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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.
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.
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.
Nowadays, GF-1 (GF is the acronym for GaoFen which means high-resolution in Chinese) remote sensing images are widely utilized in agriculture because of their high spatio-temporal resolution and free availability. However, due to the transferrable rationale of optical satellites, the GF-1 remote sensing images are inevitably impacted by clouds, which leads to a lack of ground object’s information of crop areas and adds noises to research datasets. Therefore, it is crucial to efficiently detect the cloud pixel of GF-1 imagery of crop areas with powerful performance both in time consumption and accuracy when it comes to large-scale agricultural processing and application. To solve the above problems, this paper proposed a cloud detection approach based on hybrid multispectral features (HMF) with dynamic thresholds. This approach combined three spectral features, namely the Normalized Difference Vegetation Index (NDVI), WHITENESS and the Haze-Optimized Transformation (HOT), to detect the cloud pixels, which can take advantage of the hybrid Multispectral Features. Meanwhile, in order to meet the variety of the threshold values in different seasons, a dynamic threshold adjustment method was adopted, which builds a relationship between the features and a solar altitude angle to acquire a group of specific thresholds for an image. With the test of GF-1 remote sensing datasets and comparative trials with Random Forest (RF), the results show that the method proposed in this paper not only has high accuracy, but also has advantages in terms of time consumption. The average accuracy of cloud detection can reach 90.8% and time consumption for each GF-1 imagery can reach to 5 min, which has been reduced by 83.27% compared with RF method. Therefore, the approach presented in this work could serve as a reference for those who are interested in the cloud detection of remote sensing images.
Quan Xiong; Yuan Wang; Diyou Liu; Sijing Ye; Zhenbo Du; Wei Liu; Jianxi Huang; Wei Su; Dehai Zhu; Xiaochuang Yao; Xiaodong Zhang. A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images. Remote Sensing 2020, 12, 450 .
AMA StyleQuan Xiong, Yuan Wang, Diyou Liu, Sijing Ye, Zhenbo Du, Wei Liu, Jianxi Huang, Wei Su, Dehai Zhu, Xiaochuang Yao, Xiaodong Zhang. A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images. Remote Sensing. 2020; 12 (3):450.
Chicago/Turabian StyleQuan Xiong; Yuan Wang; Diyou Liu; Sijing Ye; Zhenbo Du; Wei Liu; Jianxi Huang; Wei Su; Dehai Zhu; Xiaochuang Yao; Xiaodong Zhang. 2020. "A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images." Remote Sensing 12, no. 3: 450.
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.
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.
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.
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 StyleNing 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 StyleNing 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.
In recent years, remote sensing (RS) research on crop growth status monitoring has gradually turned from static spectrum information retrieval in large-scale to meso-scale or micro-scale, timely multi-source data cooperative analysis; this change has presented higher requirements for RS data acquisition and analysis efficiency. How to implement rapid and stable massive RS data extraction and analysis becomes a serious problem. This paper reports on a Raster Dataset Clean & Reconstitution Multi-Grid (RDCRMG) architecture for remote sensing monitoring of vegetation dryness in which different types of raster datasets have been partitioned, organized and systematically applied. First, raster images have been subdivided into several independent blocks and distributed for storage in different data nodes by using the multi-grid as a consistent partition unit. Second, the “no metadata model” ideology has been referenced so that targets raster data can be speedily extracted by directly calculating the data storage path without retrieving metadata records; third, grids that cover the query range can be easily assessed. This assessment allows the query task to be easily split into several sub-tasks and executed in parallel by grouping these grids. Our RDCRMG-based change detection of the spectral reflectance information test and the data extraction efficiency comparative test shows that the RDCRMG is reliable for vegetation dryness monitoring with a slight reflectance information distortion and consistent percentage histograms. Furthermore, the RDCGMG-based data extraction in parallel circumstances has the advantages of high efficiency and excellent stability compared to that of the RDCGMG-based data extraction in serial circumstances and traditional data extraction. At last, an RDCRMG-based vegetation dryness monitoring platform (VDMP) has been constructed to apply RS data inversion in vegetation dryness monitoring. Through actual applications, the RDCRMG architecture is proven to be appropriate for timely vegetation dryness RS automatic monitoring with better performance, more reliability and higher extensibility. Our future works will focus on integrating more kinds of continuously updated RS data into the RDCRMG-based VDMP and integrating more multi-source datasets based collaborative analysis models for agricultural monitoring.
Sijing Ye; Diyou Liu; Xiaochuang Yao; Huaizhi Tang; Quan Xiong; Wen Zhuo; Zhenbo Du; Jianxi Huang; Wei Su; Shi Shen; Zuliang Zhao; Shaolong Cui; Lixin Ning; Dehai Zhu; Changxiu Cheng; Changqing Song. RDCRMG: A Raster Dataset Clean & Reconstitution Multi-Grid Architecture for Remote Sensing Monitoring of Vegetation Dryness. Remote Sensing 2018, 10, 1376 .
AMA StyleSijing Ye, Diyou Liu, Xiaochuang Yao, Huaizhi Tang, Quan Xiong, Wen Zhuo, Zhenbo Du, Jianxi Huang, Wei Su, Shi Shen, Zuliang Zhao, Shaolong Cui, Lixin Ning, Dehai Zhu, Changxiu Cheng, Changqing Song. RDCRMG: A Raster Dataset Clean & Reconstitution Multi-Grid Architecture for Remote Sensing Monitoring of Vegetation Dryness. Remote Sensing. 2018; 10 (9):1376.
Chicago/Turabian StyleSijing Ye; Diyou Liu; Xiaochuang Yao; Huaizhi Tang; Quan Xiong; Wen Zhuo; Zhenbo Du; Jianxi Huang; Wei Su; Shi Shen; Zuliang Zhao; Shaolong Cui; Lixin Ning; Dehai Zhu; Changxiu Cheng; Changqing Song. 2018. "RDCRMG: A Raster Dataset Clean & Reconstitution Multi-Grid Architecture for Remote Sensing Monitoring of Vegetation Dryness." Remote Sensing 10, no. 9: 1376.