A Global MODIS Water Vapor Database for the Operational Atmospheric Correction of Historic and Recent Landsat Imagery

This article was supported by the German Research Foundation (DFG) and the Open Access Publication Fund of Humboldt-Universität zu Berlin.Analysis Ready Data (ARD) have undergone the most relevant pre-processing steps to satisfy most user demands. The freely available software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring) is capable of generating Landsat ARD. An essential step of generating ARD is atmospheric correction, which requires water vapor data. FORCE relies on a water vapor database obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). However, two major drawbacks arise from this strategy: (1) The database has to be compiled for each study area prior to generating ARD; and (2) MODIS and Landsat commissioning dates are not well aligned. We have therefore compiled an application-ready global water vapor database to significantly increase the operational readiness of ARD production. The free dataset comprises daily water vapor data for February 2000 to July 2018 as well as a monthly climatology that is used if no daily value is available. We systematically assessed the impact of using this climatology on surface reflectance outputs. A global random sample of Landsat 5/7/8 imagery was processed twice (i) using daily water vapor (reference) and (ii) using the climatology (estimate), followed by computing accuracy, precision, and uncertainty (APU) metrics. All APU measures were well below specification, thus the fallback usage of the climatology is generally a sound strategy. Still, the tests revealed that some considerations need to be taken into account to help quantify which sensor, band, climate, and season are most or least affected by using a fallback climatology. The highest uncertainty and bias is found for Landsat 5, with progressive improvements towards newer sensors. The bias increases from dry to humid climates, whereas uncertainty increases from dry and tropic to temperate climates. Uncertainty is smallest during seasons with low variability, and is highest when atmospheric conditions progress from a dry to a wet season (and vice versa).Peer Reviewe

A generalized framework for drought monitoring across Central European grassland gradients with Sentinel-2 time series

Fractional cover time series of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil from remote sensing provide essential detail to understand how grasslands are affected by recent and future drought periods in the 21st century. In this regard, Sentinel-2A/B offer frequent large-area observations, which have not yet been fully exploited for a spatially continuous drought monitoring of highly dynamic Central European grasslands. In this study, we developed a generalized drought monitoring framework for Central European grasslands linking Sentinel-2 data, field survey information, and spectral unmixing. We first implemented a consistent and repeatable strategy to obtain a grassland spectral library supported by the Europe-wide Land Use/Cover Area frame statistical Survey (LUCAS) and multitemporal Sentinel-2 data. Our library captured the spectral variability of PV, NPV, and soil cover from 12 grassland areas distributed along typical environmental and land use gradients of Central Europe. We trained a generalized regression-based unmixing model with synthetic data generated from the spectral library and compared fractional cover estimates to a multitemporal reference dataset. PV, NPV, and soil were estimated with good accuracy, achieving MAEs of 6.54%, 13.7%, and 12.2%, respectively. Local unmixing models trained on area-specific library subsets were overall outperformed by the generalized model highlighting the value of a comprehensive grassland library for generalized spectral unmixing. Based on fractional cover time series from 2017 to 2021, we calculated time series of the grassland-specific Normalized Difference Fraction Index (NDFI) capturing proportions of NPV and soil relative to PV. Comparison of annual growing season drought metrics derived from the NDFI to annual meteorological drought statistics from the Standardized Precipitation Evapotranspiration Index (SPEI) as well as the Soil Moisture Index (SMI) revealed widespread drought impacts on grasslands during the persistent drought period in Central Europe from 2018 to 2020. While impacts on grasslands overall closely followed meteorological and soil drought conditions, regionally varying drought metrics underline that local to regional environmental and hydrological conditions shaped the drought response of Central European grasslands. Our study emphasizes the value of combining Sentinel-2 data, field survey information, and spectral unmixing to enable drought monitoring across grassland gradients of Central Europe with Sentinel-2 time series.Peer Reviewe

