Investigating the accuracy of vegetation index-based models for estimating the fractional vegetation cover and the effects of varying soil backgrounds using <i>in situ</i> measurements and the PROSAIL model

Ding, Yanling; Zhang, Hongyan; Zhao, Kai; Zheng, Xunhua

doi:10.1080/01431161.2017.1312617

Cited by 24 publications

(4 citation statements)

References 40 publications

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“…either on a cloudy day or under an umbrella, with camera settings 24 mm focal length, 7000 K white balance; for more details see Cheng et al 2001, Tang et al 2003). We separated each picture into patches according to the substrate type and estimated canopy green cover with image analysis software (Image‐Pro Plus, Tang et al 2003, Ding et al 2017). Shoot biomass (after drought).…”

Section: Methodsmentioning

confidence: 99%

Three‐dimensional soil heterogeneity modulates responses of grassland mesocosms to an experimentally imposed drought extreme

Liu

Bortier

Nijs

et al. 2021

Oikos

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Heterogeneity is an intrinsic characteristic of soils, which regulates plant diversity and ecosystem functioning. However, whether soil heterogeneity also modulates responses of plant communities to climate change, including climate extremes, remains largely an open question. Here, we explore responses of plant communities to drought extremes across four levels of spatial soil heterogeneity, with cell sizes varying from very small to very large, i.e. 0, 12, 24 and 48 cm. These were created in mesocosms by alternating nutrient‐rich and nutrient‐poor substrate in three dimensions. A seed mixture of 24 grassland species was evenly sown on each mesocosm in spring. In late summer, a three‐week drought was imposed with a rainout shelter. During the drought, soil water content at the mesocosm scale decreased more at intermediate (12 and 24 cm) than at small or large (0 and 48 cm) cell sizes, which was reflected in increased senescence and drought‐induced heat stress. These responses could be traced to greater plant biomass coupled with higher water demand at those intermediate cell sizes, likely related to between‐cell access to nutrients and effects of diversity and community composition. Our results indicate that soil heterogeneity can modulate the impact of drought extremes on plant communities, though more research is needed on the transition between intermediate and extreme cell sizes, where heterogeneity effects seem to change most. We propose that soil heterogeneity be considered more explicitly in studies of changing precipitation regimes.

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Section: Methodsmentioning

confidence: 99%

Three‐dimensional soil heterogeneity modulates responses of grassland mesocosms to an experimentally imposed drought extreme

Liu

Bortier

Nijs

et al. 2021

Oikos

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“…It relied on spectra simulated on PROSAIL and spectra measurements delivered from UAV. Lastly, the studies of [198,199] evaluated the impact of soil background and leaf angle distribution (LAD) on fCover estimation by using a set of different vegetation indices. Table A2 summarizes the above papers for the fCover hybrid parametric method.…”

Section: Fractional Vegetation Covermentioning

confidence: 99%

“…Landsat 8 PROSAIL+ six methods of inversion rely on 4 VIs [198] The soil background has a great impact on fCover estimation. The matrix with two new VIs, RERI(705) and RERI(783), is the most effective.…”

Section: Cornmentioning

confidence: 99%

A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions

Abdelbaki

Udelhoven

2022

Remote Sensing

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Remote sensing technology allows to provide information about biochemical and biophysical crop traits and monitor their spatiotemporal dynamics of agriculture ecosystems. Among multiple retrieval techniques, hybrid approaches have been found to provide outstanding accuracy, for instance, for the inference of leaf area index (LAI), fractional vegetation cover (fCover), and leaf and canopy chlorophyll content (LCC and CCC). The combination of radiative transfer models (RTMs) and data-driven models creates an advantage in the use of hybrid methods. Through this review paper, we aim to provide state-of-the-art hybrid retrieval schemes and theoretical frameworks. To achieve this, we reviewed and systematically analyzed publications over the past 22 years. We identified two hybrid-based parametric and hybrid-based nonparametric regression models and evaluated their performance for each variable of interest. From the results of our extensive literature survey, most research directions are now moving towards combining RTM and machine learning (ML) methods in a symbiotic manner. In particular, the development of ML will open up new ways to integrate innovative approaches such as integrating shallow or deep neural networks with RTM using remote sensing data to reduce errors in crop trait estimations and improve control of crop growth conditions in very large areas serving precision agriculture applications.

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“…Therefore, in this study, we set up a time series of the modified soil adjusted vegetation index (MSAVI) [71]. This spectral vegetation index was tailor-made in order to cope with soil noise, and it has proven its usefulness and importance in previous studies on drylands [78][79][80][81][82].…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis of Land Cover Change in Dry Mountains: Insights from the Tajik Pamirs

2021

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Greening and browning trends in vegetation have been observed in many regions of the world in recent decades. However, few studies focused on dry mountains. Here, we analyze trends of land cover change in the Western Pamirs, Tajikistan. We aim to gain a deeper understanding of these changes and thus improve remote sensing studies in dry mountainous areas. The study area is characterized by a complex set of attributes, making it a prime example for this purpose. We used generalized additive mixed models for the trend estimation of a 32-year Landsat time series (1988–2020) of the modified soil adjusted vegetation index, vegetation data, and environmental and socio-demographic data. With this approach, we were able to cope with the typical challenges that occur in the remote sensing analysis of dry and mountainous areas, including background noise and irregular data. We found that greening and browning trends coexist and that they vary according to the land cover class, topography, and geographical distribution. Greening was detected predominantly in agricultural and forestry areas, indicating direct anthropogenic drivers of change. At other sites, greening corresponds well with increasing temperature. Browning was frequently linked to disastrous events, which are promoted by increasing temperatures.

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Investigating the accuracy of vegetation index-based models for estimating the fractional vegetation cover and the effects of varying soil backgrounds using in situ measurements and the PROSAIL model

Cited by 24 publications

References 40 publications

Three‐dimensional soil heterogeneity modulates responses of grassland mesocosms to an experimentally imposed drought extreme

Three‐dimensional soil heterogeneity modulates responses of grassland mesocosms to an experimentally imposed drought extreme

A Review of Hybrid Approaches for Quantitative Assessment of Crop Traits Using Optical Remote Sensing: Research Trends and Future Directions

Time Series Analysis of Land Cover Change in Dry Mountains: Insights from the Tajik Pamirs

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