A novel spectral index for estimating fractional cover of non-photosynthetic vegetation using near-infrared bands of Sentinel satellite

Tian, Jia; Su, Shanshan; Tian, Qingjiu; Zhan, Wenfeng; Xi, Yanbiao; Wang, Ning

doi:10.1016/j.jag.2021.102361

Cited by 14 publications

(10 citation statements)

References 46 publications

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“…However, similar to Guerschman et al (2009), we found that NPV typically has a considerably lower reflectance than soil in the SWIR band centered on 2200 nm, increasing the spectral separability of NPV and soil for our study area. Second, results by Ji et al (2020) and Tian et al (2021) suggest, that the three red-edge bands and the relatively narrow NIR band of Sentinel-2 provide additional information for separating NPV from soil. NPV often exhibits a stronger reflectance increase in the red-edge wavelength region towards the NIR than soils.…”

Section: Pv Npv and Soil Fractional Cover Time Series From Sentinel-2mentioning

confidence: 99%

Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series

Kowalski

Okujeni

Brell

et al. 2022

Remote Sensing of Environment

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Severe droughts caused unprecedented impacts on grasslands in Central Europe in 2018 and 2019. Yet, spatially varying drought impacts on grasslands remain poorly understood as they are driven by complex interactions of environmental conditions and land management. Sentinel-2 time series offer untapped potential for improving grassland monitoring during droughts with the required spatial and temporal detail. In this study, we quantified drought effects in a major Central European grassland region from 2017 to 2020 using a regression-based unmixing framework. The Sentinel-2-based intra-annual time series of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and soil fractional cover provide easily interpretable quantities relevant for understanding drought effects on grasslands. Fractional cover estimates from Sentinel-2 matched in-situ conditions observed during field visits. The comparison to a multitemporal reference dataset showed the best agreement for PV cover (MAE = 7.2%). Agreement was lower for soil and NPV, but we observed positive relationships between fractional cover from Sentinel-2 and the reference data with MAE = 10.1% and MAE = 15.4% for soil and NPV, respectively. Based on the fractional cover estimates, we derived a Normalized Difference Fraction Index (NDFI) time series contrasting NPV and soil cover relative to PV. In line with meteorological and soil moisture drought indices, and with the Normalized Difference Vegetation Index (NDVI), NDFI time series showed the most severe drought impacts in 2018, followed by less severe, but persisting effects in 2019. Drought-specific metrics from NDFI time series revealed a high spatial variability of onset, duration, impact, and end of drought effects on grasslands. Evaluating drought metrics on different soil types, we found that grasslands on less productive, sandy Cambisols were strongly affected by the drought in 2018 and 2019. In comparison, grasslands on Gleysols and Histosols were less severely impacted suggesting a higher drought resistance of these grasslands. Our study emphasizes that the high temporal and spatial detail of Sentinel-2 time series is mandatory for capturing relevant vegetation dynamics in Central European lowland grasslands under drought.

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Section: Pv Npv and Soil Fractional Cover Time Series From Sentinel-2mentioning

confidence: 99%

Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series

Kowalski

Okujeni

Brell

et al. 2022

Remote Sensing of Environment

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“…Since Sentinel data have the advantages of a short revisit period and high spatial resolution, surface vegetation monitoring based on Sentinel data has become a popular research topic. Many researchers have been interested in it and have developed indices adapted to it [12] proposed an NSSI vegetation index adapted to Sentinel data [11] proposed the NDVI705 vegetation index based on the red band of the Sentinel data and used it to study vegetation recovery after the Cyprus fire, [13] used Sentinel data to compute a variety of vegetation indices and used them to invert the surface salinity)…”

Section: Basic Literature Overviewmentioning

confidence: 99%

Comparison of EO4Agri Recommendations With In-Depth Literature Review

Zadražil¹,

Onckelet²,

Kubickova³

2021

Preprint

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Copernicus is Europe's space-based Earth monitoring asset, which consists of a complex set of systems that collect data from different sources: remote sensing satellites (RS) and in-situ sensors such as ground stations, airborne and marine sensors. This study was originally prepared for the needs of the Czech agricultural community, where we provided an in-depth analysis of articles related to Earth observation in precision agriculture. At a later stage, we extended this study by comparing the recommendations of the European EO4Agri project and scientific articles published in MDPI. We had two important objectives, one was to validate the results of the EO4Agri project and the other was to look for gaps in current research and community needs. To recognize the importance of using Sentinel 1 data, we also added a specific analysis of methods for data fusion of Sentinel 1 and Sentinel 2 data.