Large-scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand

Grasslands provide important ecosystem services to society, including biodiversity, water security, erosion control, and forage production. Grasslands are also vulnerable to droughts, rendering their future vitality under climate change uncertain. Yet, the grassland response to drought is not well understood, especially for heterogeneous Central European grasslands. We here fill this gap by quantifying the spatiotemporal sensitivity of grasslands to drought using a novel remote sensing dataset from Landsat/Sentinel-2 paired with climate re-analysis data. Specifically, we quantified annual grassland vitality at fine spatial scale and national extent (Germany) from 1985 to 2021. We analyzed grassland sensitivity to drought by testing for statistically robust links between grassland vitality and common drought indices. We furthermore explored the spatiotemporal variability of drought sensitivity for 12 grassland habitat types given their different biotic and abiotic features. Grassland vitality maps revealed a large-scale reduction of grassland vitality during past droughts. The unprecedented drought of 2018–2019 stood out as the largest multi-year vitality decline since the mid-1980s. Grassland vitality was consistently coupled to drought (R2 = .09–.22) with Vapor Pressure Deficit explaining vitality best. This suggests that high atmospheric water demand, as observed during recent compounding drought and heatwave events, has major impacts on grassland vitality in Central Europe. We found a significant increase in drought sensitivity over time with highest sensitivities detected in periods of extremely high atmospheric water demand, suggesting that drought impacts on grasslands are becoming more severe with ongoing climate change. The spatial variability of grassland drought sensitivity was linked to different habitat types, with declining sensitivity from dry and mesic to wet habitats. Our study provides the first large-scale, long-term, and spatially explicit evidence of increasing drought sensitivities of Central European grasslands. With rising compound droughts and heatwaves under climate change, large-scale grassland vitality loss, as in 2018–2019, will thus become more likely in the future.Peer Reviewe

evaluation of compositing windows for Landsat and Sentinel-2 time series

The article processing charge was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 491192747 and the Open Access Publication Fund of Humboldt-Universität zu Berlin.Landsat and Sentinel-2 data archives provide ever-increasing amounts of satellite data. However, the availability of usable observations greatly varies spatially and temporally. Pixel-based compositing that generates temporally equidistant cloud-free synthetic images can mitigate temporal variability, by constructing uninterrupted time series using different compositing windows. Here, we evaluated the feasibility of using compositing windows ranging from five days to one year for 1984–2021 Landsat and 2015–2021 Sentinel 2 time series to derive uninterrupted time series across Europe. We considered separate and joint use of both data archives and analyzed the spatio-temporal availability of composites during each calendar year and pixel-specific growing season across a variety of time windows and hypothesizing data interpolation. Our results demonstrated opportunities and limitations in the available data records to support medium- and long-term analyses requiring uninterrupted time series of composites with sub-annual temporal resolution. Spatial disparities across different compositing windows provide guidance on the feasibility of workflows relying on different data densities and on the challenges in wall-to-wall analyses. The feasibility of consistent time series based on composites with sub-monthly aggregation periods was mostly limited to the combined Landsat and Sentinel-2 archives after 2015, yet in some geographies requires interpolation of up to 50% of data.Peer Reviewe

National tree species mapping using Sentinel-1/2 time series and German National Forest Inventory data

Spatially explicit and detailed information on tree species composition is critical for forest management, nature conservation and the assessment of forest ecosystem services. In many countries, forest attributes are monitored regularly through sample-based forest inventories. In combination with satellite imagery, data from such forest inventories have a great potential for developing large-area tree species maps. Here, the high temporal resolution of Sentinel-1 and Sentinel-2 has been useful for extracting vegetation phenology, information that may also be valuable for improving forest tree species mapping. The objective of this study was to map the main tree species in Germany using combined Sentinel-1 and Sentinel-2 time series, and to identify and address challenges related to the use of National Forest Inventory (NFI) data in remote sensing applications. We generated cloud free time series with 5-day intervals from Sentinel-2 imagery and combine those with monthly Sentinel-1 backscatter composites. Further, we incorporate information on topography, meteorology, and climate to account for environmental gradients. To use NFI data for training machine learning models, we address the following challenges: 1) link satellite pixels with variable radius NFI plots, for which the precise area is unknown, and 2) efficiently utilize mixed-species NFI plots for model training and validation. In the past, accuracies for pixel-level species maps were often estimated solely for homogeneous pure-species stands. In this study, we assess how well pixel-level maps generalize to mixed plot conditions. Our results show the potential of combined Sentinel-2 and Sentinel-1 time series with NFI data for tree species mapping in large, environmentally diverse landscapes. Classification accuracy in pure stands ranged between 72% and 97% (F1-score) for five dominant species, while mapping less frequent species remained challenging. When including mixed forest stands in the accuracy assessment, accuracy decreased by 4–14 percentage points for the most dominant species groups. Our study highlights the importance of including mixed-forest stands when training and validating tree species maps. Based on these results, we discuss potentials and remaining challenges for tree species mapping at the national level. Our findings allow to further improve national-level tree species mapping with medium to high resolution data and provide guidance for similar approaches in other countries where ground-based inventory data are available

Landsat time series reveal simultaneous expansion and intensification of irrigated dry season cropping in Southeastern Turkey

Long-term monitoring of the extent and intensity of irrigation systems is needed to track crop water consumption and to adapt land use to a changing climate. We mapped the expansion and changes in the intensity of irrigated dry season cropping in Turkey´s Southeastern Anatolia Project annually from 1990 to 2018 using Landsat time series. Irrigated dry season cropping covered 5,779 km² (± 479 km²) in 2018, which represents an increase of 617% over the study period. Dry season cropping was practiced on average every second year, but spatial variability was pronounced. Increases in dry season cropping frequency were observed on 40% of the studied croplands. The presented maps enable the identification of land use intensity hotspots at 30 m spatial resolution, and can thus aid in assessments of water consumption and environmental degradation. All maps are openly available for further use at https://doi.org/10.5281/zenodo.4287661.Peer Reviewe

