Evaluation of the SPOT/VEGETATION Collection 3 reprocessed dataset: Surface reflectances and NDVI

Toté, C.; Swinnen, Else; Sterckx, Sindy; Clarijs, Dennis; Quang, Carine; Maes, Ronny

doi:10.1016/j.rse.2017.09.010

Cited by 38 publications

(22 citation statements)

References 44 publications

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“…Thus, monitoring and attributing such spatiotemporal changes in vegetation growth is not only critical to understanding the role that this land cover type plays in the Earth's climatic system [6], but is also a requirement for developing more sustainable strategies and policies for ecosystem management [7,8]. Long-term records of vegetation indices derived from satellite remote sensing provide unparalleled information regarding the vegetation response to climatic and anthropogenic factors at regional-to-global scales over the last two decades, such as the Normalized Difference Vegetation Index (NDVI) derived from Generation of Global Inventory Modeling and Mapping Studies 3rd Version (GIMMS 3g ), Système Pour l'Observation de la Terre (SPOT), and MODerate resolution Imaging Spectroradiometer (MODIS) [9][10][11]. A range of evaluation studies have, however, noted the fact that these remote sensing products did not yield consistent patterns of vegetation change [12][13][14][15], likely due to the impacts of sensor shifts or degradation [14,16,17].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Patterns of Vegetation Greenness Change and Associated Climatic and Anthropogenic Drivers on the Tibetan Plateau during 2000–2015

Zhang

Liu

et al. 2018

Remote Sensing

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Alpine vegetation on the Tibetan Plateau (TP) is known to be sensitive to both climate change and anthropogenic disturbance. However, the magnitude and patterns of alpine vegetation dynamics and the driving mechanisms behind their variation on the TP remains under debate. In this study, we used updated MODIS Collection 6 Normalized Difference Vegetation Index (NDVI) from the Terra satellite combined with linear regression and the Break for Additive Season and Trend model to reanalyze the spatiotemporal patterns of vegetation change on the TP during 2000–2015. We then quantified the responses of vegetation variation to climatic and anthropogenic factors by coupling climatic and human footprint datasets. Results show that growing season NDVI (GNDVI) values increased significantly overall (0.0011 year−1, p < 0.01) during 2000–2015 and that 70.37% of vegetated area on the TP (23.47% significantly with p < 0.05) exhibited greening trends with the exception of the southwest TP. However, vegetation greenness experienced trend shifts from greening to browning in half of the ecosystem zones occurred around 2010, likely induced by spatially heterogeneous temporal trends of climate variables. The vegetation changes in the northeastern and southwestern TP were water limited, the mid-eastern TP exhibited strong temperature responses, and the south of TP was driven by a combination of temperature and solar radiation. Furthermore, we found that, to some extent, anthropogenic disturbances offset climate-driven vegetation greening and aggravated vegetation browning induced by water deficit. These findings suggest that the impact of anthropogenic activities on vegetation change might not overwhelm that of climate change at the region scale.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Patterns of Vegetation Greenness Change and Associated Climatic and Anthropogenic Drivers on the Tibetan Plateau during 2000–2015

Zhang

Liu

et al. 2018

Remote Sensing

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“…42 This BRDFadjustment approach would be a more desirable approach than the view angle constraint approach, as the former not only allows for the normalization of view zenith angle, but also would allow for the normalization of the VIIRS versus MODIS orbital cycle difference. Toté et al 43 also recommended the BRDF-adjustment approach to possibly adjust cross-sensor NDVI differences between Satellite Pour l'Observation de la Terre (SPOT) VEGETATION-1 and VEGETATION-2 caused by their platform orbital drift. This approach has been adopted to the Landsat-8 and Sentinel-2 Harmonization project, where Landsat-8 and Sentinel-2 surface reflectance data are normalized to nadir viewing and fixed illumination before the inter-sensor spectral bandpass adjustment, given the differing sun and view zenith angles associated with these sensor data.…”

Section: Discussionmentioning

confidence: 99%

Assessment of cross-sensor vegetation index compatibility between VIIRS and MODIS using near-coincident observations

