The Fluorescence Explorer (FLEX) mission has been selected as ESA’s 8th Earth Explorer mission. The primary objectives of the mission are to provide global estimates of vegetation fluorescence, actual photosynthetic activity, and vegetation stress. FLEX will fly in tandem formation with Sentinel-3 providing ancillary data for atmospheric characterization and correction, vegetation related spectral indices, and land surface temperature. The purpose of this manuscript is to present its scientific payload, FLORIS, which is a push-broom hyperspectral imager, flying on a medium size platform. FLORIS will measure the vegetation fluorescence in the spectral range between 500 nm and 780 nm at medium spatial resolution (300 m) and over a swath of 150 km. It accommodates an imaging spectrometer with a very high spectral resolution (0.3 nm), to measure the fluorescence spectrum within two oxygen absorption bands (O_2A and O_2B), and a second spectrometer with lower spectral resolution to derive additional atmospheric and vegetation parameters. A compact opto-mechanical solution is the current instrument baseline. A polarization scrambler is placed in front of a common dioptric telescope serving both spectrometers to minimize the polarization sensitivity. The telescope images the ground scene onto a double slit assembly. The radiation is spectrally dispersed onto the focal planes of the grating spectrometers. Special attention has been given to the mitigation of stray-light which is a key factor to reach good accuracy of the fluorescence measurement. The absolute radiometric calibration is achieved by observing a dedicated Sun illuminated Lambertian diffuser, while the spectral calibration in flight is performed by means of vicarious techniques. The thermal stabilization is achieved by using two passive radiators looking directly to the cold space, counterbalanced by heaters in a closed loop system. The focal planes are based on custom developed CCDs. The opto-mechanical design is robust, stable vs. temperature and easy to align. The optical quality is very good as recently demonstrated by the latest tests of an elegant breadboard. The scientific data products comprise the Top Of Atmosphere (TOA) radiance measurements as well as fluorescence estimates and higher-level products related to the health status of the vegetation addressing a wide range of applications from agriculture to forestry and climate. Full article

Global surface soil moisture products are derived from passive L-band microwave satellite observations. The applied retrieval algorithms include dielectric models (relating soil water content to relative permittivity) developed for mineral soils. First efforts to generate equivalent models for areas where organic surface layers are present such as in the high-latitude regions have recently been undertaken. The objective of this study was to improve our still insufficient understanding of L-band emission of organic substrates in prospect of enhancing soil moisture estimations in the high latitudes undergoing most rapid climatic changes. To this end, L-band relative permittivity measurements using a resonant cavity were carried out on a wide range of organic surface layer types collected at different sites. This dataset was used to evaluate two already existing models for organic substrates. Some samples from underlying mineral layers were considered for comparison. In agreement with theory the bulk relative permittivity measured in organic substrate was decreased due to an increased bound water fraction (where water molecules are rotationally hindered) compared to the measured mineral material and corresponding output of the dielectric model for mineral soils used in satellite algorithms. No distinct differences in dielectric response were detected in the measurements from various organic layer types, suggesting a generally uniform L-band emission behavior. This made it possible to fit a simple empirical model to the data obtained from all collected organic samples. Outputs of the two existing models both based on only one organic surface layer type were found to lie within the spread of our measured data, and in close proximity to the derived simple model. This general consensus strengthened confidence in the validity of all these models. The simple model should be suitable for satellite soil moisture retrieval applications as it is calibrated on a wide range of organic substrate types and the entire wetness range, and does not require any auxiliary input that may be difficult to obtain globally. This renders it generically applicable wherever organic surface layers are present. Full article

A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer—Earth Observing System Sensor (AMSR-E) observations using Soil Moisture and Ocean Salinity (SMOS) Level 3 SM as a reference is discussed. The goal is to obtain longer time series of SM with no significant bias and with a similar dynamical range to that of the SMOS SM dataset. This method consists of training a neural network (NN) to obtain a global non-linear relationship linking AMSR-E brightness temperatures ( $T_{\mathrm{b}}$ ) to the SMOS L3 SM dataset on the concurrent mission period of 1.5 years. Then, the NN model is used to derive soil moisture from past AMSR-E observations. It is shown that in spite of the different frequencies and sensing depths of AMSR-E and SMOS, it is possible to find such a global relationship. The sensitivity of AMSR-E $T_{\mathrm{b}}$ ’s to soil temperature ( $T_{soil}$ ) was also evaluated using European Centre for Medium-Range Weather Forecast Interim/Land re-analysis (ERA-Land) and Modern-Era Retrospective analysis for Research and Applications-Land (MERRA-Land) model data. The best combination of AMSR-E $T_{\mathrm{b}}$ ’s to retrieve $T_{soil}$ is H polarization at 23 and 36 GHz plus V polarization at 36 GHz. Regarding SM, several combinations of input data show a similar performance in retrieving SM. One NN that uses C and X bands and $T_{soil}$ information was chosen to obtain SM in the 2003–2011 period. The new dataset shows a low bias (<0.02 m³/m³) and low standard deviation of the difference (<0.04 m³/m³) with respect to SMOS L3 SM over most of the globe’s surface. The new dataset was evaluated together with other AMSR-E SM datasets and the Climate Change Initiative (CCI) SM dataset against the MERRA-Land and ERA-Land models for the 2003–2011 period. All datasets show a significant bias with respect to models for boreal regions and high correlations over regions other than the tropical and boreal forest. All of the global SM datasets including AMSR-E NN were also evaluated against a large number of in situ measurements over four continents. Over Australia, all datasets show a strong level of agreement with in situ measurements. Models perform better over Europe and mountainous regions in North America. Remote sensing datasets (in particular NN and the Land Parameter Retrieval Model (LPRM)) perform as well as models for other North American sites and perform better than models over the Sahel region. Full article