High spatial resolution maps of Los Angeles, California are needed to capture the heterogeneity of urban land cover while spanning the regional domain used in carbon and water cycle models. We present a simplified framework for developing a high spatial resolution map of urban vegetation cover in the Southern California Air Basin (SoCAB) with publicly available satellite imagery. This method uses Sentinel-2 (10–60 × 10–60 m) and National Agriculture Imagery Program (NAIP) (0.6 × 0.6 m) optical imagery to classify urban and non-urban areas of impervious surface, tree, grass, shrub, bare soil/non-photosynthetic vegetation, and water. Our approach was designed for Los Angeles, a geographically complex megacity characterized by diverse Mediterranean land cover and a mix of high-rise buildings and topographic features that produce strong shadow effects. We show that a combined NAIP and Sentinel-2 classification reduces misclassified shadow pixels and resolves spatially heterogeneous vegetation gradients across urban and non-urban regions in SoCAB at 0.6–10 m resolution with 85% overall accuracy and 88% weighted overall accuracy. Results from this study will enable the long-term monitoring of land cover change associated with urbanization and quantification of biospheric contributions to carbon and water cycling in cities.

It is important to understand the distribution of irrigated and non-irrigated vegetation in rapidly expanding urban areas that are experiencing climate-induced changes in water availability, such as Los Angeles, California. Mapping irrigated vegetation in Los Angeles is necessary for developing sustainable water use practices and accurately accounting for the megacity’s carbon exchange and water balance changes. However, pre-existing maps of irrigated vegetation are largely limited to agricultural regions and are too coarse to resolve heterogeneous urban landscapes. Previous research suggests that irrigation has a strong cooling effect on vegetation, especially in semi-arid environments. The July 2018 launch of the ECOsystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS) offers an opportunity to test this hypothesis using retrieved land surface temperature (LST) data in complex, heterogeneous urban/non-urban environments. In this study, we leverage Landsat 8 optical imagery and 30 m sharpened afternoon summertime ECOSTRESS LST, then apply very high-resolution (0.6–10 m) vegetation fraction weighting to produce a map of irrigated and non-irrigated vegetation in Los Angeles. This classification was compared to other classifications using different combinations of sensors in order to offer a preliminary accuracy and uncertainty assessment. This approach verifies that ECOSTRESS LST data provides an accurate map (98.2% accuracy) of irrigated urban vegetation in southern California that has the potential to reduce uncertainties in regional carbon and hydrological cycle models.

Recent advances in remote imaging spectroscopy have increased their utility for detecting and quantifying greenhouse gas emissions. In fact, multiple airborne [1] and space-based [2], [3], [4], [5] instruments are actively used to estimate methane emissions. Many of these measurements are made using matched-filter-based detection and estimation algorithms. In this work, we present new methods for quantifying and improving the accuracy and uncertainty of these algorithms. Two new metrics are proposed that capture the biases and uncertainties in gas quantity measurements stemming from local surface and atmospheric variation, observation and solar geometries, and sensor noise. We show that one of these, termed the “sensitivity,” can be used to correct the bias in the gas concentration length estimates due to variable atmospheres and backgrounds, reducing the estimator’s RMS error in spectra that deviate from the mean spectrum. The second, termed the “uncertainty,” represents the bias-removed statistical uncertainty in the corrected estimator. Expressions for the RMS error both with and without the correction are provided along with interpretation to help quantify the various noise sources. The utility of the metrics is demonstrated using data from the Earth Surface Mineral Dust Source Investigation (EMIT) [6] imaging spectrometer currently collecting Earth observations onboard the International Space Station. The EMIT data also demonstrates the potential accuracy increase afforded by the sensitivity correction over variable surface types. These metrics and their concomitant estimator accuracy increases could prove valuable for future work in quantifying gas source emission rates and their uncertainties, instrument design, and machine-learning-based detection methods.