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

REMOTE SENSING OF ENVIRONMENT

卷:233

Article

Smith, William K.¹  Dannenberg, Matthew P.^1,2  Yan, Dong¹  Herrmann, Stefanie³  Barnes, Mallory L.⁴  Barron-Gafford, Greg A.⁵  Biederman, Joel A.⁶  Ferrenberg, Scott⁷  Fox, Andrew M.^1,8  Hudson, Amy¹  Knowles, John F.^5,6  MacBean, Natasha⁹  Moore, David J. P.¹  Nagler, Pamela L.¹⁰  Reed, Sasha C.¹¹  Rutherford, William A.¹  Scott, Russell L.⁶  Wang, Xian¹  Yang, Julia⁵ 

[1] Univ Arizona, Sch Nat Resources & Environm, Tucson, AZ 85719 USA

[2] Univ Iowa, Dept Geog & Sustainabil Sci, Iowa City, IA 52242 USA

[3] Univ Arizona, Biosyst Engn, Tucson, AZ 85719 USA

[4] Indiana Univ, ONeill Sch Publ & Environm Affairs, Bloomington, IN 47405 USA

[5] Univ Arizona, Sch Geog & Dev, Tucson, AZ 85719 USA

[6] ARS, USDA, Tucson, AZ 85719 USA

[7] New Mexico State Univ, Dept Biol, Las Cruces, NM 88003 USA

[8] Joint Ctr Satellite Data Assimilat, Boulder, CO 80301 USA

[9] Indiana Univ, Dept Geog, Bloomington, IN 47405 USA

[10] US Geol Survey, Southwest Biol Sci Ctr, Tucson, AZ 85719 USA

[11] US Geol Survey, Southwest Biol Sci Ctr, Moab, UT 84532 USA

关键词: Drylands;    Vegetation indices;    Leaf area index;    Biocrusts;    Phenology;    Primary production;    Evapotranspiration;    LiDAR;    Hyperspectral;    Fluorescence;    Thermal infrared;   

DOI  :  10.1016/j.rse.2019.111401

来源: Elsevier

【 摘 要 】

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. More, 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 identify strategies 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) utilizing near-continuous observations from new-and-improved geostationary satellites to capture the rapid responses of dryland ecosystems to diurnal variation in water stress; 3) expanding ground observational networks to better represent the heterogeneity of dryland systems and enable robust calibration and evaluation; 4) developing algorithms that are specifically tuned to dryland ecosystems by utilizing expanded ground observational network data; and 5) 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.

【 授权许可】

Free