Monitoring agricultural systems becomes increasingly important in the context of global challenges like climate change, biodiversity loss, population growth, and rising demand for products. High-resolution, national-scale maps land are needed to develop strategies future sustainable agriculture. However, characterization cover over large areas multiple years remains challenging due locally diverse temporally variable characteristics cultivated land. We here propose a workflow generating...

The EnMAP-Box is a toolbox that developed for the processing and analysis of data acquired by German spaceborne imaging spectrometer EnMAP (Environmental Mapping Analysis Program). It with two aims in mind order to guarantee full usage future data, i.e., (1) extending user community (2) providing access recent approaches spectroscopy processing. software freely available offers range tools applications spectral imagery, including classical as well powerful machine learning or interfaces...

Spatially explicit knowledge on grassland extent and management is critical to understand monitor the impact of use intensity ecosystem services biodiversity. While regional studies allow detailed insights into land service interactions, information a national scale can aid biodiversity assessments. However, for most European countries this not yet widely available. We used an analysis-ready-data cube that contains dense time series co-registered Sentinel-2 Landsat 8 data, covering Germany....

The paradox between environmental conservation and economic development is a challenge for Brazil, where there complex dynamic agricultural scenario. This reinforces the need effective methods detailed mapping of agriculture. In this work, we employed land surface phenological metrics derived from dense satellite image time series to classify in Cerrado biome. We used all available Landsat images April 2013 2017, applying weighted ensemble Radial Basis Function (RBF) convolution filters as...

Information on crop phenology is essential when aiming to better understand the impacts of climate and change, management practices, environmental conditions agricultural production. Today's novel optical radar satellite data with increasing spatial temporal resolution provide great opportunities derive such information. However, so far, we largely lack methods that leverage this detailed information at field level. We here propose a method based dense time series from Sentinel-1,...

The intensity of land use and management in permanent grasslands affects both biodiversity important ecosystem services. Comprehensive knowledge about these intensities is a crucial factor for sustainable decision-making landscape policy. For meadows, the can be described by proxies such as mowing frequency, usually, higher number cuts indicate intensities. Dense time series medium resolution (10–30 m) remote sensing data are suitable detection events. However, existing studies revealed...

Abstract Agricultural intensification has simplified landscape composition and configuration, which led to biodiversity declines. Increasing landscape‐wide crop heterogeneity can promote farmland biodiversity. However, knowledge is still lacking on how the effects of configurational compositional (i.e. field size diversity) are modulated by amount semi‐natural habitats in landscape, especially across large scales. We tested mean functional diversity affect bird abundance over three...

Anthropogenic interventions in natural and semi-natural ecosystems often lead to substantial changes their functioning may ultimately threaten ecosystem service provision. It is, therefore, necessary monitor these order understand impacts support management decisions that help ensuring sustainability. Remote sensing has proven be a valuable tool for purposes, especially hyperspectral sensors are expected provide data quantitative characterization of land change processes. In this study,...

Abstract Grassland plays an important role in German agriculture. The interplay of ecological processes grasslands secures ecosystem functions and, thus, ultimately contributes to essential services. To sustain, e.g., the provision fodder or filter function soils, agricultural management needs adapt site-specific grassland characteristics. Spatially explicit information derived from remote sensing data has been proven instrumental for achieving this. In this study, we analyze potential...

Abstract Land‐use intensification in grassland ecosystems (i.e. increased mowing frequency, intensified grazing) has a strong negative effect on biodiversity and ecosystem services. However, accurate information grassland‐use intensity is difficult to acquire restricted the local or regional level. Recent studies have shown that events can be mapped for large areas using satellite image time series. The transferability of such approaches, especially mountain areas, been little explored,...

Remote sensing has become a valuable tool in monitoring arctic environments. The aim of this paper is ground-based hyperspectral characterization Low Arctic Alaskan tundra communities along four environmental gradients (regional climate, soil pH, toposequence, and moisture) that all vary ground cover, biomass, dominating plant communities. Field spectroscopy connection with vegetation analysis was carried out summer 2012, the North American Transect (NAAT). Spectral metrics were extracted,...