Mapping cropland-use intensity across Europe using MODIS NDVI time series

Global agricultural production will likely need to increase in the future due to population growth, changing diets, and the rising importance of bioenergy. Intensifying already existing cropland is often considered more sustainable than converting more natural areas. Unfortunately, our understanding of cropping patterns and intensity is weak, especially at broad geographic scales. We characterized and mapped cropping systems in Europe, a region containing diverse cropping systems, using four indicators: (a) cropping frequency (number of cropped years), (b) multi-cropping (number of harvests per year), (c) fallow cycles, and (d) crop duration ratio (actual time under crops) based on the MODIS Normalized Difference Vegetation Index (NDVI) time series from 2000 to 2012. Second, we used these cropping indicators and self-organizing maps to identify typical cropping systems. The resulting six clusters correspond well with other indicators of agricultural intensity (e.g., nitrogen input, yields) and reveal substantial differences in cropping intensity across Europe. Cropping intensity was highest in Germany, Poland, and the eastern European Black Earth regions, characterized by high cropping frequency, multi-cropping and a high crop duration ratio. Contrarily, we found lowest cropping intensity in eastern Europe outside the Black Earth region, characterized by longer fallow cycles. Our approach highlights how satellite image time series can help to characterize spatial patterns in cropping intensity—information that is rarely surveyed on the ground and commonly not included in agricultural statistics: our clustering approach also shows a way forward to reduce complexity when measuring multiple indicators. The four cropping indicators we used could become part of continental-scale agricultural monitoring in order to identify target regions for sustainable intensification, where trade-offs between intensification and the environmental should be explored.Peer Reviewe

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series:Does Forest Type Matter?

Tropical environments present a unique challenge for optical time series analysis, primarily owing to fragmented data availability, persistent cloud cover and atmospheric aerosols. Additionally, little is known of whether the performance of time series change detection is affected by diverse forest types found in tropical dry regions. In this paper, we develop a methodology for mapping forest clearing in Southeast Asia using a study region characterised by heterogeneous forest types. Moderate Resolution Imaging Spectroradiometer (MODIS) time series are decomposed using Breaks For Additive Season and Trend (BFAST) and breakpoints, trend, and seasonal components are combined in a binomial probability model to distinguish between cleared and stable forest. We found that the addition of seasonality and trend information improves the change model performance compared to using breakpoints alone. We also demonstrate the value of considering forest type in disturbance mapping in comparison to the more common approach that combines all forest types into a single generalised forest class. By taking a generalised forest approach, there is less control over the error distribution in each forest type. Dry-deciduous and evergreen forests are especially sensitive to error imbalances using a generalised forest model i.e., clearances were underestimated in evergreen forest, and overestimated in dry-deciduous forest. This suggests that forest type needs to be considered in time series change mapping, especially in heterogeneous forest regions. Our approach builds towards improving large-area monitoring of forest-diverse regions such as Southeast Asia. The findings of this study should also be transferable across optical sensors and are therefore relevant for the future availability of dense time series for the tropics at higher spatial resolutions

