Satellite time series with high spatial resolution is critical for monitoring land surface dynamics in heterogeneous landscapes. Although remote sensing technologies have experienced rapid development in recent years, data acquired from a single satellite sensor are often unable to satisfy our demand. As a result, integrated use of data from different sensors has become increasingly popular in the past decade. Many spatiotemporal data fusion methods have been developed to produce synthesized images with both high spatial and temporal resolutions from two types of satellite images, frequent coarse-resolution images, and sparse fine-resolution images. These methods were designed based on different principles and strategies, and therefore show different strengths and limitations. This diversity brings difficulties for users to choose an appropriate method for their specific applications and data sets. To this end, this review paper investigates literature on current spatiotemporal data fusion methods, categorizes existing methods, discusses the principal laws underlying these methods, summarizes their potential applications, and proposes possible directions for future studies in this field.

Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant‐climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.

Numerous investigations of urbanization effects on vegetation spring phenology using satellite images have reached a consensus that vegetation spring phenology in urban areas occurs earlier than in surrounding rural areas. Nevertheless, the magnitude of this rural–urban difference is quite different among these studies, especially for studies over the same areas, which implies large uncertainties. One possible reason is that the satellite images used in these studies have different spatial resolutions from 30 m to 1 km. In this study, we investigated the impact of spatial resolution on the rural–urban difference of vegetation spring phenology using satellite images at different spatial resolutions. To be exact, we first generated a dense 10 m NDVI time series through harmonizing Sentinel-2 and Landsat-8 images by data fusion method, and then resampled the 10 m time series to coarser resolutions from 30 m to 8 km to simulate images at different resolutions. Afterwards, to quantify urbanization effects, vegetation spring phenology at each resolution was extracted by a widely used tool, TIMESAT. Last, we calculated the difference between rural and urban areas using an urban extent map derived from NPP VIIRS nighttime light data. Our results reveal: (1) vegetation spring phenology in urban areas happen earlier than rural areas no matter which spatial resolution from 10 m to 8 km is used, (2) the rural–urban difference in vegetation spring phenology is amplified with spatial resolution, i.e., coarse satellite images overestimate the urbanization effects on vegetation spring phenology, and (3) the underlying reason of this overestimation is that the majority of urban pixels in coarser images have higher diversity in terms of spring phenology dates, which leads to spring phenology detected from coarser NDVI time series earlier than the actual dates. This study indicates that spatial resolution is an important factor that affects the accuracy of the assessment of urbanization effects on vegetation spring phenology. For future studies, we suggest that satellite images with a fine spatial resolution are more appropriate to explore urbanization effects on vegetation spring phenology if vegetation species in urban areas is very diverse.

Changes in the interannual variability (IAV) of vegetation greenness and carbon sequestration are key indicators of the stability and climate sensitivities of terrestrial ecosystems. Recent studies have examined the changes in the vegetation IAV using atmospheric CO₂ observations and dynamic global vegetation models (DGVMs), however, reported different and even contradictory IAV trends. Here, we investigate the changes in the IAV of vegetation greenness, quantified as coefficient of variability (CV), over the past few decades based on multiple satellite remote sensing products and DGVMs. Our results suggested that, on half of the global vegetated surface (mostly in the tropics), the CV trends detected by different satellite remote sensing products are conflicting. We found that 22.20% and 28.20% of the global vegetated surface (mostly in the non‐tropical land surface) show significant positive and negative CV trends (p ≤ 0.1), respectively. Regions with higher air temperature and greater aridity tend to have increasing CV trends, whereas greater vegetation greening trend and higher nitrogen deposition lead to smaller CV trends. DGVMs generally cannot capture the CV trends obtained from satellite remote sensing products, while the inconsistency among satellite remote sensing products is likely caused by their process algorithms rather than the sensors utilized. Our study closely examines the changes in the IAV of global vegetation greenness, and highlights substantial uncertainty when using satellite remote sensing to study the response of terrestrial ecosystems to climate change.

El Niño/Southern Oscillation (ENSO) is the main climate mode that drives the interannual variability in climate and consequently vegetation greenness. While widespread green‐up has been reported and examined in tropical America during El Niño, it remains unclear how vegetation in tropical Asia changes during the period. Here, we used four remote sensing‐based leaf area index (LAI) products to investigate changes in vegetation greenness during the 2015/16 El Niño in tropical Asia. We found a strong green‐up during the 2015/16 El Niño in tropical Asia, with its regional average LAI stronger than that of tropical America. The drivers for the green‐up vary across the region, with radiation being the main driver for continental tropical Asia, and temperature and soil water anomalies in the west and east parts of maritime tropical Asia, respectively. These findings provide important insights into the response of tropical Asia's vegetation to extreme climate anomalies.

Satellites have different revisit frequencies (i.e., temporal resolutions), ranging from daily to monthly. The satellite revisit frequencies suitable for accurately monitoring the phenology of deciduous broad-leaved forests (DBF) are not well-known. To fill this knowledge gap, this study used MODIS Daily Nadir BRDF-Adjusted images to simulate EVI time series with a wide range of temporal resolutions from daily to 52 days, to investigate the impacts of satellite revisit frequency on monitoring spatial and temporal patterns of spring phenology, i.e., the start of season (SOS), of DBF in North America. Then, these EVI time series were used to extract SOS by two common phenology extraction methods (i.e., relative threshold and curvature methods). Our results reveal that 1 ) low temporal resolutions cannot accurately reconstruct real vegetation growth profile, which generally causes a false early SOS detection, 2 ) the impact of temporal resolutions is nonlinear. The accuracy of SOS detection from data with relatively high frequencies (e.g., 7 days) is only slightly lower than that from daily time series but the accuracy decreases largely with low frequencies, and 3 ) validation with ground observations from PhenoCam Network stations and an experiment using three real satellite datasets (i.e., MODIS, Landsat 8, and Sentinel-2) confirm the findings from our simulation study. This study suggests that satellites with medium temporal resolutions, such as Sentinel-2 and Landsat 8, could extract reliable phenology metrics in non-cloudy regions.