Moderate resolution satellite sensors including MODIS (Moderate Resolution Imaging Spectroradiometer) already provide more than 10 yr of observations well suited to describe and understand the dynamics of earth's surface. However, these time series are associated with significant uncertainties and incomplete because of cloud cover. This study compares eight methods designed to improve the continuity by filling gaps and consistency by smoothing the time course. It includes methods exploiting the time series as a whole (iterative caterpillar singular spectrum analysis (ICSSA), empirical mode decomposition (EMD), low pass filtering (LPF) and Whittaker smoother (Whit)) as well as methods working on limited temporal windows of a few weeks to few months (adaptive Savitzky–Golay filter (SGF), temporal smoothing and gap filling (TSGF), and asymmetric Gaussian function (AGF)), in addition to the simple climatological LAI yearly profile (Clim). Methods were applied to the MODIS leaf area index product for the period 2000–2008 and over 25 sites showed a large range of seasonal patterns. Performances were discussed with emphasis on the balance achieved by each method between accuracy and roughness depending on the fraction of missing observations and the length of the gaps. Results demonstrate that the EMD, LPF and AGF methods were failing because of a significant fraction of gaps (more than 20%), while ICSSA, Whit and SGF were always providing estimates for dates with missing data. TSGF (Clim) was able to fill more than 50% of the gaps for sites with more than 60% (80%) fraction of gaps. However, investigation of the accuracy of the reconstructed values shows that it degrades rapidly for sites with more than 20% missing data, particularly for ICSSA, Whit and SGF. In these conditions, TSGF provides the best performances that are significantly better than the simple Clim for gaps shorter than about 100 days. The roughness of the reconstructed temporal profiles shows large differences between the various methods, with a decrease of the roughness with the fraction of missing data, except for ICSSA. TSGF provides the smoothest temporal profiles for sites with a % gap > 30%. Conversely, ICSSA, LPF, Whit, AGF and Clim provide smoother profiles than TSGF for sites with a % gap < 30%. Impact of the accuracy and smoothness of the reconstructed time series were evaluated on the timing of phenological stages. The dates of start, maximum and end of the season are estimated with an accuracy of about 10 days for the sites with a % gap < 10% and increases rapidly with the % gap. TSGF provides more accurate estimates of phenological timing up to a % gap < 60%.

Arctic fires can release large amounts of carbon from permafrost peatlands. Satellite observations reveal that fires burned ~4.7 million hectares in 2019 and 2020, accounting for 44% of the total burned area in the Siberian Arctic for the entire 1982–2020 period. The summer of 2020 was the warmest in four decades, with fires burning an unprecedentedly large area of carbon-rich soils. We show that factors of fire associated with temperature have increased in recent decades and identified a near-exponential relationship between these factors and annual burned area. Large fires in the Arctic are likely to recur with climatic warming before mid-century, because the temperature trend is reaching a threshold in which small increases in temperature are associated with exponential increases in the area burned.

The African continent is facing one of the driest periods in the past three decades as well as continued deforestation. These disturbances threaten vegetation carbon (C) stocks and highlight the need for improved capabilities of monitoring large-scale aboveground carbon stock dynamics. Here we use a satellite dataset based on vegetation optical depth derived from low-frequency passive microwaves (L-VOD) to quantify annual aboveground biomass-carbon changes in sub-Saharan Africa between 2010 and 2016. L-VOD is shown not to saturate over densely vegetated areas. The overall net change in drylands (53% of the land area) was −0.05 petagrams of C per year (Pg C yr⁻¹) associated with drying trends, and a net change of −0.02 Pg C yr⁻¹ was observed in humid areas. These trends reflect a high inter-annual variability with a very dry year in 2015 (net change, −0.69 Pg C) with about half of the gross losses occurring in drylands. This study demonstrates, first, the applicability of L-VOD to monitor the dynamics of carbon loss and gain due to weather variations, and second, the importance of the highly dynamic and vulnerable carbon pool of dryland savannahs for the global carbon balance, despite the relatively low carbon stock per unit area.

Above‐ground vegetation biomass is one of the major carbon sinks and provides both provisioning (e.g., forestry products) and regulating ecosystem services (by sequestering carbon). Continuing deforestation and climate change threaten this natural resource but can effectively be countered by national conservation policies. Here we present time series (1999–2017) derived from complementary satellite systems to describe a phenomenon of global significance: the greening of South China Karst. We find a major increase in growing season vegetation cover from 69% in 1999 to 81% in 2017 occurring over ~1.4 million km². Over 1999–2012, we report one of the globally largest increases in biomass to occur in the South China Karst region (on average +4% over 0.9 million km²), which accounts for ~5% of the global areas characterized with increases in biomass. These increases in southern China's vegetation have occurred despite a decline in rainfall (−8%) and soil moisture (−5%) between 1999 and 2012 and are derived from effects of forestry and conservation activities at an unprecedented spatial scale in human history (~20,000 km² yr⁻¹ since 2002). These findings have major implications for the provisioning of ecosystem services not only for the Chinese karst ecosystem (e.g., carbon storage, water filtration, and timber production) but also for the study of global carbon cycles.

The rapidly growing human population in sub-Saharan Africa generates increasing demand for agricultural land and forest products, which presumably leads to deforestation. Conversely, a greening of African drylands has been reported, but this has been difficult to associate with changes in woody vegetation. There is thus an incomplete understanding of how woody vegetation responds to socio-economic and environmental change. Here we used a passive microwave Earth observation data set to document two different trends in land area with woody cover for 1992–2011: 36% of the land area (6,870,000 km²) had an increase in woody cover largely in drylands, and 11% had a decrease (2,150,000 km²), mostly in humid zones. Increases in woody cover were associated with low population growth, and were driven by increases in CO₂ in the humid zones and by increases in precipitation in drylands, whereas decreases in woody cover were associated with high population growth. The spatially distinct pattern of these opposing trends reflects, first, the natural response of vegetation to precipitation and atmospheric CO₂, and second, deforestation in humid areas, minor in size but important for ecosystem services, such as biodiversity and carbon stocks. This nuanced picture of changes in woody cover challenges widely held views of a general and ongoing reduction of the woody vegetation in Africa.