Miura

Muratsuchi

Vargas

2018

J. Appl. Rem. Sens.

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Cross-sensor compatibility of spectral vegetation indices (VIs) between Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) was investigated using their near-coincident observation pairs obtained along overlapped orbital tracks across the globe for the year 2015. The "top-of-atmosphere (TOA)" and "top-of-canopy (TOC)" normalized difference vegetation indices (NDVIs), TOC-enhanced vegetation index (EVI), and TOC two-band EVI (EVI2) were investigated. For all four VIs, VIIRS and MODIS VIs were subject to systematic differences in which VIIRS VIs were higher than their MODIS counterparts. The overall systematic differences and uncertainties (measured as mean differences and root mean square differences, respectively) were small (0.010 to 0.020 VI units and 0.015 to 0.022 VI units, respectively). TOA NDVI cross-sensor differences were neither seasonally nor view zenith angle dependent, whereas TOC NDVI cross-sensor differences slightly varied seasonally, but were not view zenith angle dependent. TOC EVI and TOC EVI2 cross-sensor differences were view zenith angle dependent, where systematic differences increased with increasing view zenith angle and, for large view zenith angles, they were higher during the summer seasons. These results support the normalization of view zenith angles as a required step to extend the MODIS VI record with VIIRS data. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…The soil water index (SWI) adds complexity to the analysis by quantifying the moisture condition at various soil depths. Finally, the maps of burned areas delineate the zones of the globe that have been affected by fire events [34,[46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65]. The Global Climate Observing System (GCOS) recognizes the maps of burned areas, FAPAR, LAI, and SWI as essential climate variables (ECVs) [40].…”

Section: Cgls Vegetation and Energy Productsmentioning

confidence: 99%

Monitoring Green Infrastructure for Natural Water Retention Using Copernicus Global Land Products

et al. 2019

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Nature-based solutions are increasingly relevant tools for spatial and environmental planning, climate change adaptation (CCA), and disaster risk reduction (DRR). For this reason, a wide range of institutions, governments, and financial bodies are currently promoting the use of green infrastructure (GI) as an alternative or a complement to traditional grey infrastructure. A considerable amount of research already certifies the benefits and multi-functionality of GI: natural water retention measures (NWRMs), as GIs related specifically to the water sector are also known, are, for instance, a key instrument for the prevention and mitigation of extreme phenomena, such as floods and droughts. However, there are persisting difficulties in locating and identifying GI and one of the most promising solutions to this issue, the use of satellite-based data products, is hampered by a lack of well-grounded knowledge, experiences, and tools. To bridge this gap, we performed a review of the Copernicus Global Land Service (CGLS) products, which consist of freely-available bio-geophysical indices covering the globe at mid-to-low spatial resolutions. Specifically, we focused on vegetation and energy indices, examining previous research works that made use of them and evaluating their current quality, aiming to define their potential for studying GI and especially NWRMs related to agriculture, forest, and hydro-morphology. NWRM benefits are also considered in the analysis, namely: (i) NWRM biophysical impacts (BPs), (ii) ecosystem services delivered by NWRMs (ESs), and (iii) policy objectives (POs) expressed by European Directives that NWRMs can help to achieve. The results of this study are meant to assist GI users in employing CGLS products and ease their decision-making process. Based on previous research experiences and the quality of the currently available versions, this analysis provides useful tools to identify which indices can be used to study several types of NWRMs, assess their benefits, and prioritize the most suitable ones.

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Evaluation of the SPOT/VEGETATION Collection 3 reprocessed dataset: Surface reflectances and NDVI

Cited by 38 publications

References 44 publications

Spatiotemporal Patterns of Vegetation Greenness Change and Associated Climatic and Anthropogenic Drivers on the Tibetan Plateau during 2000–2015

Spatiotemporal Patterns of Vegetation Greenness Change and Associated Climatic and Anthropogenic Drivers on the Tibetan Plateau during 2000–2015

Assessment of cross-sensor vegetation index compatibility between VIIRS and MODIS using near-coincident observations

Monitoring Green Infrastructure for Natural Water Retention Using Copernicus Global Land Products

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