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

Smith, William K.; Dannenberg, Matthew P.; Yan, Dong; Herrmann, Stephanie; Barnes, Mallory L.; Barron‐Gafford, Greg A.; Biederman, Joel A.; Ferrenberg, Scott; Fox, A. M.; Hudson, A.; Knowles, John F.; MacBean, Natasha; Moore, D. J.; Nagler, Pamela L.; Reed, Sasha C.; Rutherford, William A.; Scott, Russell L.; Wang, Xianfeng; Yang, Julia C.

doi:10.1016/j.rse.2019.111401

Cited by 216 publications

(172 citation statements)

References 279 publications

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“…Spatio-temporal climate relationships were consistent between the HRS and BIT datasets, except for litter ( Figure 5). This finding underscores the ability of the BIT dataset to accurately represent site conditions as observed by high-resolution imagery and the tight coupling of vegetation growth and soil moisture in drylands [16]. We found component relationships with WYPRCP similar in direction, but weaker in strength relative to previous BIT analysis in the northwest Great Basin by Shi et al [4].…”

Section: Discussionsupporting

confidence: 66%

“…By extension, validation of fractional component time-series pose the most difficult scenario, particularly in areas of subtle change. This is especially true in dryland ecosystems with frequently sparse vegetation canopies that increase the influence of soils and senesced vegetation and where only a scarce ground-based data network exists [16]. Major challenges to time-series validation include (1) validation datasets that are not directly comparable to the remotely sensed data, (2) sample size, spatial extent, or temporal extent of validation datasets, which are limited, (3) validation datasets that are not independent, and (4) appropriate data do not exist as field sampling is highly resource intensive [17,18].…”

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confidence: 99%

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Validating a Landsat Time-Series of Fractional Component Cover Across Western U.S. Rangelands

et al. 2019

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Western U.S. rangelands have been quantified as six fractional cover (0%-100%) components over the Landsat archive at a 30 m resolution, termed the "Back-in-Time" (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. Here, we used field data collected concurrently with high-resolution satellite (HRS) images over multiple locations (n = 42) and years. Field observations were used to train regression tree models, predicting the component cover across each HRS image. Our objectives were to evaluate the spatial and temporal relationships between HRS and BIT component cover and compare spatio-temporal climate responses. First, for each HRS site-year (n = 77) we averaged both the HRS and BIT predictions within each site separately and regressed the averages to quantify the temporal accuracy. Next, we regressed individual pixel values of corresponding HRS and BIT predictions to quantify the spatio-temporal accuracy. Results showed strong temporal correlations with an average R 2 of 0.63 and Root Mean Square Error (RMSE) of 5.47% as well as strong spatio-temporal correlations with an average R 2 of 0.52 and RMSE of 7.89% across components. Our approach increased the validation sample size relative to direct comparison of field observations. Validation results showed robust spatio-temporal relationships between HRS and BIT data, providing increased user confidence in the data. disturbance in the Northern Great Basin [4,5]. While most change in fractional component cover was small (<10% cover change over the study period), a large majority of pixels indicated at least some change [5]. The BIT suite was designed to capture spatially discrete abrupt change and pervasive gradual change [5] that is often ignored by the remote sensing community [6].Validation of any remotely sensed mapping application is critical to increasing user confidence in products, foster usage in management decisions, and determine the more robust estimate in cases of competing datasets [7,8]. However, validation is often challenging [3,6,[9][10][11], especially with time-series maps [12]. Validation is often somewhat subjective, relying on manual image interpretation on reference blocks [13], Google Earth time-series imagery [14,15], or case studies. Most validation methods are designed for thematic classes (e.g., [10,15]) that typically employ confusion matrices to understand accuracies [14]. An introduction of more mapping classes often results in weaker validation results [15]. By extension, validation of fractional component time-series pose the most difficult scenario, particularly in areas of subtle change. This is especially true in dryland ecosystems with frequently sparse vegetation canopies that increase the influence of soils and senesced vegetation and where only a scarce ground-based data network exists [16]. Major challenges to time-series validation include (1) validation datasets that are not directly comparable to the remotely sensed data, (2) sample size, spatial extent, or temporal extent of...

