This study explores the potential of Sentinel-1 Synthetic Aperture Radar (SAR) to identify phenological phases of wheat, sugar beet, and canola. Breakpoint and extreme value analyses were applied to a dense time series of interferometric (InSAR) and polarimetric (PolSAR) features recorded during the growing season of 2017 at the JECAM site DEMMIN (Germany). The analyses of breakpoints and extrema allowed for the distinction of vegetative and reproductive stages for wheat and canola. Certain phenological stages, measured in situ using the BBCH-scale, such as leaf development and rosette growth of sugar beet or stem elongation and ripening of wheat, were detectable by a combination of InSAR coherence, polarimetric Alpha and Entropy, and backscatter (VV/VH). Except for some fringe cases, the temporal difference between in situ observations and breakpoints or extrema ranged from zero to five days. Backscatter produced the signature that generated the most breakpoints and extrema. However, certain micro stadia, such as leaf development of BBCH 10 of sugar beet or flowering BBCH 69 of wheat, were only identifiable by the InSAR coherence and Alpha. Hence, it is concluded that combining PolSAR and InSAR features increases the number of detectable phenological events in the phenological cycles of crops.

Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths (T), requires root zone depth optimization (T_opt) and is limited in use due to its low spatial resolution. To estimate RZSM at 100-m resolution, we integrate the depth specificity of SWI and employed random forest (RF) downscaling. Topographic synthetic aperture radar (SAR) and optical datasets were utilized to develop three RF models (RF1: SAR, RF2: optical, RF3: SAR + optical). At the DEMMIN experimental site in northeastern Germany, T_opt (in days) varies from 20 to 60 for depths of 10 to 30 cm, increasing to 100 for 40–60 cm. RF3 outperformed other models with 1 km test data. Following residual correction, all high-resolution predictions exhibited strong spatial accuracy (R ≥ 0.94). Both products (1 km and 100 m) agreed well with observed RZSM during summer but overestimated in winter. Mean R between observed RZSM and 1 km (100 m; RF1, RF2, and RF3) SWI ranges from 0.74 (0.67, 0.76, and 0.68) to 0.90 (0.88, 0.81, and 0.82), with the lowest and highest R achieved at 10 cm and 30 cm depths, respectively. The average RMSE using 1 km (100 m; RF1, RF2, and RF3) SWI increased from 2.20 Vol.% (2.28, 2.28, and 2.35) at 30 cm to 3.40 Vol.% (3.50, 3.70, and 3.60) at 60 cm. These negligible accuracy differences underpin the potential of the proposed method to estimate RZSM for precise local applications, e.g., irrigation management.

Spatial information about plant health and productivity are essential when assessing the progress towards Sustainable Development Goals such as life on land and zero hunger. Plant health and productivity are strongly linked to a plant’s phenological progress. Remote sensing, and since the launch of Sentinel-1 (S1), specifically, radar-based frameworks have been studied for the purpose of monitoring phenological development. This study produces insights into how crop phenology shapes S1 signatures of PolSAR features and InSAR coherence of wheat, canola, sugar beet. and potato across multiple years and orbits. Hereby, differently smoothed time series and a base line of growing degree days are stacked to estimate the patterns of occurrence of extreme values and break points. These patterns are then linked to in situ observations of phenological developments. The comparison of patterns across multiple orbits and years reveals that a single optimized fit hampers the tracking capacities of an entire season monitoring framework, as does the sole reliance on extreme values. VV and VH backscatter intensities outperform all other features, but certain combinations of phenological stage and crop type are better covered by a complementary set of PolSAR features and coherence. With regard to PolSAR features, alpha and entropy can be replaced by the cross-polarization ratio for tracking certain stages. Moreover, a range of moderate incidence angles is better suited for monitoring crop phenology. Also, wheat and canola are favored by a late afternoon overpass. In sum, this study provides insights into phenological developments at the landscape level that can be of further use when investigating spatial and temporal variations within the landscape.

Climate change and increasing weather and seasonal dynamics challenge agricultural landscapes. To cope with this challenge information on crop performance is key. This study presents a novel framework for bridging landscape-scale vegetation dynamics with field-level crop phenology using Sentinel-1 radar time series. Unlike previous approaches that focus on local algorithm optimisation or SAR feature selection, this work integrates two scales: (1) landscape patterns derived from annual distributions of time series metrics (TSMs) and (2) field-level phenology, both linked to growing degree days (GDD). TSMs were generated through breakpoint analyses over different smoothing intensities for Sentinel-1 polarisation (PolSAR) and interferometric coherence (InSAR) features, capturing crop, orbit and sensor-specific responses. The framework quantifies uncertainties inherent in both remote sensing and ground observations, and evaluates trackable progress (phenological stage detectability) and tracking range (GDD variance around stages) to assess accuracy under variable acquisition geometries, weather and smoothing parameters. Applied to the DEMMIN site (Germany), the analysis revealed consistent TSM-GDD relationships for wheat, rape, and sugar beet, with descriptors such as soil fertility and water availability explaining spatial patterns (R² ≈ 0.8). Key novelties include the identification of low tracking ranges in drought years, the demonstration of the impact of orbit-specific incidence angles on monitoring fidelity, and the highlighting of Sentinel-1’s ability to resolve phenological variance across fragmented landscapes. By harmonising multi-scale SAR time series with agro-meteorological data, this approach advances transferable methods for operational crop monitoring, supporting precision agriculture and regional yield assessment beyond localised models.

Crop rotation analysis is fundamental to understanding agricultural landscape dynamics, yet the complexity of multi-annual, spatially explicit agricultural data presents significant analytical challenges. A key difficulty lies in tracking field parcels whose boundaries change over time through administrative splits, mergers, or reorganizations. This process requires robust spatial intersection approaches that can reconcile multi-temporal polygon geometries. Here, we present CropRotationViz, an open-source R package that provides a detailed framework for processing, analyzing, and visualizing crop rotation patterns across multiple spatial and temporal scales. The package implements a modular architecture consisting of (i) a spatial processing pipeline with automated data validation, crop nomenclature standardization through a customizable crop classification aggregation tool, and geometric handling of temporal field boundary changes through multi-year polygon intersection, (ii) interactive multi-annual flow and diversity visualizations, including Sankey diagram and structural diversity mapping, and environmental context integration, and (iii) three distinct visualization interfaces optimized for different analytical needs: a dynamic rendering mode for detailed exploration, a pre-rendered fast visualization mode for rapid regional comparisons, and a field-level interface for individual parcel inspection. The framework bridges complex geospatial data processing and user-friendly visualization through a dual-access architecture offering both graphical user interfaces and a programmatic API, making advanced crop rotation analysis accessible to researchers and practitioners across disciplines. Privacy-preserving spatial aggregation to administrative or hydrological boundaries enables analysis of sensitive field-level data while producing outputs suitable for stakeholder distribution. The framework is validated using 7 years of field-level crop data from two German federal states, encompassing approximately 2.8 million hectares and over 200,000 parcels annually, processed in under 1 hour on standard hardware. Applied to these data, the framework reveals that soil potential alone explains 61% of the variance in structural crop rotation diversity, highlighting the decisive role of edaphic constraints in shaping agricultural management.