The East Kazakhstan Region is characterized by heterogeneous mountain, steppe, agricultural, and urban–industrial landscapes. However, spatially integrated assessments of land-cover conditions, agricultural dynamics, settlement expansion, and atmospheric trace-gas patterns remain limited. This study combines open multi-source Earth observation datasets and products to assess regional land cover, cropland dynamics from 2003 to 2019, built-up-area expansion from 1985 to 2025, and tropospheric nitrogen dioxide (NO2) column density from 2019 to 2024. The land-cover classification was based on Sentinel-2 imagery and ancillary geospatial datasets. Its random forest component achieved an overall accuracy of 90.17%, a Cohen’s kappa coefficient of 0.892, and a macro-averaged F1 score of 0.888. Grassland was the largest mapped land-cover class, covering 39.3% of the study region, followed by dense vegetation (16.2%), rock (14.9%), and sparse vegetation (10.6%). Mapped cropland extent increased from 4771.6 km2 in 2003 to 5043.9 km2 in 2019, representing a net increase of 272.3 km2 (5.7%), although a minor decrease occurred after 2015. The harmonized built-up area time series showed expansion within all nine major settlements, with the largest absolute increase observed in Öskemen (Ust-Kamenogorsk). Newly detected built-up pixels during 2000–2024 were mostly associated with transition from grassland and dense shrubs. Annual Sentinel-5P observations showed recurring elevated tropospheric NO2 column densities in northwestern East Kazakhstan, particularly around Öskemen. Over the built-up footprint, the area-weighted mean increased from 24.1 µmol m−2 in 2019 to 29.2 µmol m−2 in 2024. The integrated framework provides a spatially consistent regional baseline while identifying descriptive patterns that require further process-based investigation in future.

The Moroccan locust (Dociostaurus maroccanus) is one of the most economically significant locust species in the Caucasus and Central Asia. In the past, the Mediterranean region also experienced severe damage to crops and pastures, until widespread grassland conversion to cropland began in the second half of the 20th century. However, climate change, environmental shifts, land-use changes, cropland abandonment, and overgrazing are likely to alter the spatial distribution and outbreak patterns of this pest. Understanding potential changes and geographic shifts is essential for proactive pest management, including effective monitoring and control strategies. In this study, we apply Ecological Niche Modelling (ENM) using 12 machine learning algorithms, historical survey data covering the species’ full distribution range, and relevant abiotic variables to identify the most suitable areas for potential mass breeding during 1991–2020 and the near future (2021–2040), based on the “middle-of-the-road” Shared Socioeconomic Pathway (SSP2-4.5) scenario. Our results indicate significant regional shifts. Notably, breeding suitability is projected to increase in parts of Greece, Turkey, Armenia, Georgia, Kyrgyzstan, and Tajikistan. In contrast, countries such as Turkmenistan, Afghanistan, Pakistan, and Spain are likely to experience a decline in optimal breeding areas. The forecast results support field observations of a geographical shift northward and toward higher altitudes. Additionally, higher temperatures in suitable areas suggest more drought-like conditions, which typically promote locust population explosions and outbreaks. If left unaddressed, such outbreaks can cause severe economic damage to affected regions.

The Lake Chad is an endorheic lake in Sahelian Africa, with an extension to hyper arid areas. The lake basin is not exempt from global environmental changes which significantly affect fresh water resources across the globe. Despite a plethora of research on lake Chad, the daily and seasonal surface water dynamics is not clearly understood. This study probes to reconstruct the daily and seasonal surface water dynamics, change points and trends in lake Chad with a novel global daily surface water time series dataset (2003 – 2022). The key methods involved time series decomposition and filtering, trends analysis with Mann Kendall Tau and Sen’s Slope, and change point detection of abrupt shifts in daily and seasonal surface water time series. The results showed that lake Chad water depicts marked seasonal patterns. The maximum water area in all the pools is registered between the months of December – January and the inter-seasonal surface water area varies between ~1500 km2 to ~3800 - 4000 km2. On daily time scale, the southern pool shows high water area above 2400 km2 at the start and end of each year with the exception of drought years (2006 – 2017). For wet years (2004, 2018, 2019, 2020, 2021), surface water area between day 1 to around day 66, and 301 to 365/6 ranges between 2200km2 to about 2400km2. With the exception of extreme dry years, the water area between the rest of 67 – 300 days of the year is between 1600km2 – 2000km2. In contrast, the northern pool’s maximum water area ranges between 1600km2 to ~1700km2. With the exception of 2004, 2012, 2013, 2015, 2020 and 2021, the northern pool only fluctuates between ≤ 200km2 to ~800km2, which only stays for few days of the year. While surface water area coverage is quasi-stable across all seasons in the southern pool, the northern pool only has minimal water coverage from April to October yearly. Mean annual water coverage in lake Chad varied from 2953km2 to 3114km2 between 2004 and 2021 respectively. Meanwhile between 2005 – 2012 and 2016 – 2019, surface water area is below 2500km2. While the southern pool remains somewhat stable, the northern pool shows recovery and dwindling phases. Within the monitoring period, two abrupt changes were identified on the cycle of lake Chad, a decreasing trend between 2003 – 2012 (2275km2) and an increasing trend from 2013 to 2022 (2745km2), p = 0.000. In conclusion, the study found that lake Chad is slowly recovering as revealed by statistical trend analysis (Tau = 0.157, Sen’s slope = 0.0782 and p = 0.012), with an annual average increase of 28.543km2.

