Quantifying the accuracy of remote sensing products is a timely endeavor given the rapid increase in Earth observation missions. A validation site for Sentinel-2 products was hence established in central Germany. Automatic multispectral and hyperspectral sensor systems were installed in parallel with an existing eddy covariance flux tower, providing spectral information of the vegetation present at high temporal resolution. Normalized Difference Vegetation Index (NDVI) values from ground-based hyperspectral and multispectral sensors were compared with NDVI products derived from Sentinel-2A and Moderate-resolution Imaging Spectroradiometer (MODIS). The influence of different spatial and temporal resolutions was assessed. High correlations and similar phenological patterns between in situ and satellite-based NDVI time series demonstrated the reliability of satellite-based phenological metrics. Sentinel-2-derived metrics showed better agreement with in situ measurements than MODIS-derived metrics. Dynamic filtering with the best index slope extraction algorithm was nevertheless beneficial for Sentinel-2 NDVI time series despite the availability of quality information from the atmospheric correction procedure.

The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic radiometric correction for long time series of remote sensing imagery, as shown in previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery. In addition, they can be employed to obtain more coherence among remote sensing data from different sensors. The present work validates the use of PIA for the radiometric correction of pairs of images acquired almost simultaneously (Landsat-7 (ETM+) or Landsat-8 (OLI) and Sentinel-2A (MSI)). Four pairs of images from a region in SW Spain, corresponding to four different dates, together with field spectroradiometry measurements collected at the time of satellite overpass were used to evaluate a PIA-based radiometric correction. The results show a high coherence between sensors (r² = 0.964) and excellent correlations to in-situ data for the MiraMon implementation (r² > 0.9). Other methodological alternatives, ATCOR3 (ETM+, OLI, MSI), SAC-QGIS (ETM+, OLI, MSI), 6S-LEDAPS (ETM+), 6S-LaSRC (OLI), and Sen2Cor-SNAP (MSI), were also evaluated. Almost all of them, except for SAC-QGIS, provided similar results to the proposed PIA-based approach. Moreover, as the PIA-based approach can be applied to almost any image (even to images lacking of extra atmospheric information), it can also be used to solve the robust integration of data from new platforms, such as Landsat-8 or Sentinel-2, to enrich global data acquired since 1972 in the Landsat program. It thus contributes to the program’s continuity, a goal of great interest for the environmental, scientific, and technical community.

Maps of ecosystem service (ES) supply are frequently used to guide spatial planning, policy making, and ecosystem management. However, these are typically based upon coarse land-cover proxies. This approach lacks a strong mechanistic basis, and neglects spatial biodiversity dynamics and interactions among landscape properties that can modify ES provision. We present an analytical framework for ES upscaling that incorporates spatial interactions between landscape properties to determine ES supply. The resulting models can be viewed as a spatially informed ES production function. The approach comprises seven steps that include several elements absent from most existing approaches, notably a procedure for identifying geodata variables that represent the true mechanistic drivers, the inclusion of spatial interactions in the upscaling model, and modification following expert feedback on the selected model. We demonstrate the approach using two example ES from German grasslands: biodiversity conservation and water supply. We show that the inclusion of spatial interactions in the upscaling model improved model predictions from 15% to 33% depending on the ES evaluated. In addition, inclusion of spatial interactions led to reduced error associated with the upscaled estimates. By overcoming several shortcomings of existing, upscaling approaches we generate resulting maps of ES supply that can more reliably inform spatial planning Further, the underlying models allow for simulation of changes in the drivers of ES supply and estimation of respective outcomes. These advantages have the potential to better link detailed local-scale ecological understanding and land management with large-scale ES supply mapping, and thus better inform decision making and spatial planning.