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Section: Discussionsupporting

confidence: 66%

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confidence: 99%

Validating a Landsat Time-Series of Fractional Component Cover Across Western U.S. Rangelands

et al. 2019

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“…Therefore, mostly they are in water-limited areas which have enough energy from the sun. Dissecting the whole globe in just two classes might not be sufficient to explore their characteristics [41]. Therefore, based on [29], we classified the world based on their PP/PET into five classes (Figure 7a,b) to compare the results with two classes analysis.…”

Section: Changes In Aet Pet and Pp For Water And Energy-limited Areasmentioning

confidence: 99%

Global Trends in Evapotranspiration Dominated by Increases across Large Cropland Regions

et al. 2020

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Irrigated croplands require large annual water inputs and are critical to global food production. Actual evapotranspiration (AET) is a main index of water use in croplands, and several remote-sensing products have been developed to quantify AET at the global scale. In this study, we estimate global trends in actual AET, potential ET (PET), and precipitation rate (PP) utilizing the MODIS Evapotranspiration product (2001–2018) within the Google Earth Engine cloud-computing environment. We then introduce a new index based on a combination of AET, PET, and PP estimates—the evapotranspiration warning index (ETWI)—which we use to evaluate the sustainability of observed AET trends. We show that while AET has not considerably changed across global natural lands, it has significantly increased across global croplands (+14% ± 5%). The average ETWI for global croplands is −0.40 ± 0.25, which is largely driven by an extreme trend in AET, exceeding both PET and PP trends. Furthermore, the trends in water and energy limited areas demonstrate, on a global scale, while AET and PET do not have significant trends in both water and energy limited areas, the increasing trend of PP in energy-limited areas is more than water-limited areas. Averaging cropland ETWI trends at the country level further revealed nonsustainable trends in cropland water consumptions in Thailand, Brazil, and China. These regions were also found to experiencing some of the largest increases in net primary production (NPP) and solar-induced fluorescence (SIF), suggesting that recent increases in food production may be dependent on unsustainable water inputs. Globally, irrigated maize was found to be associated with nonsustainable AET trends relative to other crop types. We present an online open access application designed to enable near real-time monitoring and improve the understanding of global water consumption and availability.

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“…Campioli et al 2016; Teets et al 2017; Xu et al 2017), but weakens considerably in the context of drought – many times due to dynamic C allocation away from radial growth (Rocha et al 2006; Mund et al 2010). Additionally, the linkages between tree rings and remotely sensed vegetation indexes can be tenuous (Gazol et al 2018; Seftigen et al 2018), especially when canopy cover is sparse (Smith et al 2019; Tei et al 2019). One recent study at a temperate broadleaf forest recovering from a severe drought did provide evidence that preferential C allocation away from the stem towards the canopy could result in reduced tree growth despite a rapid recovery of GPP (Kannenberg et al 2019c).…”

Section: Introductionmentioning

confidence: 99%

Ghosts of the past: how drought legacy effects shape forest functioning and carbon cycling

2020

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Multi-year lags in tree drought recovery, termed 'drought legacy effects', are important for understanding the impacts of drought on forest ecosystems, including carbon (C) cycle feedbacks to climate change. Despite the ubiquity of lags in drought recovery, large uncertainties remain regarding the mechanistic basis of legacy effects and their importance for the C cycle. In this review, we identify the approaches used to study legacy effects, from tree rings to whole forests. We then discuss key knowledge gaps pertaining to the causes of legacy effects, and how the various mechanisms that may contribute these lags in drought recovery could have contrasting implications for the C cycle. Furthermore, we conduct a novel data synthesis and find that legacy effects differ drastically in both size and length across the US depending on if they are identified in tree rings versus gross primary productivity. Finally, we highlight promising approaches for future research to improve our capacity to model legacy effects and predict their impact on forest health. We emphasise that a holistic view of legacy effectsfrom tissues to whole forestswill advance our understanding of legacy effects and stimulate efforts to investigate drought recovery via experimental, observational and modelling approaches.

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

Cited by 216 publications

References 279 publications

Validating a Landsat Time-Series of Fractional Component Cover Across Western U.S. Rangelands

Validating a Landsat Time-Series of Fractional Component Cover Across Western U.S. Rangelands

Global Trends in Evapotranspiration Dominated by Increases across Large Cropland Regions

Ghosts of the past: how drought legacy effects shape forest functioning and carbon cycling

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