Water, a fundamental resource for both ecosystems and human populations, faces escalating challenges in Africa due to water stress and changes in climate, demography, and socioeconomics. Because these changes are happening at a rapid pace, it is essential to understand the dynamics of water bodies and the factors that impact them to ensure sustainable usage strategies. Our research aims to analyze the long-term trends of surface water availability in Africa, identify the causal impacts on major water bodies, and explore the similarities between different lakes. We use daily time series based on Earth observation, including the MODIS-based Global WaterPack for a daily uninterrupted time series of the continent's surface water area. Furthermore, we incorporate daily time series of hydrologically relevant variables such as precipitation, total evapotranspiration, groundwater, soil moisture, and Gross Primary Productivity (GPP) to analyze their impact on surface water dynamics of major African lakes. For this, we employ the Peter and Clark Momentary Conditional Independence causal identification algorithm. Our findings reveal subbasin-wide surface water and GPP to be the dominant drivers of surface water dynamics in most cases. We further find that dynamically similar lakes often share common drivers, allowing the generation of regional lake clusters. Understanding the drivers of African lakes may significantly help in the formulation of sustainable development strategies. In conclusion, our continent-wide analysis provides valuable insights, particularly beneficial for stakeholders engaged in international development and ecosystem protection and restoration. As we deal with the challenges of water resource management in Africa, our research aims to contribute substantively to the formulation of strategies that foster sustainability and resilience in the face of evolving environmental and socio-economic conditions.

Water availability is vital for the sustenance of livelihoods in the Lake Chad Basin. However, the daily and seasonal dynamics of open water bodies are not well understood. This study aims to (1) analyze the daily and seasonal dynamics of water bodies, (2) estimate changes in surface water area extent including trends and change points, and (3) assess the connection between surface water extent and seasonal rainfall variation. To achieve this, we used the Global WaterPack and ERA5-Land daily aggregated datasets. We employed time series decomposition, trends analysis, and temporal lag correlation in our analysis. The results showed strong seasonal patterns of natural lakes compared to reservoirs/dams. Between 2003 and 2022, Lake Chad averaged 2,475.64 km(-2). The Northern pool of Lake Chad exhibited significant fluctuations, remaining below 600 km(2) between 2005 and 2012, from 2016 to 2019), with less than 350 km(2) lasting only for a few days annually. The Southern pool averaged between 2,200 and 2,400 km(-2), except during drought years (2006-2007), specifically between the days of the year to approximately 66, and days 301-365/6. In Lake Fitri, the yearly maximum and minimum water extents were observed between days 1-59 and 305-365/6, and between days 60 and 304, respectively.

With temperatures in Central Asia (CA) increasing more than the global average, this region is one of the global hotspots affected by climate change. CA is mostly characterized by arid climate, which is why available water resources are of paramount importance for the societies, economies, and the environment. In this regard, quantifying changes on the land surface and controlling factors that influence land surface dynamics are of great interest to improve our understanding of climate change impacts in this region. Hence, this study analyzes multivariate time series covering climatic, hydrological and Earth observation (EO)-based land surface variables. The used EO time series characterize the land surface and include data on the normalized difference vegetation index (NDVI), surface water area (SWA), and snow cover area (SCA) between December 2002 to November 2021. To analyze these time series, we employ trend analyses and a causal discovery algorithm. Both analyses were carried out at multiple spatial and temporal scales. The results show that NDVI trends were mostly significantly negative in the Northwest and positive in the Northeast of CA in summer. In summer and autumn, the percentage of significant negative NDVI trends outweighed the positive trends. For SWA, the detected trends were mostly significant negative throughout all scales. Significant negative trends were retrieved for SCA across all seasons, except for autumn regionally. Particularly the Tian Shan and Pamir mountains show significant declines of SCA in winter and spring. The causal analyses revealed that the NDVI is mostly controlled by water availability in summer. In spring and autumn, temperature is the leading driver on the NDVI. Likewise, temperature is found to largely control SWA in spring and autumn. SCA is mostly negatively coupled to temperature during spring and autumn. A positive coupling between SCA and precipitation is identified in winter.

