Abstract
Time series of leaf area index (LAI) products are now widely used, and the temporal validation is the prerequisite for their proper application. However, a systematical comparison between different products using both direct and indirect methods is still lacking. The objective of this paper is to assess and compare the temporal performances of four <small>LAI</small> products: Moderate Resolution Imaging Spectroradiometer <small>(MODIS) LAI (MOD)15A2, MOD15A2h</small>, Geoland2 Version 1 <small>(GEOV1)</small>, and Global Land Surface Satellite (<small>GLASS</small>). The study area, which is dominated by grasslands, is located in the northeastern Tibetan Plateau (<small>TP</small>), and temperature is the main stress factor affecting grass growth. Both a correlation analysis with temperature and a direct comparison with temporally continuous <small>LAI</small> reference maps were implemented in our temporal validation experiments. The results show that no single product can capture the rapid change and the seasonal trend in <small>LAI</small> simultaneously, and the compositing period used in each product determines the quality of the corresponding <small>LAI</small> time series. The <small>MOD15A2</small> and <small>MOD15A2h</small> products, which have short compositing windows (eight days), are suitable for detecting rapid change. A grazing-induced biomass decrease that occurred around day of year 205 in 2014 in our study area was clearly revealed in these two products. For the GEOV1</small> and <small>GLASS</small> products, which have compositing windows of 30 days and 1 year, respectively, the grazing date was shifted (<small>GEOV1</small>) or even invisible (<small>GLASS</small>). However, products with prolonged compositing windows may be more robust to observation noise, and the resulting products may be suitable for capturing the seasonal trend. This study highlights that the concurrent use of data from various sensors onboard different satellites, and the introduction of new generations of satellites (e.g., Gaofen-6 ), are two promising ways to further improve existing <small>LAI</small> time series.
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Claims 7
Claim · model-written summary
No single LAI product captured both rapid change and seasonal trend.
Verbatim
e0“no single product can capture the rapid change and the seasonal trend in <small>LAI</small> simultaneously”
t0“Grasslands”
Claim · model-written summary
Product compositing period determined LAI time-series quality.
Verbatim
e0“the compositing period used in each product determines the quality of the corresponding <small>LAI</small> time series.”
t0“Grasslands”
Claim · model-written summary
MOD15A2 and MOD15A2h detected rapid LAI change.
Verbatim
e0“The <small>MOD15A2</small> and <small>MOD15A2h</small> products, which have short compositing windows (eight days), are suitable for detecting rapid change.”
t0“Grasslands”
Claim · model-written summary
Both short-window products revealed a grazing-induced biomass decrease around day 205 of 2014.
Verbatim
e0“A grazing-induced biomass decrease that occurred around day of year 205 in 2014 in our study area was clearly revealed in these two products.”
t0“Grasslands”
Claim · model-written summary
GEOV1 shifted the apparent grazing date.
Verbatim
e0“the grazing date was shifted (<small>GEOV1</small>) or even invisible (<small>GLASS</small>).”
t0“Grasslands”
Claim · model-written summary
GLASS did not show the grazing date.
Verbatim
e0“the grazing date was shifted (<small>GEOV1</small>) or even invisible (<small>GLASS</small>).”
t0“Grasslands”
Claim · model-written summary
Products with longer compositing windows may better capture seasonal trend while resisting noise.
Verbatim
e0“products with prolonged compositing windows may be more robust to observation noise, and the resulting products may be suitable for capturing the seasonal trend.”
t0“Grasslands”