Using imaging spectroscopy to detect variation in terrestrial ecosystem productivity across a water-stressed landscape.

A central challenge to understanding how climate anomalies, such as drought and heatwaves, impact the terrestrial carbon cycle, is quantification and scaling of spatial and temporal variation in ecosystem gross primary productivity (GPP). Existing empirical and model-based satellite broadband spectra-based products have been shown to miss critical variation in GPP. Here, we evaluate the potential of high spectral resolution (10 nm) shortwave (400-2,500 nm) imagery to better detect spatial and temporal variations in GPP across a range of ecosystems, including forests, grassland-savannas, wetlands, and shrublands in a water-stressed region. Estimates of GPP from eddy covariance observations were compared against airborne hyperspectral imagery, collected across California during the 2013-2014 HyspIRI airborne preparatory campaign. Observations from 19 flux towers across 23 flight campaigns (102 total image-flux tower pairs) showed GPP to be strongly correlated to a suite of spectral wavelengths and band ratios associated with foliar physiology and chemistry. A partial least squares regression (PLSR) modeling approach was then used to predict GPP with higher validation accuracy (adjusted R2  = 0.71) and low bias (0.04) compared to existing broadband approaches (e.g., adjusted R2  = 0.68 and bias = -5.71 with the Sims et al. model). Significant wavelengths contributing to the PLSR include those previously shown to coincide with Rubisco (wavelengths 1,680, 1,740, and 2,290 nm) and Vcmax (wavelengths 1,680, 1,722, 1,732, 1,760, and 2,300 nm). These results provide strong evidence that advances in satellite spectral resolution offer significant promise for improved satellite-based monitoring of GPP variability across a diverse range of terrestrial ecosystems.

Variation in the urban vegetation, surface temperature, air temperature nexus.

Our study examines the urban vegetation - air temperature (Ta) - land surface temperature (LST) nexus at micro- and regional-scales to better understand urban climate dynamics and the uncertainty in using satellite-based LST for characterizing Ta. While vegetated cooling has been repeatedly linked to reductions in urban LST, the effects of vegetation on Ta, the quantity often used to characterize urban heat islands and global warming, and on the interactions between LST and Ta are less well characterized. To address this need we quantified summer temporal and spatial variation in Ta through a network of 300 air temperature sensors in three sub-regions of greater Los Angeles, CA, which spans a coastal to desert climate gradient. Additional sensors were placed within the inland sub-region at two heights (0.1m and 2m) within three groundcover types: bare soil, irrigated grass, and underneath citrus canopy. For the entire study region, we acquired new imagery data, which allowed calculation of the normalized difference vegetation index (NDVI) and LST. At the microscale, daytime Ta measured along a vertical gradient, ranged from 6 to 3°C cooler at 0.1 and 2m, underneath tall canopy compared to bare ground respectively. At the regional scale NDVI and LST were negatively correlated (p<0.001). Relationships between diel variation in Ta and daytime LST at the regional scale were progressively weaker moving away from the coast and were generally limited to evening and nighttime hours. Relationships between NDVI and Ta were stronger during nighttime hours, yet effectiveness of mid-day vegetated cooling increased substantially at the most arid region. The effectiveness of vegetated Ta cooling increased during heat waves throughout the region. Our findings suggest an important but complex role of vegetation on LST and Ta and that vegetation may provide a negative feedback to urban climate warming.

Towards an Improved LAI Collection Protocol via Simulated and Field-Based PAR Sensing.

In support of NASA's next-generation spectrometer-the Hyperspectral Infrared Imager (HyspIRI)-we are working towards assessing sub-pixel vegetation structure from imaging spectroscopy data. Of particular interest is Leaf Area Index (LAI), which is an informative, yet notoriously challenging parameter to efficiently measure in situ. While photosynthetically-active radiation (PAR) sensors have been validated for measuring crop LAI, there is limited literature on the efficacy of PAR-based LAI measurement in the forest environment. This study (i) validates PAR-based LAI measurement in forest environments, and (ii) proposes a suitable collection protocol, which balances efficiency with measurement variation, e.g., due to sun flecks and various-sized canopy gaps. A synthetic PAR sensor model was developed in the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model and used to validate LAI measurement based on first-principles and explicitly-known leaf geometry. Simulated collection parameters were adjusted to empirically identify optimal collection protocols. These collection protocols were then validated in the field by correlating PAR-based LAI measurement to the normalized difference vegetation index (NDVI) extracted from the "classic" Airborne Visible Infrared Imaging Spectrometer (AVIRIS-C) data ( R 2 was 0.61). The results indicate that our proposed collecting protocol is suitable for measuring the LAI of sparse forest (LAI < 3-5 ( m 2 / m 2 )).

Increases in the climate change adaption effectiveness and availability of vegetation across a coastal to desert climate gradient in metropolitan Los Angeles, CA, USA.

Urbanization has increased heat in the urban environment, with many consequences for human health and well-being. Managing climate change in part through increasing vegetation is desired by many cities to mitigate current and future heat related issues. However, little information is available on what influences the current effectiveness and availability of vegetation for local cooling. In this study, we identified the variation in the interacting relationships among vegetation (normalized difference vegetation index), socioeconomic status (neighborhood income), elevation and land surface temperature (LST) to identify how vegetation based surface cooling services change throughout the pronounced coastal to desert climate gradient of the Los Angeles, CA metropolitan region, a megacity of >18 million residents. A key challenge for understanding variation in vegetation as a climate change adaptation tool spanning neighborhood to megacity scales is developing new "big data" analytical tools. We used structural equation modeling (SEM) to quantify the interacting relationships among socio-economic status data obtained from government census data, elevation and new LST and vegetation data obtained from an airborne imaging campaign conducted in 2013 for the urban and suburban areas across a series of fifteen climate zones. Vegetation systematically increased in cooling effectiveness from 6.06 to 31.77 degrees with increasing distance from the coast. Vegetation and neighborhood income were positively correlated throughout all climate zones with a peak in the relationship occurring near 25km from the coast. Because of the interaction between these two relationships, we also found that higher income neighborhoods were cooler and that this effect peaked at about 30km from the coast. These results show the availability and effectiveness of vegetation on the local climate varies tremendously throughout the Los Angeles, CA metropolitan area. Further, using the more inland climate zones as future analogs for more coastal zones, suggests that in the warmer climate conditions projected for the region the effectiveness of vegetation for regional cooling may increase thus acting as a localized negative feedback mechanism.