Abschlussbericht

Im Rahmen von GreenGrass war es das Ziel, innovative Fernerkundungstechnologien zu entwickeln und zu erproben, um die futterbauliche und biotische Ausstattung der Landschaft in hoher räumlicher und zeitlicher Auflösung zu analysieren. Diese Erkenntnisse sollen als Grundlage für eine effiziente Steuerung der Nutztiere in der Landschaft und eine optimierte Weidenutzung dienen. Die Satelliten des Copernicus Programms ermöglichen mit 10-20 m Auflösung die Abbildung der kleinräumigen Heterogenität des Grünlandes. Während Sentinel-1 witterungs- und wetterunabhängig aufzeichnet (C-Band SAR), bietet die multispektrale Auflösung von Sentinel-2 einen idealen Zugang zu den spektralen Eigenschaften des Grünlandes. Die hohe raum-zeitliche Auflösung resultiert in einem Big-Data-Problem, welches effiziente und automatisierte Prozessierungsketten benötigt. Zu Beginn des Projektes wurden Fernerkundungssysteme zur Bestimmung der Biomasse und Futterqualität im Grünland nur in wenigen Studien angewandt. Wobei die Herausforderung besonders in der Verbesserung der Robustheit der Fernerkundungsverfahren zur Ableitung von Futtermasse und -qualität und der Entwicklung einer Methodik zur Beschreibung von Habitatqualität bestand. Im Rahmen des Arbeitspakets 2 - Landschaft - erfolgte die Bearbeitung von zwei Tasks. In Task 2.2 sollten verfügbare Daten der Copernicus-Satellitenkonstellation für den Untersuchungszeitraum umgesetzt werden. Die Ziele waren: 1) Entwicklung einer integrierten Vorverarbeitungskette für Sentinel-1 und Sentinel-2 Daten zur Ableitung von Phänometriken aus optischen und Radardaten. 2) Die flächendeckende Kartierung von phänologischen Veränderungen und Biomasse für die Living Labs. Und 3) die Beschreibung der Habitatqualität auf regionaler Ebene mit Fokus auf den Living Labs. In Task 2.3 wurde getestet, inwiefern UAV Daten (Task 2.1, Köln) genutzt werden können, um Modelle aus den Copernicus Daten zu verbessern. Alle geplanten Arbeiten der ersten Phase von GreenGrass wurden in angemessenem Rahmen umgesetzt und alle Meilensteine erreicht. Die getesteten und entwickelten Methoden (z.B. automatisierte Vorverarbeitungsketten, Entmischungs- und Biomassemodelle) erlauben es, die Produktivität (Biomasse, Biomassequalität) von Grünland aus Satellitendaten für alle Living-Labs abzuleiten. Die gesteigerten Prozessierungskapazitäten des in GreenGrass finanzierten Servers haben es in Kombination mit Methoden des Machine Learnings (KI) außerdem ermöglicht deutschlandweite intra-annuelle Karten zu erstellen, welche sowohl die Einschätzung der Produktivität (Biomassequalität) als auch der Phänologie und Teilaspekte der Habitatqualität (Trockenstress, Dürre-Sensitivität verschiedener EU-Habitattypen) erlauben. Für das GreenGrass-Konsortium sind diese Karten wichtige Informations-Layer, welche dazu dienen können, flächendeckend Beweidungsempfehlungen auszusprechen und zu entscheiden, wann Tiere auf welche Flächen gelenkt und von welchen Flächen sie ausgegrenzt werden sollen

Multidecadal grassland fractional cover time series retrieval for Germany from the Landsat and Sentinel-2 archives

Time series data provided by the Sentinel-2 and Landsat satellite missions offer manifold opportunities for grassland monitoring. The high intra-annual observation density of Sentinel-2 combined with the continuous long-term data record of Landsat enable analyses at seasonal, annual, and decadal scales. Fractional cover estimates of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil provide essential information to describe grassland conditions and processes. Yet, retrieving consistent grassland fractional cover time series from Landsat and Sentinel-2 imagery represents a major challenge. In this study, we implemented a multisensor spectral unmixing approach for retrieving multidecadal (i.e., 1984 to 2021) fractional cover time series of PV, NPV, and soil for Germany's permanent grasslands from the Landsat and Sentinel-2 archives. The spectral consistency of Landsat 5/7/8 and Sentinel-2A/B imagery as well as the coherency of a Sentinel-2-based spectral library to be used across Landsat and Sentinel-2 sensors served as the foundation for implementing the unmixing approach. We then employed regression-based unmixing using synthetic training data from spectral libraries for developing spatially and temporally generalized models. Applying these models to the Landsat and Sentinel-2 data facilitated multidecadal fractional cover mapping at a national-scale. We evaluated the quality of our multidecadal grassland fractional cover time series using statistical validation and linear correspondence analysis. The statistical validation was based on a multitemporal reference dataset spanning 2017 to 2021, derived from very high-resolution (VHR) imagery. Landsat 7/8- and Sentinel-2A/B-derived fractions showed similar Mean Absolute Errors (MAEs), i.e., 0.067 and 0.08 for PV, 0.149 and 0.15 for NPV, and 0.135 and 0.129 for soil. Linear correspondence analysis confirmed consistent PV and NPV fractional cover estimates among Landsat and Sentinel-2 sensors, suggesting similar errors beyond the statistical validation period. However, higher errors and weaker linear correspondence pointed to remaining uncertainties in soil fractional cover estimates. We further showed that the differences in spatial and spectral resolutions, i.e., the pixel size and the number of spectral bands, between Landsat and Sentinel-2 had a minor effect and were well mitigated by the spectral unmixing approach. We finally illustrated the value of the dense time series available for more recent years for describing seasonal trajectories of grassland conditions and land use intensities, as well as the use of the entire time series for analyzing long-term grassland dynamics based on annual fraction anomalies. Our study emphasizes the efficacy of generalized multisensor spectral unmixing approaches for retrieving consistent PV, NPV, and soil cover fractions across space, time, and sensors, providing a valuable means for grassland monitoring.Peer Reviewe