Nitrogen (N) has been linked to different ecosystem processes, and retrieving this important foliar biochemical constituent from remote sensing observations is of widespread interest. Since N is not explicitly represented in physically based radiative transfer models, empirical methods have been used as an alternative. The spectral bands selected during the calibration of empirical methods have been interpreted in the context of light-N interactions and, consequently, ecosystem function. The low amount of leaves on shrubs and their sparse distribution in drylands create an environment, in which the canopy structure and the bright background soil play an important role in the canopy total radiation budget. In this paper, we examine the assumption that removing the impact of canopy structure and soil will result in improved N retrieval using the empirical methods. We report the inconsistencies in the selection of spectral bands among the empirical approaches. Moreover, these methods are sensitive to the scale of the study and band transformations. Using the generalized theory of canopy spectral invariants, we found that at the canopy scale, a combination of canopy structure and soil dominates the total canopy radiation. At the plot scale, soil

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Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community. Ecosphere 12(12):e03833.

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways towards mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.