An Approach to Refining MODIS LAI Data Using a Fitting Scale Factor Time Series
The leaf area index (LAI) serves as a key metric for tracking crop growth and can be integrated into crop models for yield estimation. Although the remote sensing LAI data provide a critical foundation for monitoring crop growth and estimating yields, the existing datasets often exhibit notable erro...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/293 |
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| author | Junxian Tang Peijuan Wang Rui Feng Yang Li Qing Li |
| author_facet | Junxian Tang Peijuan Wang Rui Feng Yang Li Qing Li |
| author_sort | Junxian Tang |
| collection | DOAJ |
| description | The leaf area index (LAI) serves as a key metric for tracking crop growth and can be integrated into crop models for yield estimation. Although the remote sensing LAI data provide a critical foundation for monitoring crop growth and estimating yields, the existing datasets often exhibit notable errors due to the pixel-level heterogeneity. To improve the applicability and inversion accuracy of MODIS LAI products in the Northeast China (NEC) region, this study upscaled the 500-m resolution MODIS LAI product to a 5-km resolution by initially calculating the mean value. Then, the scale factors were estimated based on the observed LAI data of spring maize. To further refine the accuracy of the remotely sensed LAI, 1-km resolution land use data were resampled to 500-m resolution, and the pixel purity of spring maize was calculated for each 5-km grid cell. The scale factor time series was fitted with and without consideration of pixel purity, and the accuracy of the adjusted LAI using these two methods was compared. Our findings demonstrate that the optimal method for fitting scale factors for spring maize LAI data is piecewise function method which combines Gaussian and quadratic polynomial functions. The time series of scale factors derived from high- and low-purity pixels, differentiated by a 50% purity threshold, resulted in improved performance in adjusting the spring maize LAI compared to traditional remote sensing LAI data. The adjusted LAI performed better in reflecting the growth characteristics of spring maize in the NEC region, with the relative mean square errors between observed and adjusted LAI of spring maize during 2016 and 2020 below 1 m<sup>2</sup>/m<sup>2</sup>. This study provides crucial support for monitoring the growth process and estimating the yield of spring maize in the NEC region and also offers valuable scientific references for the optimization and application of remote sensing data. |
| format | Article |
| id | doaj-art-1f926c667406485892ce2d1a3d029e0d |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-1f926c667406485892ce2d1a3d029e0d2025-01-24T13:48:01ZengMDPI AGRemote Sensing2072-42922025-01-0117229310.3390/rs17020293An Approach to Refining MODIS LAI Data Using a Fitting Scale Factor Time SeriesJunxian Tang0Peijuan Wang1Rui Feng2Yang Li3Qing Li4Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaState Key Laboratory of Severe Weather, Shenyang Institute of Agricultural and Ecological Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaInstitute of Atmospheric Environment, China Meteorological Administration, Shenyang 110016, ChinaState Key Laboratory of Severe Weather, Shenyang Institute of Agricultural and Ecological Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaThe leaf area index (LAI) serves as a key metric for tracking crop growth and can be integrated into crop models for yield estimation. Although the remote sensing LAI data provide a critical foundation for monitoring crop growth and estimating yields, the existing datasets often exhibit notable errors due to the pixel-level heterogeneity. To improve the applicability and inversion accuracy of MODIS LAI products in the Northeast China (NEC) region, this study upscaled the 500-m resolution MODIS LAI product to a 5-km resolution by initially calculating the mean value. Then, the scale factors were estimated based on the observed LAI data of spring maize. To further refine the accuracy of the remotely sensed LAI, 1-km resolution land use data were resampled to 500-m resolution, and the pixel purity of spring maize was calculated for each 5-km grid cell. The scale factor time series was fitted with and without consideration of pixel purity, and the accuracy of the adjusted LAI using these two methods was compared. Our findings demonstrate that the optimal method for fitting scale factors for spring maize LAI data is piecewise function method which combines Gaussian and quadratic polynomial functions. The time series of scale factors derived from high- and low-purity pixels, differentiated by a 50% purity threshold, resulted in improved performance in adjusting the spring maize LAI compared to traditional remote sensing LAI data. The adjusted LAI performed better in reflecting the growth characteristics of spring maize in the NEC region, with the relative mean square errors between observed and adjusted LAI of spring maize during 2016 and 2020 below 1 m<sup>2</sup>/m<sup>2</sup>. This study provides crucial support for monitoring the growth process and estimating the yield of spring maize in the NEC region and also offers valuable scientific references for the optimization and application of remote sensing data.https://www.mdpi.com/2072-4292/17/2/293spring maizeNortheast Chinaleaf area indexpixel purityscale factor |
| spellingShingle | Junxian Tang Peijuan Wang Rui Feng Yang Li Qing Li An Approach to Refining MODIS LAI Data Using a Fitting Scale Factor Time Series Remote Sensing spring maize Northeast China leaf area index pixel purity scale factor |
| title | An Approach to Refining MODIS LAI Data Using a Fitting Scale Factor Time Series |
| title_full | An Approach to Refining MODIS LAI Data Using a Fitting Scale Factor Time Series |
| title_fullStr | An Approach to Refining MODIS LAI Data Using a Fitting Scale Factor Time Series |
| title_full_unstemmed | An Approach to Refining MODIS LAI Data Using a Fitting Scale Factor Time Series |
| title_short | An Approach to Refining MODIS LAI Data Using a Fitting Scale Factor Time Series |
| title_sort | approach to refining modis lai data using a fitting scale factor time series |
| topic | spring maize Northeast China leaf area index pixel purity scale factor |
| url | https://www.mdpi.com/2072-4292/17/2/293 |
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