Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets
Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/11/2542 |
| _version_ | 1827664232692318208 |
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| author | Yungang Cao Puying Du Min Zhang Xueqin Bai Ruodan Lei Xiuchun Yang |
| author_facet | Yungang Cao Puying Du Min Zhang Xueqin Bai Ruodan Lei Xiuchun Yang |
| author_sort | Yungang Cao |
| collection | DOAJ |
| description | Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>f</mi><mrow><mi>s</mi><mi>t</mi><mi>a</mi><mi>r</mi><mi>f</mi><mi>m</mi></mrow><mn>8</mn></msubsup></mrow></semantics></math></inline-formula>); (2) the estimation accuracy based on 8d composite datasets (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow><mo stretchy="true">¯</mo></mover></mrow></semantics></math></inline-formula> = 11.1d) is better than daily fusion datasets (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow><mo stretchy="true">¯</mo></mover></mrow></semantics></math></inline-formula> = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow><mo stretchy="true">¯</mo></mover></mrow></semantics></math></inline-formula> of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation. |
| first_indexed | 2024-03-10T00:56:17Z |
| format | Article |
| id | doaj.art-f16103a2cbeb4a09b8dd19aee14e5f43 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T00:56:17Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f16103a2cbeb4a09b8dd19aee14e5f432023-11-23T14:43:25ZengMDPI AGRemote Sensing2072-42922022-05-011411254210.3390/rs14112542Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion DatasetsYungang Cao0Puying Du1Min Zhang2Xueqin Bai3Ruodan Lei4Xiuchun Yang5Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Grassland Science, Beijing Forestry University, Beijing 100083, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Grassland Science, Beijing Forestry University, Beijing 100083, ChinaEstimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>f</mi><mrow><mi>s</mi><mi>t</mi><mi>a</mi><mi>r</mi><mi>f</mi><mi>m</mi></mrow><mn>8</mn></msubsup></mrow></semantics></math></inline-formula>); (2) the estimation accuracy based on 8d composite datasets (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow><mo stretchy="true">¯</mo></mover></mrow></semantics></math></inline-formula> = 11.1d) is better than daily fusion datasets (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow><mo stretchy="true">¯</mo></mover></mrow></semantics></math></inline-formula> = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><mi>R</mi><mi>M</mi><mi>S</mi><mi>E</mi></mrow><mo stretchy="true">¯</mo></mover></mrow></semantics></math></inline-formula> of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation.https://www.mdpi.com/2072-4292/14/11/2542grasslandSOSspatio-temporal fusionNDVISTARFM |
| spellingShingle | Yungang Cao Puying Du Min Zhang Xueqin Bai Ruodan Lei Xiuchun Yang Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets Remote Sensing grassland SOS spatio-temporal fusion NDVI STARFM |
| title | Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets |
| title_full | Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets |
| title_fullStr | Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets |
| title_full_unstemmed | Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets |
| title_short | Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets |
| title_sort | quantitative evaluation of grassland sos estimation accuracy based on different modis landsat spatio temporal fusion datasets |
| topic | grassland SOS spatio-temporal fusion NDVI STARFM |
| url | https://www.mdpi.com/2072-4292/14/11/2542 |
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