Land surface albedo, a key parameter to derive Earth's surface energy balance, is used in the parameterization of numerical weather prediction, climate monitoring and climate change impact assessments. Changes in albedo due to fire have not been fully investigated on a continental and global scale. The main goal of this study, therefore, is to quantify the changes in instantaneous shortwave albedo produced by biomass burning activities and their associated radiative forcing. The study relies on the MODerate-resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned-area product to create an annual composite of areas affected by fire and the MCD43C2 bidirectional reflectance distribution function (BRDF) albedo snow-free product to compute a bihemispherical reflectance time series. The approximate day of burning is used to calculate the instantaneous change in shortwave albedo. Using the corresponding National Centers for Environmental Prediction (NCEP) monthly mean downward solar radiation flux at the surface, the global radiative forcing associated with fire was computed. The analysis reveals a mean decrease in shortwave albedo of −0.014 (1σ = 0.017), causing a mean positive radiative forcing of 3.99 Wm−2 (1σ = 4.89) over the 2002–20012 time period in areas affected by fire. The greatest drop in mean shortwave albedo change occurs in 2002, which corresponds to the highest total area burned (378 Mha) observed in the same year and produces the highest mean radiative forcing (4.5 Wm−2). Africa is the main contributor in terms of burned area, but forests globally give the highest radiative forcing per unit area and thus give detectable changes in shortwave albedo. The global mean radiative forcing for the whole period studied (~0.0275 Wm−2) shows that the contribution of fires to the Earth system is not insignificant.

Smallholder farmers are the mainstay of the agricultural economies of sub-Saharan Africa (SSA), where they produce several crops, predominantly centered on maize. Smallholder productivity remains limited resulting from a range of confounding factors, but a primary cause is loss from pests and diseases, particularly insects. To improve productivity, recommendations for the mitigation of crop loss globally include early-warning and management systems for in-season indigenous pests. There are many early-warning systems in temperate regions; however, such systems are poorly established in Africa. This is in part due to the need for a combination of pest modeling, data handling and dissemination infrastructure, capacity, and resource provision. While each of these components is progressing in Africa, the means to successfully deploy such systems remain limited. To bridge this, the development of the Pest Risk Information Service (PRISE) began in 2017 for farmers in SSA. Implemented in Kenya, Ghana, Malawi, and Zambia, PRISE developed temperature-driven phenology models for major maize, bean, and tomato pests. Using downscaled and processed Earth Observation data to drive the models, PRISE partnered with African national agencies to communicate pre- and in-season pest alerts that forecast the time to act against key insect pests. Alerts were designed to be integrated into country-specific Good Agricultural Practice (GAP) recommendations to provide a complementary package to agricultural stakeholders. End line studies with farmers showed that those who received information about the target crops including PRISE pest forecasts, generally reported better outcomes in terms of reduced losses and increased incomes compared with farmers who did not.

Earth observation (EO) data play a crucial role in monitoring ecosystems and environmental processes. Time series of satellite data are essential for long-term studies in this context. Working with large volumes of satellite data, however, can still be a challenge, as the computational environment with respect to storage, processing and data handling can be demanding, which sometimes can be perceived as a barrier when using EO data for scientific purposes. In particular, open-source developments which comprise all components of EO data handling and analysis are still scarce. To overcome this difficulty, we present Tools for Analyzing Time Series of Satellite Imagery (TATSSI), an open-source platform written in Python that provides routines for downloading, generating, gap-filling, smoothing, analyzing and exporting EO time series. Since TATSSI integrates quality assessment and quality control flags when generating time series, data quality analysis is the backbone of any analysis made with the platform. We discuss TATSSI’s 3-layered architecture (data handling, engine and three application programming interfaces (API)); by allowing three APIs (a native graphical user interface, some Jupyter Notebooks and the Python command line) this development is exceptionally user-friendly. Furthermore, to demonstrate the application potential of TATSSI, we evaluated MODIS time series data for three case studies (irrigation area changes, evaluation of moisture dynamics in a wetland ecosystem and vegetation monitoring in a burned area) in different geographical regions of Mexico. Our analyses were based on methods such as the spatio-temporal distribution of maxima over time, statistical trend analysis and change-point decomposition, all of which were implemented in TATSSI. Our results are consistent with other scientific studies and results in these areas and with related in-situ data.

