Wheat planted area detection from the MODIS NDVI time series classification using the nearest neighbour method calculated by the Euclidean distance and cosine similarity measures

Silva, Miriam Rodrigues da; Júnior, Osmar Abı́lio de Carvalho; Guimarães, Renato Fontes; Gomes, Roberto Arnaldo Trancoso; Silva, Cristiano Rosa

doi:10.1080/10106049.2019.1581266

Cited by 10 publications

(5 citation statements)

References 64 publications

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“…Cosine similarity [53] is a measure of similarity between two datasets. The cosine of two sets can be derived by the Euclidean dot product formula:…”

Section: Cosine Similaritymentioning

confidence: 99%

Validation and Comparison of Seven Land Surface Evapotranspiration Products in the Haihe River Basin, China

et al. 2022

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Evapotranspiration (ET) is an important part of the surface energy balance and water balance. Due to imperfect model parameterizations and forcing data, there are still great uncertainties concerning ET products. The validation of land surface ET products has a certain research significance. In this study, two direct validation methods, including the latent heat flux (LE) from the flux towers validation method and the water balance validation method, and one indirect validation method, the three-corned hat (TCH) uncertainty analysis, were used to validate and compare seven types of ET products in the Haihe River Basin in China. The products evaluated included six ET products based on remotely-sensed observations (surface energy balance based global land evapotranspiration [EB-ET], Moderate Resolution Imaging Spectroradiometer [MODIS] global terrestrial evapotranspiration product [MOD16], Penman–Monteith–Leuning Evapotranspiration version 2 [PML_V2], Global Land Surface Satellite [GLASS], global land evaporation Amsterdam model [GLEAM], and Zhangke evapotranspiration [ZK-ET]) and one ET product from atmospheric re-analysis data (Japanese 55-year re-analysis, JRA-55). The goals of this study were to provide a reference for research on ET in the Haihe River Basin. The results indicate the following: (1) The results of the six ET products have a higher accuracy when the flux towers validation method is used. Except for MOD16_ET and EB_ET, the Pearson correlation coefficients (R) were all greater than 0.6. The root mean square deviation (RMSD) values were all less than 40 W/m2. The GLASS_ET data have the smallest average deviation (BIAS) value. Overall, the GLEAM_ET data have a higher accuracy. (2) When the validation of the water balance approach was used, the low values of the MOD16_ET were overestimated and the high values were underestimated. The values of the EB_ET, GLEAM_ET, JRA_ET, PML_ET, and ZK_ET were overestimated. According to the seasonal variations statistics, most of the ET products have higher R values in spring and lower R values in summer, and the RMSD values of most of the products were the highest in summer. (3) According to the results of the uncertainty quantification based on the TCH method, the average value of the relative uncertainties of the GLEAM_ET data were the lowest. The relative uncertainties of the JRA_ET and ZK_ET were higher in mountainous areas than in non-mountainous area, and the relative uncertainties of the PML_ET were lower in mountainous areas. The performances of the EB_ET, GLEAM_ET, and MOD16_ET in mountainous and non-mountainous areas were relatively equal. The relative uncertainties of the ET products were significantly higher in summer than in other periods, and they also varied in the different sub-basins.

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“…Cosine similarity [53] is a measure of similarity between two datasets. The cosine of two sets can be derived by the Euclidean dot product formula:…”

Section: Cosine Similaritymentioning

confidence: 99%

Validation and Comparison of Seven Land Surface Evapotranspiration Products in the Haihe River Basin, China

et al. 2022

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“…Take the average of the points closest to the test sample within the threshold range, and then, this value is the predicted value of this sample point [20]. Wi-Fi indoor positioning measures the Euclidean distance [21] dis, which is the distance between the signal strength, Received Signal Strength Indication 3 Wireless Communications and Mobile Computing (RSSI) [22] collected by mobile terminal equipment in realtime, and the RSSI vector sets of the points. Select the observation point coordinates with the smallest difference in Euclidean distance, and estimate the final position coordinates of the positioning point to be tested.…”

Section: K-neighbormentioning

confidence: 99%

Intelligent Perception and Positioning Technology of Internet of Things by $K$ -Nearest Neighbor Matching Algorithm

Zou

Xingrui

Zeren

2022

Wireless Communications and Mobile Computing

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To study the intelligent sensing and positioning technology of the Internet of Things (IoT) combined with the K -nearest neighbor algorithm, the K -nearest neighbor matching algorithm and optimization algorithm are introduced using the indoor Wi-Fi positioning technology. The study proposes weighting K -nearest neighbor (WKNN) by weighted Euclidean distance, adaptive weighted Euclidean distance K-nearest neighbor Wi-Fi localization algorithm, and optimal K -value Wi-Fi fingerprint localization algorithm. The experimental error is verified. The experimental results show that the lowest error of continuous acquisition of 3 s signal values in experimental environment A is 1.8815 m, which is 10.13% lower than the error of only acquiring 1 s for the same K -value. The lowest error of environment B scheme two can reach 1.8862, which is 7.06% lower than the error of the same K -value. The optimal K -value Wi-Fi fingerprint positioning algorithm by distance constraint has better positioning accuracy than other KNN positioning algorithms, and the positioning fluctuation is smaller. The average positioning error of the optimal K in environment A is 1.2987 m, which is 0.2797 m less than the average of the traditional positioning algorithm. In environment B, the average positioning error of the optimal K is 1.5353 m, which is 0.3253 m less than the average of the traditional positioning algorithm. Therefore, the optimal K -value Wi-Fi positioning algorithm proposed has better performance.

