Abstract
Abstract. 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 % (R2 = 0.52) and the European Center for Medium-Range Weather Forecasts ERA-Interim reanalysis data can explain 64 % of PAR variance (R2 = 0.64); 2) estimating GPP with Landsat 30-m spatial resolution data and ERA daily meteorology data has the highest accuracy(R2 = 0.85, RMSE < 3 gC/m2/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.
Highlights
Gross Primary Production (GPP) is one of the most important components of for terrestrial ecosystem carbon cycle and global change monitoring
In band 1 of GOES-13 imager instrument, its digital number (DN) value represented for shortwave radiation reflectance from top of the atmosphere
This study evaluated GPP estimation accuracy influenced by three major aspects of data input, including: i) different spatial scale remote sensing vegetation index input like 30-m resolution Landsat NDVI, 1-km resolution MODIS NDVI; ii) different frequency meteorology data input in GPP model like daily, 8-day, 16-day; iii) different source of data input for GPP estimation like flux tower based data input and ERA reanalysis data input
Summary
Gross Primary Production (GPP) is one of the most important components of for terrestrial ecosystem carbon cycle and global change monitoring. Light-use-efficiency (LUE) models would be one of the most effective model which have sample data input but higher estimation precisely in different scale. LUEmax is the potential LUE under no environmental stress, but mostly the LUE stresses by many factors such as temperature, water content, and light quality (Kalfas et al, 2011; Wu et al, 2008; Maselli et al.2009; Suyker and Verma 2012; Nguy-Robertson et al, 2015).Since satellites can supply large scale observation of terrestrial vegetation, a diverse set of satellite based models can apply to model GPP from small region to whole continent (Field et al, 1995; Running et al, 2000; Xiao et al, 2004, Yuan et al, 2007, King et al, 2011, Mahadevan et al, 2008). Most of the LUE based models needs the satellite and reanalysis meteorology data input, but these data are mostly at lower spatial resolution. For during the downscale, the reanalysis climate data need to resample, the
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