Africa has the most dynamic demographic development worldwide. Current projections predict a population of > 3 billion people by the end of the century. Sub-Saharan Africa alone will likely see a 40% increase in population between 2020 and 2050. Although it is well known that large parts of Africa are in a constant state of water stress, its surface water resources remain understudied. This study analyses long-term trends of surface water in Africa. It identifies causal impacts on major lakes and reservoirs for the timeframe 2003-2020, as well as dynamic and causal similarities between the various lakes. For this, a set of daily time series based on Earth observation is employed. Global WaterPack data is used for a daily uninterrupted time series of the continent's surface water area. Additionally, an array of relevant independent variables, namely precipitation, total evapotranspiration, groundwater, soil moisture, and gross primary productivity (GPP) in different land use areas is analysed. For causal identification, the Peter and Clark Momentary Conditional Independence algorithm is used. Findings show that > 42% of African countries and > 34% of African ecoregions experience shrinking surface water area. Over 80% of investigated surface water bodies are driven by the surface water in their upstream subbasins and GPP in agriculturally used areas. About 85% of investigated lakes are significantly driven by agricultural usage, often in the form of water abstraction, as referenced regional studies confirm. Our analysis demonstrates the feasibility of conducting causal analyses of surface water dynamics using Earth observation data. Dynamically similar lakes are often impacted by the same drivers, forming regional lake clusters. Considering the causes identified may greatly help adapt strategies for sustainable development. A causality analysis to identify drivers of surface water dynamics has, to our knowledge, never been performed before on this scale and at such high temporal resolution.

Open surface water across the globe is essential for many life forms and is an important source for human settlements, agriculture, and industry. The presence and variation in time and space is influenced by different natural conditions (e.g. climate, topography, geology) and human use (e.g. irrigation, flood protection). The information on the spatial and temporal distribution of open surface water is fundamental for many disciplines and is also required as an essential parameter for hydrological and climatological modelling. Here, we present a dataset derived from satellite earth observation, which is based on more than 6.3 million single MODIS products with a volume of approx. 300 TB. The resulting dataset reflects the situation of open surface water on a global scale for each day over the time period from 2003 to 2022 at a spatial resolution of 250 m. The dataset enables the analysis of the development of lake and reservoir surface areas, freezing cycles, and inundation areas.

This study introduces a methodological approach utilizing dense optical satellite time series data to monitor the spatiotemporal evolution of onshore coastal pond aquaculture in Asia. The methodology, applied across 22 coastal countries, involves extracting, delineating, and analyzing pond aquaculture at a continental scale. The comprehensive dataset reveals distinct expansion patterns from the 1980s to 2019, indicating significant growth and spatial shifts. Geostatistical analyses on growth rates and spatial expansion patterns at various administrative levels unveil the developmental dynamics, showcasing the impacts on coastal ecosystems and protective vegetation barriers. The findings emphasize the critical role of spatial data in understanding and managing the extensive pond aquaculture systems that dominate Asia's coastal regions.

Desert locust (Schistocerca gregaria) plagues threaten agricultural production, food security and the environment across Africa, the Middle East, and Southwest Asia. Control methods targeting adult desert locusts present significant challenges and financial costs. Recognizing this, we developed a ground-breaking fuzzy set Mamdani type inference model that provides an innovative solution for early warning alerts. The model aids in predicting the juvenile stages of locust development, thereby preventing wide-scale locust swarming and mitigating its extensive damages and socioeconomic costs. The novelty of our approach lies in our unique application of environmental variables relevant for locust breeding to estimate the timing and location of desert locust hatching. Additionally, we improved the algorithmic handling of these variables, with localized desert locust bands data used as a proxy for hatching timing with a temporal offset of 35 days. The model's boundary conditions were determined using a training area in Sudan, where comprehensive ground data was available. This rule set was subsequently applied to Turkana County in Kenya, a data-scarce region, demonstrating the model's applicability and success in different contexts. The model's accuracy, assessed by data from the Sudan training site, demonstrated a remarkable score of 82% for true predictions. Furthermore, the model correctly identified the months of highest hatching probabilities in Turkana during 2020, demonstrating its real-world effectiveness and practical value. A correlation analysis affirmed that hatching was associated with increases in chlorophyll levels and precipitation accumulations. Our study marks a significant advancement in predicting the timing of hatching using fuzzy logic in data-scarce environments. By operationalizing more targeted early responses to desert locust infestations, our model facilitates more effective locust control. This study stands as an important contribution to locust management strategies, with substantial implications for agricultural production and food security in affected regions.

