The MEthane Remote sensing Lidar missioN (MERLIN) aims at demonstrating the spaceborne active measurement of atmospheric methane, a potent greenhouse gas, based on an Integrated Path Differential Absorption (IPDA) nadir-viewing LIght Detecting and Ranging (Lidar) instrument. MERLIN is a joint French and German space mission, with a launch currently scheduled for the timeframe 2021/22. The German Space Agency (DLR) is responsible for the payload, while the platform (MYRIADE Evolutions product line) is developed by the French Space Agency (CNES). The main scientific objective of MERLIN is the delivery of weighted atmospheric columns of methane dry-air mole fractions for all latitudes throughout the year with systematic errors small enough (<3.7 ppb) to significantly improve our knowledge of methane sources from global to regional scales, with emphasis on poorly accessible regions in the tropics and at high latitudes. This paper presents the MERLIN objectives, describes the methodology and the main characteristics of the payload and of the platform, and proposes a first assessment of the error budget and its translation into expected uncertainty reduction of methane surface emissions.

A method has been developed to extend the Global Inundation Estimate from Multiple Satellites (GIEMS). The method presented here is based on retrieval principals similar to GIEMS but with an updated estimation of microwave emissivity in order to be less dependent on ancillary data and with some changes to the final surface water estimation to correct a known overestimation over low vegetation areas. The new methodology, GIEMS‐2, provides monthly estimates of surface water extent, including open water, wetlands, or rice paddies, and it has been applied to the Special Sensor Microwave/Imager and the Special Sensor Microwave Imager Sounder intercalibrated observations to produce a global data record of surface water extent from 1992 to 2015, on an equal area grid of 0.25° × 0.25° at the equator (∼25 km). The time series have been thoroughly evaluated: they are seamless and do not show any obvious artifact related to changes in satellite instrumentation over the ∼25 years. Comparisons with precipitation estimates show good agreement, displaying expected patterns related to surface conditions and precipitation regimes. The temporal variability of basin‐averaged estimates has also been compared with altimeter river height, showing a reasonable agreement. Production will be continued up to current time as soon as the observations become available, with efforts to improve the spatial and temporal resolutions of the estimates currently underway.

This study presents a methodology to detect and monitor surface water with Sentinel-1 Synthetic Aperture Radar (SAR) data within Cambodia and the Vietnamese Mekong Delta. It is based on a neural network classification trained on Landsat-8 optical data. Sensitivity tests are carried out to optimize the performance of the classification and assess the retrieval accuracy. Predicted SAR surface water maps are compared to reference Landsat-8 surface water maps, showing a true positive water detection of ∼90% at 30 m spatial resolution. Predicted SAR surface water maps are also compared to floodability maps derived from high spatial resolution topography data. Results show high consistency between the two independent maps with 98% of SAR-derived surface water located in areas with a high probability of inundation. Finally, all available Sentinel-1 SAR observations over the Mekong Delta in 2015 are processed and the derived surface water maps are compared to corresponding MODIS/Terra-derived surface water maps at 500 m spatial resolution. Temporal correlation between these two products is very high (99%) with very close water surface extents during the dry season when cloud contamination is low. This study highlights the applicability of the Sentinel-1 SAR data for surface water monitoring, especially in a tropical region where cloud cover can be very high during the rainy seasons.

Climate change resulting in ocean warming, sea level rise, and sea ice melting has consequences for the global economy, navigation, and security. The Copernicus Imaging Microwave Radiometer (CIMR) mission is a high priority candidate mission within the European Copernicus Expansion program. CIMR is designed to observe the ocean and sea ice and more particularly the Arctic environment. Sea surface temperature (SST), ocean wind speed, sea surface salinity (SSS), and sea ice concentration (SIC) are fundamental variables for understanding, monitoring, and predicting the state of the ocean and sea ice. CIMR is a conically scanning microwave radiometer imager that includes channels at 1.4, 6.9, 10.65, 18.7, and 36.5 GHz, in a Sun‐synchronous polar orbit, to provide SST, ocean wind speed, SSS, and SIC with an increased accuracy and/or spatial resolution. Here we analyze the performances of the CIMR mission in terms of theoretical retrieval precision and spatial resolution on the SST, SSS, and SIC products. A careful information content analysis is conducted. The CIMR performances are compared with the Advanced Microwave Scanning Radiometer 2 and the Soil Moisture Active Passive current missions. Maps of the retrieval precision based on realistic conditions are computed. CIMR will provide SST, SSS, and SIC with a spatial resolution of 15, 55, and 5 km and a precision of 0.2 K, 0.3 psu, and 5%, respectively. The SST and SIC will be retrieved at better than 30 km from the coast. CIMR is currently in preparatory phase, and if selected, it is for a launch in the 2025+ time frame.