Julia Pöhlitz

Soil moisture (SM) is a key parameter for irrigation monitoring, scheduling, and supporting precision agriculture. In this study, we used Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data to estimate SM at high spatial resolution (20 m) in the Lower Chenab Canal Command (LCC) area of Punjab, Pakistan. To achieve this, we applied a semiempirical water cloud model (WCM) using both the VV and VH polarizations from SAR data. Additionally, two widely used machine learning (ML) models, random forest (RF) and support vector machines (SVM), were employed to estimate SM at the field scale. To assess model reliability and transferability, reference data from two field sites were split using two approaches: (1) stratified random sampling, with 50% of data from each site used for training and 50% for validation, and (2) spatial splitting, where one site was used solely for training and the other for validation. Results showed that with random splitting, ML models, particularly SVM, outperformed the WCM, with a coefficient of determination (R²) of 0.58, root mean square error (RMSE) of 6.78 Vol.%, and mean absolute error (MAE) of 5.71 Vol.%. Moreover, VV outperformed VH with an RMSE (R ² ) of 7.37 Vol.% (0.51), compared with 7.69 Vol.% (0.46) when a stratified random split was used. In contrast, the spatial split yielded lower accuracy, as WCM with VH polarization performed best, achieving an R ² of 0.45 and RMSE (MAE) of 7.82 (6.23) Vol.%. The high-resolution SM estimates offer practical value for optimizing irrigation management, particularly in agricultural areas challenged by limited water availability.

The measurement of available water for agricultural plants is a crucial parameter for farmers, particularly to plan irrigation. However, an area-wide measurement is often not trivial as there are several inputs and outputs of water into the system. Here, we present a high-resolution, remote sensing-based water balance model for starch potato cultivation, combining multispectral ground station data with UAV and satellite imagery. Over a three-year period (2021–2023), data from Arable Mark 2 ground stations, DJI Phantom 4 MS drones, PlanetScope satellites, and Sentinel-2 satellites were collected in Mecklenburg–Western Pomerania, Germany. The model utilizes NDVI-based crop coefficients (R² = 0.999) to estimate evapotranspiration and integrates on-farm irrigation and precipitation data for precise water balance calculations. A correlation with reference NDVI observations by Arable Mark 2 systems can be shown for UAV (R² = 0.94), PlanetScope satellite data (R² = 0.94), and Sentinel-2 satellite data (R² = 0.93). We demonstrate the model’s ability to capture intra-site heterogeneity on a precision farming scale. Our spatially comprehensive model enables farmers to optimize irrigation strategies, reducing water and energy use. Although the results are based on sprinkler irrigation, the model remains adaptable for advanced irrigation methods such as drip and subsurface systems.

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.

Soil tillage is widely acknowledged to affect soil characteristics and agricultural productivity. This research investigates the short‐term effects of various tillage methods on soil physical properties and crop yields at a Central German field site with a dry climate (mean temperature 9.5°C; annual precipitation 470 mm). Three tillage approaches were evaluated: conventional plough tillage (25 cm depth), cultivator tillage (18 cm depth), and double‐layer plough tillage (15 and 30 cm depth). We assessed soil physical properties through standard laboratory analyses, compression tests, soil pore structure via X‐ray computed tomography (X‐ray CT) and crop yields over 3 years. The results indicate that cultivator tillage approach increased soil bulk density relative to conventional tillage, especially in the second year, though this effect diminished over time. Double‐layer plough tillage emerged as a viable short‐term alternative to conventional tillage, achieving comparable soil bulk density. Saturated hydraulic conductivity values were generally higher for soils under conventional tillage or double‐layer plough tillage than for cultivator tillage, highlighting their soil loosening effect. Classical soil analysis methods combined with X‐ray computed tomography provided valuable insights into tillage induced changes to soil structure. Cultivator tillage resulted in a distinct pore structure with reduced macroporosity and pore connectivity. Despite notable soil property variations, crop yields remained consistent across the tillage methods. Overall, double‐layer plough tillage presents a sustainable option, moderately improving soil physical properties while maintaining crop yields. This study highlights the need to assess the short‐term effects of tillage on soils and contributes to the broader dialogue on optimizing tillage strategies for effective soil management and crop production.

In the view of increasing water demands in agriculture, efficient water use is a key factor in potato production. The aim of this study was to compare two deficit (80% and 90%) and one abundant (120%) gun sprinkler irrigation levels with the longtime used irrigation level of a farmer (100%). Irrigation was supplied during the 2021 growing season on a loamy sand site in Mecklenburg–Western Pomerania, Germany. Yield and tuber quality of the high-amylopectin potato (HAPP) variety “Waxy/Henriette” were assessed in a three-grade tuber size distribution. Five economic indicators were used to assess the suitability of the investigated irrigation levels to secure economic responses. Yield and starch yield did not significantly differ between the 90% (561.1 dt ha⁻¹ and 102.0 dt ha⁻¹) and the 100% irrigation levels (559.1 dt ha⁻¹ and 102.3 dt ha⁻¹), with total production increasing by 2.0 dt ha⁻¹ and starch production decreasing by 0.4 dt ha⁻¹ at the 90% irrigation level. Tuber lesions decreased the economic responses at all irrigation levels. Potentially, 87,469 m³ of irrigation water (125.8 m³ ha⁻¹) could have been saved on the loamy sand starch potato sites of the local farm (695.3 ha) in 2021.