Remote sensing of dryland ecosystem structure and function: Progress, challenges and opportunities

Authors: SMITH, W.K. - University Of Arizona; DANNENBERG, M.O. - University Of Iowa; YAN, D. - University Of Arizona; HERMANN, S. - University Of Arizona; BARNES, M.L. - Indiana University; BARRON-GAFFORD, G.A. - University Of Arizona; Biederman, Joel; FERRENBERG, S. - New Mexico State University; FOX, A. - University Of Arizona; HUDSON, A. - University Of Arizona; Knowles, John; MACBEAN, N. - Indiana University; MOORE, D.J.P. - University Of Arizona; NAGLER, P.A. - Us Geological Survey (USGS); REED, S.C. - Us Geological Survey (USGS); RUTHERFORD, W.A. - University Of Arizona; Scott, Russell - Russ; WNG, X. - University Of Arizona; YANG, J. - University Of Arizona

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/25/2019
Publication Date: 11/1/2019
Publication URL: https://handle.nal.usda.gov/10113/6628282
Citation: Smith, W., Dannenberg, M., Yan, D., Hermann, S., Barnes, M., Barron-Gafford, G., Biederman, J.A., Ferrenberg, S., Fox, A., Hudson, A., Knowles, J.F., Macbean, N., Moore, D., Nagler, P., Reed, S., Rutherford, W., Scott, R.L., Wng, X., Yang, J. 2019. Remote sensing of dryland ecosystem structure and function: Progress, challenges and opportunities. Remote Sensing of Environment. 233. https://doi.org/10.1016/j.rse.2019.111401.
DOI: https://doi.org/10.1016/j.rse.2019.111401

Interpretive Summary: Water-limited regions make up more than one-third of Earth’s surface and provide valuable plant resources for agriculture, grazing and habitat. Society’s ability to understand and predict the health of dryland agro-ecosystems over such vast areas relies in large part on remote sensing from satellites. However, most of the expertise on satellite sensors has been developed in wetter regions of the world, and there are critical challenges to our ability to use and understand remote sensing signals in drylands. These include difficulty viewing sparse vegetation, highly reflective soils, biologically active soils that can’t be viewed from space, high spatial variability, and irregular seasonality of growing seasons. In this paper, we review some of the technical challenges to sensing drylands from space and offer recommendations based on existing and new technologies.

Technical Abstract: Drylands make up roughly 40% of the Earth’s land surface, and billions of people depend on services provided by these critically important ecosystems. Despite their relatively sparse vegetation, dryland ecosystems are structurally and functionally diverse, and emerging evidence suggests that these ecosystems play a dominant role in the trend and variability of the terrestrial carbon sink. Moreover, drylands are highly sensitive to climate and are likely to have large, non-linear responses to hydroclimatic change. Monitoring the spatiotemporal dynamics of dryland ecosystem structure (e.g., leaf area index) and function (e.g., primary production and evapotranspiration) is therefore a high research priority. Yet, dryland remote sensing is defined by unique challenges not typically encountered in mesic or humid regions. Major challenges include low vegetation signal-to-noise ratios, high soil background reflectance, presence of photosynthetic soils (i.e., biological soil crusts), high spatial heterogeneity from plot to regional scales, and irregular growing seasons due to unpredictable seasonal rainfall and frequent periods of drought. Additionally, there is a relative paucity of continuous, long-term measurements in drylands, which impedes robust calibration and evaluation of remotely-sensed dryland data products. Due to these issues, remote sensing techniques developed in other ecosystems or for global application often result in inaccurate, poorly constrained estimates of dryland ecosystem structural and functional dynamics. Here, we review past achievements and current progress in remote sensing of dryland ecosystems, including a detailed discussion of the major challenges associated with remote sensing of key dryland structural and functional dynamics. We then offer recommendations aimed at leveraging new and emerging opportunities in remote sensing to overcome previous challenges and more accurately contextualize drylands within the broader Earth system. Specifically, we recommend: 1) Exploring novel combinations of sensors and techniques (e.g., solar-induced fluorescence, thermal, microwave, hyperspectral, and LiDAR) across a range of spatiotemporal scales to gain new insights into dryland structural and functional dynamics; 2) developing algorithms that are specifically tuned to dryland ecosystems by utilizing expanded ground observational network data; 3) expanding ground observational networks to better represent the heterogeneity of dryland systems and enable robust calibration and evaluation; and 4) coupling remote sensing observations with process-based models using data assimilation to improve mechanistic understanding of dryland ecosystem dynamics and to better constrain ecological forecasts and long-term projections.