Essential climate variables (ECVs) have been recognized as crucial information for achieving Sustainable Development Goals (SDGs). There is an agreement on 54 ECVs to understand climate evolution, and multiple rely on satellite Earth observation (abbreviated as s-ECVs). Despite the efforts to encourage s-ECV use for SDGs, there is still a need to further integrate them into the indicator calculations. Therefore, we conducted a systematic literature review to identify s-ECVs used in SDG monitoring. Results showed the use of 14 s-ECVs, the most frequent being land cover, ozone, precursors for aerosols and ozone, precipitation, land surface temperature, soil moisture, soil carbon, lakes, and leaf area index. They were related to 16 SDGs (mainly SDGs 3, 6, 11, 14, and 15), 33 targets, and 23 indicators. However, only 10 indicators (belonging to SDGs 6, 11, and 15) were calculated using s-ECVs. This review raises research opportunities by identifying s-ECVs yet to be used in the indicator calculations. Therefore, indicators supporting SDGs must be updated to use this valuable source of information which, in turn, allows a worldwide indicator comparison. Additionally, this review is relevant for scientists and policymakers for future actions and policies to better integrate s-ECVs into the Agenda 2030.

A new satellite-based technique for rainfall retrieval in high spatio-temporal resolution (3 km, 15 min) for Iran is presented. The algorithm is based on the infrared bands of the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG SEVIRI). Random forest models using microwave-only rainfall information of the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) product as a reference were developed to (i) delineate the rainfall area and (ii) to assign the rainfall rate. The method was validated against independent microwave-only GPM IMERG rainfall data not used for model training. Additionally, the new technique was validated against completely independent gauge station data. The validation results show a promising performance of the new rainfall retrieval technique, especially when compared to the GPM IMERG IR-only rainfall product. The standard verification scored an average Heidke Skill Score of 0.4 for rain area delineation and an average R between 0.1 and 0.7 for rainfall rate assignment, indicating uncertainties for the Lut Desert area and regions with high altitude gradients.

A new satellite-based algorithm for rainfall retrieval in high spatio-temporal resolution for Ecuador is presented. The algorithm relies on the precipitation information from the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) and infrared (IR) data from the Geostationary Operational Environmental Satellite-16 (GOES-16). It was developed to (i) classify the rainfall area (ii) assign the rainfall rate. In each step, we selected the most important predictors and hyperparameter tuning parameters monthly. Between 19 April 2017 and 30 November 2017, brightness temperature derived from the GOES-16 IR channels and ancillary geo-information were trained with microwave-only IMERG-V06 using random forest (RF). Validation was done against independent microwave-only IMERG-V06 information not used for training. The validation results showed the new rainfall retrieval technique (multispectral) outperforms the IR-only IMERG rainfall product. This offers using the multispectral IR data can improve the retrieval performance compared to single-spectrum IR approaches. The standard verification scored a median Heidke skill score of ~0.6 for the rain area delineation and R between ~0.5 and ~0.62 for the rainfall rate assignment, indicating uncertainties for Andes’s high elevation. Comparison of RF rainfall rates in 2 km² resolution with daily rain gauge measurements reveals the correlation of R = ~0.33.

Ground based rainfall information is hardly available in most high mountain areas of the world due to the remoteness and complex topography. Thus, proper understanding of spatio-temporal rainfall dynamics still remains a challenge in those areas. Satellite-based rainfall products may help if their rainfall assessment are of high quality. In this paper, microwave-based integrated multi-satellite retrieval for the Global Precipitation Measurement (GPM) (IMERG) (MW-based IMERG) was assessed along with the random-forest-based rainfall (RF-based rainfall) and infrared-only IMERG (IR-only IMERG) products against the quality-controlled rain radar network and meteorological stations of high temporal resolution over the Pacific coast and the Andes of Ecuador. The rain area delineation and rain estimation of each product were evaluated at a spatial resolution of 11 km² and at the time of MW overpass from IMERG. The regionally calibrated RF-based rainfall at 2 km² and 30 min was also investigated. The validation results indicate different essential aspects: (i) the best performance is provided by MW-based IMERG in the region at the time of MW overpass; (ii) RF-based rainfall shows better accuracy rather than the IR-only IMERG rainfall product. This confirms that applying multispectral IR data in retrieval can improve the estimation of rainfall compared with single-spectrum IR retrieval algorithms. (iii) All of the products are prone to low-intensity false alarms. (iv) The downscaling of higher-resolution products leads to lower product performance, despite regional calibration. The results show that more caution is needed when developing new algorithms for satellite-based, high-spatiotemporal-resolution rainfall products. The radar data validation shows better performance than meteorological stations because gauge data cannot correctly represent spatial rainfall in complex topography under convective rainfall environments.

Climate change is expected to increase the frequency and intensity of heavy rainfall events (HRE) globally. In the Galápagos Islands, HREs have caused severe impacts such as infrastructure loss and ecosystem damage, yet their occurrence remains poorly understood. This study identifies 137 HREs between 1985 and 2024 and demonstrates that local sea surface temperatures (SST) from a new regional SST index, called the Galápagos Regional Niño Index (GReNI), correlate more strongly with HREs than widely used large‐scale El Niño‐Southern Oscillation indices. We analyzed the rainfall and 10 m wind fields from the Galápagos Archipelago Refined Analysis data set and find that while HREs occur outside Pacific‐wide El Niño events, they are generally strongest during local GReNI El Niño‐like phases. These findings highlight how local SST anomalies influence mesoscale rainfall, suggesting local rather than large‐scale SST are significant HRE modulators.

Accurate rainfall information in high spatio-temporal resolution is important for water resource management, particularly in water-scarce remote areas which are characterized by a coarse network of operational precipitation gauge stations. For such regions, satellite-based rainfall products potentially represent a source for reliable and area-wide data on rainfall. The poster presents a new satellite-based precipitation algorithm for semi-arid regions in Ecuador with the elevation range between 12 to 5700 a.s.l. The algorithm relies on the combination of  precipitation information from the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) and infrared (IR) data from the Geostationary Operational Environmental Satellite 16 (GOES-16). The algorithm is developed to (i) classify the rainfall area and then (ii) to assign the rainfall rate. For the period between 19.04.2017 to 19.04.2018 the brightness temperature derived from GOES-16 IR channels and ancillary geo-information are trained with microwave only rainfall information of the half-hourly IMERG-V06 product using the machine learning algorithm random forest. The validation is done against independent microwave-only IMERG-V06 rainfall data not used for model training and available gauge stations. The validation results show overall very good accuracy of the new rainfall retrieval technique in this case study, mostly in comparison with the GPM IMERG IR-only rainfall product. The product offers the potential for high spatio-temporal (2 km, 15 min) rainfall resolution in near real-time for Ecuador.