Description: This dataset is composed of three-season simulated EnMAP mosaics for the Santa Barbara region, USA. HyspIRI Airborne Campaign AVIRIS imagery from spring, summer and fall formed the basis for simulating EnMAP data with 30 m spatial resolution and 195 spectral bands ranging from 420 to 2450 nm. The mosaics are provided as Analysis-Ready-Datasets (tiled surface reflectance products) to be used for regional-scale and multi-season hyperspectral image analysis of California’s diverse ecoregions. The dataset primarily intends to support the development of processing algorithms and to demonstrate spaceborne hyperspectral data capabilities during the pre-launch activities of the forthcoming EnMAP mission. This dataset was processed in line with companion simulated EnMAP mosaics for the San Francisco Bay Area (Cooper et al. 2020a) and for the Lake Tahoe region (Okujeni et al. 2021a).

Description: Multi-sensor remote sensing applications consistently gain importance, boosted by a growing number of freely available earth observation data, increasing computing capacity, and increasingly complex algorithms that need as temporally dense data as possible. Using data provided by different sensors can greatly improve the temporal resolution of time series, fill data gaps and thus improve the quality of land cover monitoring applications. However, multi-sensor approaches are often adversely affected by different spectral characteristics of the sensing instruments, leading to inconsistencies in downstream products. Spectral harmonization, i.e., the transformation of one sensor into the spectral domain of another sensor, may reduce these inconsistencies. It simplifies workflows, increases the reliability of subsequently derived multi-sensor products and may also enable the generation of new products that are not possible with the initial spectral definition. In this paper, we compare the effect of multivariate spectral harmonization techniques on the inter-sensor reflectance consistency and derived products such as spectral indices or land cover classifications. We simulated surface reflectance data of Landsat-8 and Sentinel-2A from airborne hyperspectral data to eliminate any sources of error originating from unequal acquisition geometries, illumination or atmospheric state. We evaluate different methods based on linear, quadratic and random forest regression as well as linear interpolation, and predict not only matching but also unilaterally missing bands (red edge). We additionally consider material-dependent spectral characteristics in the harmonization process by using separate transformation functions for spectral clusters of the input dataset. Our results suggest that spectral harmonization is useful to improve multi-sensor consistency of remote sensing data and subsequently derived products, especially if multiple transformation functions are incorporated. There is a strong dependency between harmonization performance and the similarity of source and target sensor's spectral characteristics. For spectrally transforming Landsat-8 to Sentinel-2A, we achieved the lowest radiometric inter-sensor deviations with 50 spectral clusters and linear regression. Based on simulated data, deviations are below 1.7% reflectance within the red edge spectral region and below 0.3% reflectance for the remaining bands (RMSE). Regarding spectral indices, our results show a reduction of inter-sensor deviation (vegetation pixels only) to 38% of the initial error for NDVI (Normalized Difference Vegetation Index) and to 43% for EVI (Enhanced Vegetation Index). Furthermore, we computed the REIP (Red Edge Inflection Point) with an accuracy of 3.1 nm from Sentinel-2 adapted Landsat-8 data. An exemplary multispectral classification use case revealed an increasing inter-sensor consistency of classification results from 92.3% to 97.3% mean error. Applied to time series of real Landsat-8 and Sentinel-2 data, we observed similar trends, albeit intermingled with non-sensor-induced inconsistencies.

Description: Measurements of reflected solar radiation by imaging spectrometers can quantify water in different states (solid, liquid, gas) thanks to the discriminative absorption shapes. We developed a retrieval method to quantify the amount of water in each of the three states from spaceborne imaging spectroscopy data, such as those from the German EnMAP mission. The retrieval couples atmospheric radiative transfer simulations from the MODTRAN5 radiative transfer code to a surface reflectance model based on the Beer-Lambert law. The model is inverted on a per-pixel basis using a maximum likelihood estimation formalism. Based on a unique coupling of the canopy reflectance model HySimCaR and the EnMAP end-to-end simulation tool EeteS, we performed a sensitivity analysis by comparing the retrieved values with the simulation input leading to an R2 of 0.991 for water vapor and 0.965 for liquid water. Furthermore, we applied the algorithm to airborne AVIRIS-C data to demonstrate the ability to map snow/ice extent as well as to a CHRIS-PROBA dataset for which concurrent field measurements of canopy water content were available. The comparison between the retrievals and the ground measurements showed an overall R2 of 0.80 for multiple crop types and a remarkable clustering in the regression analysis indicating a dependency of the retrieved water content from the physical structure of the vegetation. In addition, the algorithm is able to produce smoother and more physically-plausible water vapor maps than the ones from the band ratio approaches used for multispectral data, since biases due to background reflectance are reduced. The demonstrated potential of imaging spectroscopy to provide accurate quantitative measures of water from space will be further exploited using upcoming spaceborne imaging spectroscopy missions like PRISMA or EnMAP.

Description: In preparation of the German spaceborne imaging spectroscopy mission EnMAP (The Environmental Mapping and Analysis Program) and its upcoming launch in early 2022, the data product validation activities have been intensified. As part of the science preparation and mission support project led by the German Research Center (GFZ) Potsdam, the overall quality of the official EnMAP products has to be accessed and evaluated independently from the data quality control activities performed by the Ground Segment at DLR EOC. Therefore, the radiometric, spectral, reflective, geometric and general quality of the three official EnMAP products (L1B, L1C and L2A) has to be validated during the commissioning and nominal phase.This paper presents an update of the data product validation activities, an in-depth insight into the overall approach and into specifically designed methods described in the EnMAP Product Validation Plan.

Description: This dataset is composed of three-season simulated EnMAP mosaics for the Santa Barbara region, USA. HyspIRI Airborne Campaign AVIRIS imagery from spring, summer and fall formed the basis for simulating EnMAP data with 30 m spatial resolution and 195 spectral bands ranging from 420 to 2450 nm. The mosaics are provided as Analysis-Ready-Datasets (tiled surface reflectance products) to be used for regional-scale and multi-season hyperspectral image analysis of California’s diverse ecoregions. The dataset primarily intends to support the development of processing algorithms and to demonstrate spaceborne hyperspectral data capabilities during the pre-launch activities of the forthcoming EnMAP mission. This dataset was processed in line with companion simulated EnMAP mosaics for the San Francisco Bay Area and for the Lake Tahoe region.