This manuscript explores the spatio-temporal trade-off in thermal remote sensing and advocates for sub-daily Land Surface Temperature (LST) measurements. We present a novel approach to dynamic temperature measurements based on OroraTech’s growing constellation of cost-efficient cubesats equipped with thermal infrared sensors. With this, we aim to overcome current limitations in spatial and temporal LST coverage and pave the way for a new era of thermal remote sensing capabilities. Emphasizing the significance of high-frequency LST data, we showcase its applications in dynamic land processes, such as irrigation management, soil organic carbon estimation, wildfire risk prediction, and drought monitoring. Furthermore, we outline the design and capabilities of our constellation, focusing on thermal anomaly detection and unprecedented revisit frequencies. Further, we provide an early validation of LST products from FOREST-2, which is our precursor mission for the full constellation of sensors. Finally, we discuss the potential of data fusion techniques to further increase the spatial resolution for certain use cases. Our forthcoming work aims to demonstrate the potential downstream performance of a sub-daily LST data product, providing valuable insights for various scientific and practical applications.

Earth observation is a fundamental tool for monitoring the evolution of land use in specific areas of interest. Observing and precisely defining change, in this context, requires both time-series data and pixel-wise segmentations. To that end, we propose the DynamicEarthNet dataset that consists of daily, multi-spectral satellite observations of 75 selected areas of interest distributed over the globe with imagery from Planet Labs. These observations are paired with pixel-wise monthly semantic segmentation labels of 7 land use and land cover (LULC) classes. DynamicEarthNet is the first dataset that provides this unique combination of daily measurements and high-quality labels. In our experiments, we compare several established baselines that either utilize the daily observations as additional training data (semi-supervised learning) or multiple observations at once (spatio-temporal learning) as a point of reference for future research. Finally, we propose a new evaluation metric SCS that addresses the specific challenges associated with time-series semantic change segmentation. The data is available at: https://mediatum.ub.tum.de/1650201.

Recent developments in deep learning have pushed the capabilities of pixel-wise change detection. This work introduces the winning solution of the DynamicEarthNet Weakly-Supervised Multi-Class Change Detection Challenge held at the EARTHVISION Workshop in CVPR 2021. The proposed approach is a pixel-wise change detection network coined Siamese Attention U-Net that incorporates attention mechanisms in the Siamese U-Net architecture. Moreover, this work finds the location of the attention mechanism within the network is crucial in achieving higher performance. Positioning the attention blocks in the up-sample path of the decoder filters noisy lower resolution features and allows for more fine-grained outputs. The impact of architectural changes, alongside training strategies such as semi-supervised learning are also evaluated on the DynamicEarthNet Challenge dataset.11Code is available at: https://github.com/solcummings/earthvision2021-weakly-supervised.

In this paper, we compare trends in air quality measured by official stations on the ground with remote-sensing-based measurements in Munich, Germany. With Earth Observation data from Sentinel-5P and Sentinel-2, this study investigates the discrepancies that may arise in trends and pollutant levels of NO2. We find that the defined fixed measuring stations in Munich cover the worst pollution levels in the city. However, we do also find inconsistencies in the information provided by ground measurements regarding pollutant concentrations in contrast to high spatial coverage remote sensing data.

Nighttime light (NTL) emissions are widely used across disciplines to map the spatial distribution of a variety of socioeconomic variables. For economic studies, NTLs allow to proxy for levels of economic indicators at the country level as well as the local level. Further, multi-temporal differences in NTL intensity are also related to GDP differences on the country level. In this study, we investigate if this relation in temporal differences also holds for the local level. We test this with DMSP as well as VIIRS NTLs data from 2010–2015 in Nigeria, Tanzania, and Uganda. Even though we successfully map local levels of socio-economic status with NTLs, we find multi-temporal changes in NTLs at this local level to be uncorrelated with socio-economic development over time. We conclude that luminosity values based on current DMSP and VIIRS sensors are no silver bullet in measuring local economic changes over time and should, if at all, only be used with caution for this.