Abstract In times of rapid global change, ecosystem monitoring is utmost importance. Combined field and remote sensing data enable large‐scale assessments, while maintaining local relevance accuracy. heterogeneous landscapes, however, the integration field‐collected with image pixels not a trivial matter. Indeed, much uncertainty in models that use to map larger areas lies on integration. this study, we propose fine spatial resolution (5 × 5 m 2 ) as auxiliary for upscaling field‐sampled...

The quantification and spatially explicit mapping of carbon stocks in terrestrial ecosystems is important to better understand the global cycle monitor report change processes, especially context international policy mechanisms such as REDD+ or implementation Nationally Determined Contributions (NDCs) UN Sustainable Development Goals (SDGs). Especially heterogeneous ecosystems, Savannas, accurate quantifications are still lacking, where highly variable vegetation densities occur a strong...

Abstract Effective monitoring of agricultural lands requires accurate spatial information about the locations and boundaries fields. Through satellite imagery, such can be mapped on a large scale at high temporal frequency. Various methods exist in literature for segmenting fields from images. Edge-based, region-based, or hybrid segmentation are traditional that have widely been used Lately, use deep neural networks (DNNs) various tasks remote sensing has gaining traction. Therefore, to...

Detailed maps on the spatial and temporal distribution of crops are key for a better understanding agricultural practices food security management. Multi-temporal remote sensing data deep learning (DL) have been extensively studied deriving accurate crop maps. However, strategies to solve problem transferring classification models over time, e.g., training model with recent year mapping back past, not fully explored. This is due lack generalized method aggregating optical regard irregularity...

In times of global environmental change, the sustainability human–environment systems is only possible through a better understanding ecosystem processes. An assessment anthropogenic impacts depends upon monitoring natural ecosystems. These are intrinsically complex and dynamic, characterized by ecological gradients. Remote sensing data repeatedly collected in systematic manner suitable for describing such gradual changes over time landscape gradients, e.g., information on vegetation’s...

Monitoring natural ecosystems and ecosystem transitions is crucial for a better understanding of land change processes. By providing synoptic views in space time, remote sensing data have proven to be valuable sources such purposes. With the forthcoming Environmental Mapping Analysis Program (EnMAP), frequent area-wide mapping environments by means high quality hyperspectral becomes possible. However, amplified spectral mixing due sensor’s ground sampling distance 30 m on one hand patterns...

Summary Spatial patterns of community composition turnover (beta diversity) may be mapped through generalised dissimilarity modelling ( GDM ). While remote sensing data are adequate to describe these patterns, the often high‐dimensional nature poses some analytical challenges, potentially resulting in loss generality. This hinder use such for mapping and monitoring beta‐diversity patterns. study presents Sparse Generalised Dissimilarity Modelling SGDM ), a methodological framework designed...

The Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) considers agricultural fields as one of the essential variables that can be derived from satellite data. We evaluated accuracy at which delineated Sentinel-1 (S1) and Sentinel-2 (S2) images in different landscapes throughout growing season. used supervised segmentation based multiresolution (MRS) algorithm to first identify optimal feature set S1 S2 for field delineation. Based this set, we analyzed with...

Global biodiversity change creates a need for standardized monitoring methods. Modelling and mapping spatial patterns of community composition using high-dimensional remotely sensed data requires adapted methods adequate to such datasets. Sparse generalized dissimilarity modelling is designed deal with high dimensional datasets, as time series or hyperspectral remote sensing data. In this manuscript we present sgdm, an R package performing sparse (SGDM). The includes some general tools that...

Abstract. The Cerrado biome in Brazil covers approximately 24% of the country. It is one richest and most diverse savannas world, with 23 vegetation types (physiognomies) consisting mostly tropical savannas, grasslands, forests dry forests. considered as global hotspots biodiversity because high level endemism rapid loss its original habitat. This work aims to analyze potential Landsat Analysis Ready Data (ARD) combination different environmental data classify two hierarchical levels. Here...

Long-term monitoring of the extent and intensity irrigation systems is needed to track crop water consumption adapt land use a changing climate. We mapped expansion changes in irrigated dry season cropping Turkey´s Southeastern Anatolia Project annually from 1990 2018 using Landsat time series. Irrigated covered 5,779 km² (± 479 km²) 2018, which represents an increase 617% over study period. Dry was practiced on average every second year, but spatial variability pronounced. Increases...