Negative impacts on agricultural activities by different locust species are well documented and have always been one of the major threats to food security and livelihoods, especially for local communities. Locust management and control have led to less frequent and intense plagues and outbreaks worldwide. However, political insecurity and armed conflicts affect locust management, and can as well as changing climate, and land use management contribute to new outbreaks. In the context of the increasing world population and higher demand for agricultural production, locust pests will remain of high concern. Geospatial and remote sensing data have become an important source of information for different applications within locust research and management. However, there is still a gap between available information and actual practical usage. In this study, we demonstrate the importance of geospatial and remote sensing data and how this information can be prepared for a straightforward application for stakeholders. For this purpose, we use the h3-hexagonal hierarchical geospatial indexing system to simplify and structure spatial information into standardized hexagon units. The presented concept provides decision makers and ground teams with a simplified information database that contains area-wide information over time and space and can be used without detailed geospatial knowledge and background. The concept is designed for the use case of Italian locust management in the Pavlodar region (Kazakhstan) and based on actual practices. It can be extrapolated to any other study area or species of interest. Our results underline the importance of actual land management on locust presence. Up-to-date land management information can be derived from time-series analyses of remote sensing data. Furthermore, essential meteorological data are used to generate locust-specific climatic characteristics within the h3-system. Within this system, areal prioritizing for locust management can be achieved based on the included spatial information and experience from ongoing practices.

The Moroccan locust has been considered one of the most dangerous agricultural pests in the Mediterranean region. The economic importance of its outbreaks diminished during the second half of the 20th century due to a high degree of agricultural industrialization and other human-caused transformations of its habitat. Nevertheless, in Sardinia (Italy) from 2019 on, a growing invasion of this locust species is ongoing, being the worst in over three decades. Locust swarms destroyed crops and pasture lands of approximately 60,000 ha in 2022. Drought, in combination with increasing uncultivated land, contributed to forming the perfect conditions for a Moroccan locust population upsurge. The specific aim of this paper is the quantification of land cover land use (LCLU) influence with regard to the recent locust outbreak in Sardinia using remote sensing data. In particular, the role of untilled, fallow, or abandoned land in the locust population upsurge is the focus of this case study. To address this objective, LCLU was derived from Sentinel-2A/B Multispectral Instrument (MSI) data between 2017 and 2021 using time-series composites and a random forest (RF) classification model. Coordinates of infested locations, altitude, and locust development stages were collected during field observation campaigns between March and July 2022 and used in this study to assess actual and previous land cover situation of these locations. Findings show that 43% of detected locust locations were found on untilled, fallow, or uncultivated land and another 23% within a radius of 100 m to such areas. Furthermore, oviposition and breeding sites are mostly found in sparse vegetation (97%). This study demonstrates that up-to-date remote sensing data and target-oriented analyses can provide valuable information to contribute to early warning systems and decision support and thus to minimize the risk concerning this agricultural pest. This is of particular interest for all agricultural pests that are strictly related to changing human activities within transformed habitats.

