## 1 Introduction
However, attempts to use the SCOPE model to estimate temporal variation in GPP of a forest stand by exploring the potential of combined remote sensing data are lacking. The SCOPE model integrates radiative transfer, micrometeorology, and plant physiology to perform land surface process (e.g., GPP) simulations [20]. The SCOPE model uses a modular architecture, making it flexible and allowing the use of different modules as standalone instances. The radiative transfer module (RTMo) can be calibrated, i.e., a data-assimilation approach, against the surface reflectance obtained from remote sensing data to optimize its input parameters. These optimized parameters are the retrieved vegetation biochemical and structural properties. The retrieved information, together with the time series of meteorological variables, can be then directly used as inputs for the SCOPE model for GPP estimation. In general, the vegetation properties at one point in time can yield a rough estimate of GPP. However, temporal variations in the vegetation properties (i.e., vegetation phenology) are also needed to reflect the vegetation responses to climate variability and were found to improve the temporal GPP estimated by the SCOPE model [20; 22]. Accurate description of the temporal variations in vegetation properties depends on the availability of rich time series of high quality remote sensing data. A single sensor may not provide the needed temporal coverage due to cloud cover and sensor temporal revisit limitations. More than 300 Earth observation satellites for optical imaging are orbiting the Earth, allowing us to combine observations from multiple sensors for improved temporal coverage. However, a simple combination of observations does not result in smooth spectral time series, e.g., due to the differences in radiometric characteristics and mismatch in spatial resolution between the sensors. It is not always suitable to assess the vegetation phenology and retrieve consistent time series of vegetation characteristics from the combined datasets. Recently, the National Aeronautics and Space Administration (NASA) produced the harmonized Landsat and Sentinel (HLS) data set by combining the surface reflectance data from the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) sensors aboard Landsat-8 and Sentinel-2 satellites, respectively [30], to further improve the temporal resolution of the combined product. In HLS data, a smooth spectral time series is produced by accounting for the differences in spatial resolution, sensor-to-sensor differences in atmospheric correction approaches, and view geometry and radiometric characteristics of spectral bands. The potential of HLS time series data has not yet been fully explored with respect to GPP estimations. Recently, [PERSON] et al. [31] applied a data-driven approach using HLS data to evaluate GPP variations in natural vegetation types. However, the full potential of HLS time series data has not yet been explored with respect to GPP estimation using data-assimilation approaches. The objective of this study was to fill this gap by assimilating multi-sensor spectral information obtained from the HLS data within the SCOPE model to retrieve the temporal variations in biochemical and structural vegetation properties to evaluate the improvement in GPP estimation. This research addresses the following two questions: (1) What information is preserved or lost in the retrieval of vegetation properties using multispectral HLS data? (2) What improvement in the GPP estimation can be expected by providing the SCOPE model with the temporal variations in the vegetation properties? We assumed that HLS multispectral data can provide sufficient information for retrieval of vegetation properties. However, whether such information can improve GPP estimation needs more investigation. We aimed to gain insights regarding these two aspects.