Satellite-based vegetation datasets enable phenology detection at large scales, among which Solar-Induced Chlorophyll Fluorescence (SIF) and Enhanced Vegetation Index (EVI) are widely used proxies for detecting from photosynthesis greenness perspectives, respectively. Recent studies have revealed the divergent performances of SIF EVI estimating different metrics, i.e., start season (SOS) end (E...

Vegetation indices have been used extensively to estimate the vegetation density from satellite and airborne images for many years. In this paper we first focus on one of the most popular vegetation indices, the normalized difference vegetation index (NDVI ) [1] and introduce a statistical framework to analyze it. We propose a solution to the saturation problem of this index based on our statis...

During the past several decades, observational data have shown a faster increase in hot temperature extremes than the change in mean temperature. Increasingly high extreme temperatures are expected to affect terrestrial ecosystem function. The ecological impact of hot extremes on vegetation production, however, remains uncertain across biomes in natural climatic conditions. In this study, we in...

Vegetation indices play an important role in monitoring variations in vegetation.The Enhanced Vegetation Index (EVI) proposed by the MODIS Land Discipline Groupand the Normalized Difference Vegetation Index (NDVI) are both global-based vegetationindices aimed at providing consistent spatial and temporal information regarding globalvegetation. However, many environmental factors such as atmosphe...

This study evaluates the performances of three global satellite datasets (Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite pour l’ observation de la Terre (SPOT) of the Mongolian Plateau, where in situ observation is insufficient to assess vegetation dynamics on terrestrial systems. We give a comprehensive assessment of the hi...

Effective conservation and management of natural resources requires up-to-date information on the land cover (LC) types and their dynamics. Multi-resolution remote sensing (RS) data with appropriate classification strategies have been used to categorise land use and land cover (LULC) of a landscape. RS data coupled with other important environmental layers (both remotely acquired or derived fro...

Because of all-weather working ability, sensitivity to biomass and moisture, and high spatial resolution, Synthetic aperture radar (SAR) satellite images can perfectly complement optical images for pasture monitoring. This paper aims to examine the potential of the integration of COnstellation of small Satellites for the Mediterranean basin Observasion (COSMO-SkyMed), Environmental Satellite Ad...

Assessing biomass dynamics is highly critical for monitoring ecosystem balance and its response to climate change and anthropogenic activities. In this study, we introduced a direct link between Landsat vegetation spectral indices and ground/airborne LiDAR data; this integration was established to estimate the biomass dynamics over various years using multi-temporal Landsat satellite images. Ou...

Algorithms that use remotely-sensed vegetation indices to estimate gross primary production (GPP), a key component of the global carbon cycle, have gained a lot of popularity in the past decade. Yet despite the amount of research on the topic, the most appropriate approach is still under debate. As an attempt to address this question, we compared the performance of different vegetation indices ...