In this study, we present a fused multi-scale approach to model habitat suitability index (HSI) maps for three different locust species. The presented methodology was applied for the Italian locust (Calliptamus italicus, CIT) in Pavlodar oblast, Northern Kazakhstan, for the Moroccan locust (Dociostaurus maroccanus, DMA) in Turkistan oblast, South Kazakhstan and for the desert locust (Schistocerca gregaria) in Awash river basin, Ethiopia, Djibouti, Somalia. The main novelty is based on implementing results from ecological niche modelling (ENM) with time-series analyses of high spatial resolution remote sensing data (Sentinel-2) and further auxiliary datasets in a fused HSI model. Within the ENM important climatic variables (e.g. temperature, rainfall) and edaphic variables (e.g. sand and moisture contents) are included at a coarse spatial resolution. The analyses of Sentinel-2 time-series data enables mapping locust breeding habitats based on recent remotely sensed land observation at high spatial resolution and mirror the actual vegetation state, land use, land cover and in this way identify areas with favorable conditions for egg survival and breeding. The fused HSI results for year 2019 were validated based on ground field observation and reach area under curve (AUC) performance of 0.747% for CIT, 0.850% for DMA and 0.801% for desert locust. The innovation of this study is a multi-scale approach which accounts not only for climatic and environmental conditions but also for current vegetation and land management situation. This kind of up-to-date spatial detailed information on breeding suitability could enable area prioritization for risk assessment, monitoring and early intervention of locust pests.

The Indo-Gangetic river basins feature a wide range of climatic, topographic, and land cover characteristics providing a suitable setting for the exploration of multivariate time series. Here, we collocated a comprehensive feature space for these river basins including Earth observation time series on the normalized difference vegetation index (NDVI), surface water area (SWA), and snow cover area (SCA) in combination with driving variables between December 2002 and November 2020. First, we evaluated changes using multi-faceted trend analyses. Second, we employed the causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI) to disentangle interactions within the feature space. PCMCI quantifies direct and indirect relationships between variables and has been rarely applied to remote sensing applications. The results showed that vegetation greening continues significantly. Irrigated croplands in the Indus basin indicated the highest trend magnitude (0.042 NDVI/decade⁻¹). At annual and basin scale, positive trends were also identified for SWA in the Indus (837 km²/decade⁻¹) and Ganges basin (677 km²/decade⁻¹). Annual trends in SCA were insignificant at basin scale. Considering elevation zones, negative SCA trends were found in high altitudes of the Ganges and Brahmaputra river basins. Similarly, NDVI and SWA showed positive trends in high elevations. Furthermore, the causal analysis revealed that NDVI was controlled by water availability. SWA was directly influenced by river discharge and indirectly by precipitation. In high altitudes, SWA was controlled by SCA and temperature. Precipitation and temperature were identified as important drivers of SCA with spatio-temporal variations. With amplified climate change, the joint exploitation of time series will be of increasing importance to further enhance the understanding of land surface change and complex interplays across the spheres of the Earth system. The insights of this study and used methods could greatly support the development of climate change adaptation strategies for the investigated region.

Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource-if not overexploited-sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.

Observations of changes in terrestrial water storage (TWS) obtained from the satellite mission GRACE (Gravity Recovery and Climate Experiment) have frequently been used for water cycle studies and for the improvement of hydrological models by means of calibration and data assimilation. However, due to a low spatial resolution of the gravity field models, spatially localized water storage changes, such as those occurring in lakes and reservoirs, cannot properly be represented in the GRACE estimates. As surface storage changes can represent a large part of total water storage, this leads to leakage effects and results in surface water signals becoming erroneously assimilated into other water storage compartments of neighbouring model grid cells. As a consequence, a simple mass balance at grid/regional scale is not sufficient to deconvolve the impact of surface water on TWS. Furthermore, non-hydrology-related phenomena contained in the GRACE time series, such as the mass redistribution caused by major earthquakes, hamper the use of GRACE for hydrological studies in affected regions. In this paper, we present the first release (RL01) of the global correction product RECOG (REgional COrrections for GRACE), which accounts for both the surface water (lakes and reservoirs, RECOG-LR) and earthquake effects (RECOG-EQ). RECOG-LR is computed from forward-modelling surface water volume estimates derived from satellite altimetry and (optical) remote sensing and allows both a removal of these signals from GRACE and a relocation of the mass change to its origin within the outline of the lakes/reservoirs. The earthquake correction, RECOG-EQ, includes both the co-seismic and post-seismic signals of two major earthquakes with magnitudes above Mw9. We discuss that applying the correction dataset (1) reduces the GRACE signal variability by up to 75 % around major lakes and explains a large part of GRACE seasonal variations and trends, (2) avoids the introduction of spurious trends caused by leakage signals of nearby lakes when calibrating/assimilating hydrological models with GRACE, and (3) enables a clearer detection of hydrological droughts in areas affected by earthquakes. A first validation of the corrected GRACE time series using GPS-derived vertical station displacements shows a consistent improvement of the fit between GRACE and GNSS after applying the correction. Data are made available on an open-access basis via the Pangaea database (RECOG-LR: Deggim et al., 2020a, https://doi.org/10.1594/PANGAEA.921851; RECOG-EQ: Gerdener et al., 2020b, https://doi.org/10.1594/PANGAEA.921923).

