We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in the ground-truth height measurements, and employs data from the Shuttle Radar Topography Mission to effectively filter out erroneous labels in mountainous regions, enhancing the reliability of our predictions in those areas. A comparison between predictions and ground-truth labels yields an MAE/RMSE of 2.43 / 4.73 (meters) overall and 4.45 / 6.72 (meters) for trees taller than five meters, which depicts a substantial improvement compared to existing global-scale products. The resulting height map as well as the underlying framework will facilitate and enhance ecological analyses at a global scale, including, but not limited to, large-scale forest and biomass monitoring.

Decade years of efforts on the retrieval of soil moisture based on radiative transfer model have largely improved the accuracy of soil moisture (SM). This paper focus on the other parameter, namely vegetation optical depth (VOD). We retrieved X-band VOD from AMSR-E and AMSR2 observations by inverting the L-MEB model (Wigneron et al. 2007 [1]) at X-band, considering that SM was known. As SM input to the L-MEB inversion we used the ECMWF SM product. This step avoids correlation between VOD and SM retrievals from the mono-angular AMSR-E observations. In a first step we evaluated the retrieved VOD with the Copernicus Global Land Service (CGLS) LAI. The evaluation results indicate our model has a great potential for VOD retrievals from AMSR-E/2 satellite data.

Massive amounts of satellite data have been gathered over time, holding the potential to unveil a spatiotemporal chronicle of the surface of Earth. These data allow scientists to investigate various important issues, such as land use changes, on a global scale. However, not all land-use phenomena are equally visible on satellite imagery. In particular, the creation of an inventory of the planet's road infrastructure remains a challenge, despite being crucial to analyze urbanization patterns and their impact. Towards this end, this work advances data-driven approaches for the automatic identification of roads based on open satellite data. Given the typical resolutions of these historical satellite data, we observe that there is inherent variation in the visibility of different road types. Based on this observation, we propose two deep learning frameworks that extend state-of-the-art deep learning methods by formalizing road detection as an ordinal classification task. In contrast to related schemes, one of the two models also resorts to satellite time series data that are potentially affected by missing data and cloud occlusion. Taking these time series data into account eliminates the need to manually curate datasets of high-quality image tiles, substantially simplifying the application of such models on a global scale. We evaluate our approaches on a dataset that is based on Sentinel 2 satellite imagery and OpenStreetMap vector data. Our results indicate that the proposed models can successfully identify large and medium-sized roads. We also discuss opportunities and challenges related to the detection of roads and other infrastructure on a global scale.

Land degradation in drylands has been investigated extensively over recent decades and several remote sensing based techniques attempt to decouple the human influence from the natural climate variability, but are contested in literature. We introduce a novel approach termed SeRGS-TREND that is designed to monitor land degradation by suppressing the impact from climate variability and highlight vegetation disturbances may it be human or climate-induced. SeRGS-TREND is based on the interpretation of the slope of a linear regression analysis within a sequentially moving window along the temporal axis of the time series of remote sensing data. The use of a moving window increases the probability of a statistically significant linear vegetation-rainfall relationship (VRR), which in turn provides an improved statistical basis for the results produced and thereby confidence in the assessment of degradation. We test and compare SeRGS-Trend and the commonly used RESTREND by simulating different degradation scenarios and find that SeRGS reveals both, more significant and more exact information about degradation events (e.g. starting and end point) while keeping the VRR correlation coefficients high, thus rendering results more reliable.

Tropical aboveground carbon changes during 2010–2016 were estimated by a newly developed vegetation optical depth (VOD) product retrieved from the low-frequency L-band (1.4 GHz) passive microwave observations from the Soil Moisture and Ocean salinity (SMOS) satellite. The aboveground carbon changes estimated by VOD in the tropical region during 2010–2016 indicate the tropical region acts as a net carbon source of 111 Tg C yr-1 during 2010–2016. The declines in tropical aboveground carbon were found mainly in eastern America, African drylands and Indonesia.

The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infra-red vegetation indices. This study is devoted to the characterisation of a new L-Band (1.4 GHz) VOD dataset (SMOS-IC L-VOD) obtained from the SMOS (Soil Moisture and Ocean Salinity) satellite. SMOS IC L-VOD is evaluated through a comparison with several vegetation-related quantities such as tree height and above ground biomass (AGB) for different land cover types. SMOS L-VOD shows monotonic relationships with respect to the variables extracted from these different datasets without signs of saturation at high values. The relationships between L-VOD and AGB were also compared to those obtained using the Normalized Difference Vegetation Index (NDVI) and K/X/C-VOD (VOD measured at 19, 10.7, and 6.9 GHz). In contrast to NDVI and K/X/C-VOD, L-VOD shows a relationship to AGB that is closer to a linear one without significant signs of saturation. SMOS L-VOD is a very promising dataset for large scale monitoring of biomass, at coarse scale spatial resolution (~ 40 km), but with high temporal resolution and with an improved sensitivity with respect to higher-frequency VOD data.

In 2017, the new SMOS-IC retrieval product of soil moisture (SM) and L-band Vegetation Optical depth (L-VOD) was developed. This product relies on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which requires little ancillary information and was found to be accurate, making it very well-suited for application in agriculture, hydrology, climate and vegetation monitoring. In this communication we present recent improvements in the SMOS-IC retrieval algorithm and recent applications using the soil moisture or VOD retrievals from the SMOS-IC data set. SMOS-IC SM is available at the French CATDS center.