Land surface albedo, a key parameter to derive Earth's surface energy balance, is used in the parameterization of numerical weather prediction, climate monitoring and climate change impact assessments. Changes in albedo due to fire have not been fully investigated at continental and global scale. The main goal of this study therefore, is to quantify the changes in albedo produced by biomass burning activities and their associated shortwave radiative forcing. The study relies on the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned area product to create an annual composite of areas affected by fire and the MCD43C2 BRDF-Albedo snow-free product to compute a bihemispherical reflectance time series. The approximate day of burn is used to calculate the instantaneous change in shortwave Albedo. Using the corresponding National Centers for Environmental Prediction (NCEP) monthly mean downward solar radiation flux at the surface, the global radiative forcing associated to fire was computed. The analysis reveals a mean decrease in shortwave albedo of −0.023 (1σ = 0.018) causing a mean positive radiative forcing of 6.31 W m–2 (1σ = 5.04) over the 2002–2012 time period in areas affected by fire. The greatest drop in mean shortwave albedo change occurs in 2002, which corresponds to the highest total area burnt (3.66 Mha) observed in the same year and produces the highest mean radiative forcing (6.75 W m–2). Africa is the main contributor in terms of burned area but forests globally are giving the highest radiative forcing per unit area, thus give detectable changes in shortwave albedo. The global mean radiative forcing for the whole studied period ~ 0.04 W m–2 shows that the contribution of fires into the Earth system is not insignificant.

Peatland restoration and conservation, including sustainable peatland management, require robust, consistent, efficient, and accessible methodologies to map peatlands, and identify and better understand the changes and impacts of natural and anthropogenic changes, including restoration measures. Peatland mapping and monitoring tools should enable users to (i) locate peat soils; (ii) identify peatlands at risk of degradation and in need of protection and/or restoration; (iii) monitor the success of management interventions; and (iv) support national and international reporting requirements. Given the spatial scale of peatlands lend themselves to use of Earth Observation techniques. In response to these needs, ESA’s WorldPeatland project will work closely with stakeholders in the peatland community to define, validate, and promote Earth Observation-based products and tools that facilitate the mapping and monitoring of peatlands in different states and biomes. This presentation consists of two parts. In the first part, we summarize the outcome of a survey on the user requirements for Earth Observation-based peatland mapping and monitoring and the associated EO-based tools. The survey consisted of an online questionnaire and two user requirement workshops. In the second part, the project structure is presented with details about the planned development of innovative monitoring products and tools along with some initial results from case studies.

Monitoring air quality is critical for understanding pollution impacts on public health. The Copernicus Atmosphere Monitoring Service (CAMS) provides estimates of nitrogen oxides (NO_x), nitrogen dioxide (NO₂), fine particulate matter (PM_2.5) and inhalable coarse particles (PM₁₀), but its spatial resolution may limit accuracy at individual urban stations. This study introduces a graph centrality-based framework for station-level correction of CAMS European reanalysis data using in situ observations from ARPA Lombardia (2018 to 2021). Stations are modelled as nodes in a multiplex graph, with spatial influence quantified through six centrality measures including topology-based and context-aware variants such as the Adapted PageRank Algorithm (APA) and Eigendata. The centrality features are integrated with Geo-Temporally Weighted Regression (GTWR), Extreme Gradient Boosting (XGBoost) and Random Forest with Spatio-Temporal Kriging (RF-STK), evaluated under station, temporal and combined holdout strategies. Under station holdout, XGBoost achieves an $R^2$ of up to 0.66 for NO₂, while RF-STK can exceed this when residual spatial kriging is active. Adding OpenStreetMap (OSM)-context APA centrality yields pollutant-dependent changes: gains for NO_x and PM_2.5, mixed results for NO₂, and a temporal improvement for PM₁₀ with RF-STK. Results confirm that graph-theoretic structural context complements station-level CAMS correction but is not a general surrogate for pollutant dispersion.