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“…The MD mainly measures the sum of the absolute wheelbases of two curves in the standard coordinate system: the smaller the MD, the more similar the two curves [44]. The ED mainly refers to the true distance between two points in m-dimensional space; the smaller the ED between two time series, the more similar the two time series are [45,46]. The RMSE is a derivative of the ED; for two curves, the smaller the RMSE value, the more similar the two curves are [43].…”

Section: Calculation Of Different Similarity Indicatorsmentioning

confidence: 99%

Comparison of Regional Winter Wheat Mapping Results from Different Similarity Measurement Indicators of NDVI Time Series and Their Optimized Thresholds

Ren

et al. 2021

Remote Sensing

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Generally, there is an inconsistency between the total regional crop area that was obtained from remote sensing technology and the official statistical data on crop areas. When performing scale conversion and data aggregation of remote sensing-based crop mapping results from different administrative scales, it is difficult to obtain accurate crop planting area that match crop area statistics well at the corresponding administrative level. This problem affects the application of remote sensing-based crop mapping results. In order to solve the above problem, taking Fucheng County of Hebei Province in the Huanghuaihai Plain of China as the study area, based on the Sentinel-2 normalized difference vegetation index (NDVI) time series data covering the whole winter wheat growth period, the statistical data of the regional winter wheat planting area were regarded as reference for the winter wheat planting area extracted by remote sensing, and a new method for winter wheat mapping that is based on similarity measurement indicators and their threshold optimizations (WWM-SMITO) was proposed with the support of the shuffled complex evolution-University of Arizona (SCE-UA) global optimization algorithm. The accuracy of the regional winter wheat mapping results was verified, and accuracy comparisons with different similarity indicators were carried out. The results showed that the total area accuracy of the winter wheat area extraction by the proposed method reached over 99.99%, which achieved a consistency that was between the regional remote sensing-based winter wheat planting area and the statistical data on the winter wheat planting area. The crop recognition accuracy also reached a high level, which showed that the proposed method was effective and feasible. Moreover, in the accuracy comparison of crop mapping results based on six different similarity indicators, the winter wheat distribution that was extracted by root mean square error (RMSE) had the best recognition accuracy, and the overall accuracy and kappa coefficient were 94.5% and 0.8894, respectively. The overall accuracies of winter wheat that were extracted by similarity indicators, such as Euclidean distance (ED), Manhattan distance (MD), spectral angle mapping (SAM), and spectral correlation coefficient (SCC) were 94.1%, 93.9%, 93.3%, and 92.8%, respectively, and the kappa coefficients were 0.8815, 0.8776, 0.8657, and 0.8558, respectively. The accuracy of the winter wheat results extracted by the similarity indicator of dynamic time warping (DTW) was relatively low. The results of this paper could provide guidance and serve as a reference for the selection of similarity indicators in crop distribution extraction and for obtaining large-scale, long-term, and high-precision remote sensing-based information on a regional crop spatial distribution that is highly consistent with statistical crop area data.

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Wheat planted area detection from the MODIS NDVI time series classification using the nearest neighbour method calculated by the Euclidean distance and cosine similarity measures

Cited by 10 publications

References 64 publications

Validation and Comparison of Seven Land Surface Evapotranspiration Products in the Haihe River Basin, China

Validation and Comparison of Seven Land Surface Evapotranspiration Products in the Haihe River Basin, China

Intelligent Perception and Positioning Technology of Internet of Things by $K$ -Nearest Neighbor Matching Algorithm

Comparison of Regional Winter Wheat Mapping Results from Different Similarity Measurement Indicators of NDVI Time Series and Their Optimized Thresholds

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Wheat planted area detection from the MODIS NDVI time series classification using the nearest neighbour method calculated by the Euclidean distance and cosine similarity measures

Cited by 10 publications

References 64 publications

Validation and Comparison of Seven Land Surface Evapotranspiration Products in the Haihe River Basin, China

Validation and Comparison of Seven Land Surface Evapotranspiration Products in the Haihe River Basin, China

Intelligent Perception and Positioning Technology of Internet of Things by K -Nearest Neighbor Matching Algorithm

Comparison of Regional Winter Wheat Mapping Results from Different Similarity Measurement Indicators of NDVI Time Series and Their Optimized Thresholds

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Intelligent Perception and Positioning Technology of Internet of Things by $K$ -Nearest Neighbor Matching Algorithm