaofeng Farm. Overall, UAVs appear to provide a promising method to support phenotyping for crop breeding, and the phenotyping of corn breeding in this study validated this application.
Wei Su; Mingzheng Zhang; Dahong Bian; Zhe Liu; Jianxi Huang; Wei Wang; Jiayu Wu; Hao Guo. Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images. Remote Sensing 2019, 11, 2021 .
AMA StyleWei Su, Mingzheng Zhang, Dahong Bian, Zhe Liu, Jianxi Huang, Wei Wang, Jiayu Wu, Hao Guo. Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images. Remote Sensing. 2019; 11 (17):2021.
Chicago/Turabian StyleWei Su; Mingzheng Zhang; Dahong Bian; Zhe Liu; Jianxi Huang; Wei Wang; Jiayu Wu; Hao Guo. 2019. "Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images." Remote Sensing 11, no. 17: 2021.
An appropriate layout of crop multi-environment trial (MET) sites is imperative for evaluating new crop varieties’ performance in terms of agronomic traits and stress tolerance, and this information is used to determine the utilization value and suitable promotion region of new varieties. Actually, traditional maize test sites have been selected according to the experience of breeding experts, which leads to the strong subjective and unscientific conclusions regarding sites, as well as test results that are not representative of the target population of environments (TPE). Therefore, in this study, we proposed a new method for MET sites layout. Meteorological data, maize growth period data, and county-level maize planting area data were collected for the spatiotemporal classification of a given maize planting region to analyze change rules in the environmental category of each minimum research unit within the study period. If the occurrence frequency of its final attribution category reaches a certain threshold (50%), this minimum research unit is classified as a typical environment region; otherwise, it is classified as an atypical environment region. Then, the number of test sites in each environmental category is allocated by spatial stratified sampling. At last, we establish the optimal test sites layout and a reliability measurement (test adequacy) methods. The practicability of this method was proved by taking the Three Northeastern Provinces of China as the study area. The result shows that there should be 112 test sites in the study area, the distribution of the test sites is uniform, and the environmental representation is high. Test adequacy analysis of the test sites reveals that most of the environmental categories have a test adequacy that reaches 1 in each test period. The method proposed in this paper provides support for the scientific layout of crop varieties test sites and helps to improve the representative and reliability of variety test results while optimizing resources.
XuLi Zan; Zuliang Zhao; Wei Liu; Xiaodong Zhang; Zhe Liu; Shaoming Li; Dehai Zhu. The Layout of Maize Variety Test Sites Based on the Spatiotemporal Classification of the Planting Environment. Sustainability 2019, 11, 3741 .
AMA StyleXuLi Zan, Zuliang Zhao, Wei Liu, Xiaodong Zhang, Zhe Liu, Shaoming Li, Dehai Zhu. The Layout of Maize Variety Test Sites Based on the Spatiotemporal Classification of the Planting Environment. Sustainability. 2019; 11 (13):3741.
Chicago/Turabian StyleXuLi Zan; Zuliang Zhao; Wei Liu; Xiaodong Zhang; Zhe Liu; Shaoming Li; Dehai Zhu. 2019. "The Layout of Maize Variety Test Sites Based on the Spatiotemporal Classification of the Planting Environment." Sustainability 11, no. 13: 3741.
Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.
Mingzheng Zhang; Dehai Zhu; Wei Su; Jianxi Huang; Xiaodong Zhang; Zhe Liu. Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring. Remote Sensing 2019, 11, 1266 .
AMA StyleMingzheng Zhang, Dehai Zhu, Wei Su, Jianxi Huang, Xiaodong Zhang, Zhe Liu. Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring. Remote Sensing. 2019; 11 (11):1266.
Chicago/Turabian StyleMingzheng Zhang; Dehai Zhu; Wei Su; Jianxi Huang; Xiaodong Zhang; Zhe Liu. 2019. "Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring." Remote Sensing 11, no. 11: 1266.
Variety regional tests based on multiple environments play a critical role in understanding the high yield and adaptability of new crop varieties. However, the current approach mainly depends on experience from breeding experts and is difficulty to promote because of inconsistency between testing and actual situation. We propose a spatial layout method based on the existing systematic regional test network. First, the method of spatial clustering was used to cluster the planting environment. Then, we used spatial stratified sampling to determine the minimum number of test sites in each type of environment. Finally, combined with the factors such as the convenience of transportation and the planting area, we used spatial balance sampling to generate the layout of multi-environment test sites. We present a case study for maize in Jilin Province and show the utility of the method with an accuracy of about 94.5%. The experimental results showed that 66.7% of sites are located in the same county and the unbalanced layout of original sites is improved. Furthermore, we conclude that the set of operational technical ideas for carrying out the layout of multi-environment test sites based on crop varieties in this paper can be applied to future research.
Zuliang Zhao; Liu Zhe; Xiaodong Zhang; XuLi Zan; Xiaochuang Yao; Sijia Wang; Sijing Ye; Shaoming Li; Dehai Zhu. Spatial Layout of Multi-Environment Test Sites: A Case Study of Maize in Jilin Province. Sustainability 2018, 10, 1424 .
AMA StyleZuliang Zhao, Liu Zhe, Xiaodong Zhang, XuLi Zan, Xiaochuang Yao, Sijia Wang, Sijing Ye, Shaoming Li, Dehai Zhu. Spatial Layout of Multi-Environment Test Sites: A Case Study of Maize in Jilin Province. Sustainability. 2018; 10 (5):1424.
Chicago/Turabian StyleZuliang Zhao; Liu Zhe; Xiaodong Zhang; XuLi Zan; Xiaochuang Yao; Sijia Wang; Sijing Ye; Shaoming Li; Dehai Zhu. 2018. "Spatial Layout of Multi-Environment Test Sites: A Case Study of Maize in Jilin Province." Sustainability 10, no. 5: 1424.