ESTIMATING GROSS PRIMARY PRODUCTION IN CROPLAND WITH HIGH SPATIAL AND TEMPORAL SCALE REMOTE SENSING DATA
Satellite remote sensing data provide spatially continuous and temporally repetitive observations of land surfaces, and they have become increasingly important for monitoring large region of vegetation photosynthetic dynamic. But remote sensing data have their limitation on spatial and temporal scal...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-04-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/1009/2018/isprs-archives-XLII-3-1009-2018.pdf |
| _version_ | 1818947353843859456 |
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| author | S. Lin S. Lin J. Li Q. Liu Q. Liu Q. Liu |
| author_facet | S. Lin S. Lin J. Li Q. Liu Q. Liu Q. Liu |
| author_sort | S. Lin |
| collection | DOAJ |
| description | Satellite remote sensing data provide spatially continuous and temporally repetitive observations of land surfaces, and they have become increasingly important for monitoring large region of vegetation photosynthetic dynamic. But remote sensing data have their limitation on spatial and temporal scale, for example, higher spatial resolution data as Landsat data have 30-m spatial resolution but 16 days revisit period, while high temporal scale data such as geostationary data have 30-minute imaging period, which has lower spatial resolution (> 1 km). The objective of this study is to investigate whether combining high spatial and temporal resolution remote sensing data can improve the gross primary production (GPP) estimation accuracy in cropland. For this analysis we used three years (from 2010 to 2012) Landsat based NDVI data, MOD13 vegetation index product and Geostationary Operational Environmental Satellite (GOES) geostationary data as input parameters to estimate GPP in a small region cropland of Nebraska, US. Then we validated the remote sensing based GPP with the in-situ measurement carbon flux data. Results showed that: 1) the overall correlation between GOES visible band and in-situ measurement photosynthesis active radiation (PAR) is about 50 % (R<sup>2</sup> = 0.52) and the European Center for Medium-Range Weather Forecasts ERA-Interim reanalysis data can explain 64 % of PAR variance (R<sup>2</sup> = 0.64); 2) estimating GPP with Landsat 30-m spatial resolution data and ERA daily meteorology data has the highest accuracy(R<sup>2</sup> = 0.85, RMSE < 3 gC/m<sup>2</sup>/day), which has better performance than using MODIS 1-km NDVI/EVI product import; 3) using daily meteorology data as input for GPP estimation in high spatial resolution data would have higher relevance than 8-day and 16-day input. Generally speaking, using the high spatial resolution and high frequency satellite based remote sensing data can improve GPP estimation accuracy in cropland. |
| first_indexed | 2024-12-20T08:29:34Z |
| format | Article |
| id | doaj.art-1b06daae3cbf4965a4e36f31355d9640 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-20T08:29:34Z |
| publishDate | 2018-04-01 |
| publisher | Copernicus Publications |
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| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-1b06daae3cbf4965a4e36f31355d96402022-12-21T19:46:44ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342018-04-01XLII-31009101410.5194/isprs-archives-XLII-3-1009-2018ESTIMATING GROSS PRIMARY PRODUCTION IN CROPLAND WITH HIGH SPATIAL AND TEMPORAL SCALE REMOTE SENSING DATAS. Lin0S. Lin1J. Li2Q. Liu3Q. Liu4Q. Liu5College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaJoint Center for Global Change Studies (JCGCS), Beijing 100875, ChinaSatellite remote sensing data provide spatially continuous and temporally repetitive observations of land surfaces, and they have become increasingly important for monitoring large region of vegetation photosynthetic dynamic. But remote sensing data have their limitation on spatial and temporal scale, for example, higher spatial resolution data as Landsat data have 30-m spatial resolution but 16 days revisit period, while high temporal scale data such as geostationary data have 30-minute imaging period, which has lower spatial resolution (> 1 km). The objective of this study is to investigate whether combining high spatial and temporal resolution remote sensing data can improve the gross primary production (GPP) estimation accuracy in cropland. For this analysis we used three years (from 2010 to 2012) Landsat based NDVI data, MOD13 vegetation index product and Geostationary Operational Environmental Satellite (GOES) geostationary data as input parameters to estimate GPP in a small region cropland of Nebraska, US. Then we validated the remote sensing based GPP with the in-situ measurement carbon flux data. Results showed that: 1) the overall correlation between GOES visible band and in-situ measurement photosynthesis active radiation (PAR) is about 50 % (R<sup>2</sup> = 0.52) and the European Center for Medium-Range Weather Forecasts ERA-Interim reanalysis data can explain 64 % of PAR variance (R<sup>2</sup> = 0.64); 2) estimating GPP with Landsat 30-m spatial resolution data and ERA daily meteorology data has the highest accuracy(R<sup>2</sup> = 0.85, RMSE < 3 gC/m<sup>2</sup>/day), which has better performance than using MODIS 1-km NDVI/EVI product import; 3) using daily meteorology data as input for GPP estimation in high spatial resolution data would have higher relevance than 8-day and 16-day input. Generally speaking, using the high spatial resolution and high frequency satellite based remote sensing data can improve GPP estimation accuracy in cropland.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/1009/2018/isprs-archives-XLII-3-1009-2018.pdf |
| spellingShingle | S. Lin S. Lin J. Li Q. Liu Q. Liu Q. Liu ESTIMATING GROSS PRIMARY PRODUCTION IN CROPLAND WITH HIGH SPATIAL AND TEMPORAL SCALE REMOTE SENSING DATA The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | ESTIMATING GROSS PRIMARY PRODUCTION IN CROPLAND WITH HIGH SPATIAL AND TEMPORAL SCALE REMOTE SENSING DATA |
| title_full | ESTIMATING GROSS PRIMARY PRODUCTION IN CROPLAND WITH HIGH SPATIAL AND TEMPORAL SCALE REMOTE SENSING DATA |
| title_fullStr | ESTIMATING GROSS PRIMARY PRODUCTION IN CROPLAND WITH HIGH SPATIAL AND TEMPORAL SCALE REMOTE SENSING DATA |
| title_full_unstemmed | ESTIMATING GROSS PRIMARY PRODUCTION IN CROPLAND WITH HIGH SPATIAL AND TEMPORAL SCALE REMOTE SENSING DATA |
| title_short | ESTIMATING GROSS PRIMARY PRODUCTION IN CROPLAND WITH HIGH SPATIAL AND TEMPORAL SCALE REMOTE SENSING DATA |
| title_sort | estimating gross primary production in cropland with high spatial and temporal scale remote sensing data |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/1009/2018/isprs-archives-XLII-3-1009-2018.pdf |
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