Abstract
Estimation of essential vegetation properties from remote sensing is crucial for a quantitative understanding of the Earth system. Ill-posedness of the model inversion problem leads to multiple interpretations of one satellite observation, and using prior information is a promising way to reduce the ill-posedness and increase the accuracy of land surface products. Tobler's first law of geography states that “everything is related to everything else, but near things are more related than distant things”. Likewise, it is expected that the state of an object at a single moment is related to the state at every other moment, but temporally near attributes are more related than distant ones. This temporal autocorrelation is a vital source of prior information and can be used to improve the retrieval accuracy. In this study, we develop a retrieval framework that makes use of the temporal autocorrelation and dependence of land surface and atmospheric properties. We apply this retrieval algorithm to Sentinel-3 Ocean and Land Colour Instrument (OLCI) satellite data to derive land surface biophysical variables with a focus on leaf area index (LAI) from top-of-atmosphere (TOA) radiance observations. The results from both a synthetic dataset and a real satellite dataset show that the use of the temporal continuity as a priori information improves the accuracy of the estimation of land surface properties, such as leaf chlorophyll content and LAI. Compared with the MODIS LAI products, much less unrealistic short-term fluctuations are found in the LAI retrievals from OLCI with the time-series retrieval approach across different land cover types including cropland, forest and savannah. Field measurements of LAI at two forest sites quantitatively confirm that the estimated LAI from OLCI is reasonably accurate with R2 > 0.65 and RMSE < 1.00 m2m−2. Overall, the time series retrieval results in more robust and smoother time series than standard retrievals of LAI from individual scenes, more stable retrievals than the MODIS LAI product, and values of LAI that match better with reported measurements in the field. The present retrieval framework can make better use of time series of spectral observations and potentially of multi-sensor observations.
Highlights
Vegetation is a dynamic component in the Earth system and an essential factor in land-atmosphere interactions
We examined the corre lation of Ocean and Land Colour Instrument (OLCI) spectral observations acquired among pairs of days in an annual time series at the four study sites and found that the correlation coefficients generally decrease with increasing time interval (Fig. 11)
Time series of spectral observations from various satellite sensors allow a better monitoring of land surface properties
Summary
Vegetation is a dynamic component in the Earth system and an essential factor in land-atmosphere interactions. Translation of remote sensing signals to vegetation properties are often based on knowledge gained from radiative transfer modelling or empirical relationships between vegetation properties and remote sensing signals. Physically-based (e.g., look-up tables and numerical optimization) and statistical (e.g., regression models based on vegetation indices) approaches have been developed to retrieve vegetation biophysical variables (Berger et al 2020; Combal et al 2003; Darvishzadeh et al 2008; De Grave et al 2020; Verrelst et al 2015; Xiao et al 2014). Physically-based approaches rely on radiative transfer models (RTM) to link vegetation properties with remote sensing signals for various sun-observer geometries. Inversion of RTMs allows translating remote sensing signals to vegetation biophysical variables, but these models are usually nonlinear and sometimes complex. A more challenging and fundamental issue encountered in practice is the so-called ill-posedness of model inversion problems (Combal et al 2003; Quaife and Lewis 2010; Tarantola 2005; Weiss et al 2000), which is commonly expressed by the possibility that an observed spectrum can be reproduced by multiple different combinations of model parameters
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