Remote sensing time series offer the possibility to monitor surface water at dense temporal intervals. Open data archives as well as developments in cloud computing are the main drivers towards and increased availability of raw data allowing for the extraction of detailed information on water bodies such as natural lakes and artificial reservoirs. At the same time, changes in precipitation patterns, increasing frequency and intensity of droughts, intensification of human water use, and regulatory upstream measurements affect water resources around the world today. With regard to water availability and supply-demand balance, artificial water reservoirs have become most important elements e.g. for hydropower, irrigated agriculture, flood control, as well as for domestic and industrial water use. Nevertheless, publicly accessible information on reservoirs is still not harmonized and available at global scale. Therefore, it is more essential than ever to acquire detailed knowledge about spatio-temporal variability of water resources - especially reservoirs - and the drivers of their dynamics. In this study, we analyze daily water extent time series of the 1267 largest reservoirs worldwide based on the existing DLR-DFD Global WaterPack product derived from MODIS data (Klein et al., 2017). The study aims to present an experimental way of spatio-temporal variability analysis by implementing the TIMESAT software which is usually used for vegetation analyses. In our experimental approach we derive information on the timing when the open surface water areas of reservoirs increase and decrease by identifying start date, end date and duration of such reservoir cycles as well as timing of maximum surface water extent (hydro-metrics). For four selected reservoirs, these hydro-metrics derived from surface water extent are compared with hydro-metrics derived from in-situ water level measurements or altimetry datasets and are discussed in more detail. Based on the presented examples we demonstrate the potential of high temporal resolution surface water extent data and spatio-temporal variability analyses with TIMESAT for future applications supporting the understanding of reservoir variability as a result of water management and hydroclimatic variability.

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.

Recently, locust outbreaks around the world have destroyed agricultural and natural vegetation and caused massive damage endangering food security. Unusual heavy rainfalls in habitats of the desert locust (Schistocerca gregaria) and lack of monitoring due to political conflicts or inaccessibility of those habitats lead to massive desert locust outbreaks and swarms migrating over the Arabian Peninsula, East Africa, India and Pakistan. At the same time, swarms of the Moroccan locust (Dociostaurus maroccanus) in some Central Asian countries and swarms of the Italian locust (Calliptamus italicus) in Russia and China destroyed crops despite developed and ongoing monitoring and control measurements. These recent events underline that the risk and damage caused by locust pests is as present as ever and affects 100 million of human lives despite technical progress in locust monitoring, prediction and control approaches. Remote sensing has become one of the most important data sources in locust management. Since the 1980s, remote sensing data and applications have accompanied many locust management activities and contributed to an improved and more effective control of locust outbreaks and plagues. Recently, open-access remote sensing data archives as well as progress in cloud computing provide unprecedented opportunity for remote sensing-based locust management and research. Additionally, unmanned aerial vehicle (UAV) systems bring up new prospects for a more effective and faster locust control. Nevertheless, the full capacity of available remote sensing applications and possibilities have not been exploited yet. This review paper provides a comprehensive and quantitative overview of international research articles focusing on remote sensing application for locust management and research. We reviewed 110 articles published over the last four decades, and categorized them into different aspects and main research topics to summarize achievements and gaps for further research and application development. The results reveal a strong focus on three species-the desert locust, the migratory locust (Locusta migratoria), and the Australian plague locust (Chortoicetes terminifera)-and corresponding regions of interest. There is still a lack of international studies for other pest species such as the Italian locust, the Moroccan locust, the Central American locust (Schistocerca piceifrons), the South American locust (Schistocerca cancellata), the brown locust (Locustana pardalina) and the red locust (Nomadacris septemfasciata). In terms of applied sensors, most studies utilized Advanced Very-High-Resolution Radiometer (AVHRR), Satellite Pour l'Observation de la Terre VEGETATION (SPOT-VGT), Moderate-Resolution Imaging Spectroradiometer (MODIS) as well as Landsat data focusing mainly on vegetation monitoring or land cover mapping. Application of geomorphological metrics as well as radar-based soil moisture data is comparably rare despite previous acknowledgement of their importance for locust outbreaks. Despite great advance and usage of available remote sensing resources, we identify several gaps and potential for future research to further improve the understanding and capacities of the use of remote sensing in supporting locust outbreak- research and management.

Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product’s performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.