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“…Analyzing annual vegetation dynamics provides key information for determining the overall trends in natural vegetation and agricultural areas that may be subjected to abnormal drought, poor cropland management practices, or abandonment [41,42]. In arid regions, vegetation can be categorized either as photosynthetic (i.e., green leaves, healthy growing crops) or non-photosynthetic (dead/decaying/brown vegetation) [42][43][44][45][46][47][48][49]. Fractional cover algorithms use spectral unmixing [43,[46][47][48] to determine the proportion of photosynthetic vegetation (pv), non-photosynthetic vegetation (npv), and bare soil (bs) (i.e., bare soil or rock) contained within a single pixel of imagery [44,46,49].…”

Section: Introductionmentioning

confidence: 99%

“…In arid regions, vegetation can be categorized either as photosynthetic (i.e., green leaves, healthy growing crops) or non-photosynthetic (dead/decaying/brown vegetation) [42][43][44][45][46][47][48][49]. Fractional cover algorithms use spectral unmixing [43,[46][47][48] to determine the proportion of photosynthetic vegetation (pv), non-photosynthetic vegetation (npv), and bare soil (bs) (i.e., bare soil or rock) contained within a single pixel of imagery [44,46,49]. Comparing the ratios over months in a particular year and over multiple years indicates trends in a nation's land use management [47] and, more broadly, land cover.…”

Section: Introductionmentioning

confidence: 99%

Examining Spatiotemporal Photosynthetic Vegetation Trends in Djibouti Using Fractional Cover Metrics in the Digital Earth Africa Open Data Cube

Wardle,

Phillips

2024

Remote Sensing

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The Horn of Africa has sensitive, arid ecosystems, with its vegetation commonly distressed by factors such as climate change, population increase, unstable water resources, and rarely enforced land use management practices. These factors make countries such as Djibouti highly variable locations for the growth of vegetation and agricultural products, and these countries are becoming more vulnerable to food insecurity as the climate warms. The rapid growth of satellite and digital image processing technology over the last five decades has improved our ability to track long-term agricultural and vegetation changes. Data cubes are a newer approach to managing satellite imagery and studying temporal patterns. Here, we use the cloud-based Digital Earth Africa, Open Data Cube to analyze 30 years of Landsat imagery and orthomosaics. We analyze long-term trends in vegetation dynamics by comparing annual fractional cover metrics (photosynthetic vegetation, non-photosynthetic vegetation, and bare ground) to the Normalized Difference Vegetation Index. Investigating Djibouti-wide and regional vegetation trends, we provide a comparison of trends between districts and highlight a primary agricultural region in the southeast as a detailed example of vegetation change. The results of the Sen’s slope and Mann–Kendall regression analyses of the data cube suggest a significant decline in vegetation (p = 0.00002), equating to a loss of ~0.09 km2 of arable land per year (roughly 2.7 km2 over the 30-year period). Overall, decreases in photosynthetic vegetation and increases in both non-photosynthetic vegetation and bare soil areas indicate that the region is becoming more arid and that land cover is responding to this trend.

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A novel spectral index for estimating fractional cover of non-photosynthetic vegetation using near-infrared bands of Sentinel satellite

Cited by 14 publications

References 46 publications

Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series

Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series

Comparison of EO4Agri Recommendations With In-Depth Literature Review

Examining Spatiotemporal Photosynthetic Vegetation Trends in Djibouti Using Fractional Cover Metrics in the Digital Earth Africa Open Data Cube

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