High-frequency Earth observation (EO) data have been shown to be effective in identifying crops and monitoring their development. The purpose of this paper is to derive quantitative indicators of crop productivity using synthetic aperture radar (SAR). This study shows that the field-specific SAR time series can be used to characterise growth and maturation periods and to estimate the performance of cereals. Winter wheat fields on the Rothamsted Research farm in Harpenden (UK) were selected for the analysis during three cropping seasons (2017 to 2019). Average SAR backscatter from Sentinel-1 satellites was extracted for each field and temporal analysis was applied to the backscatter cross-polarisation ratio (VH/VV). The calculation of the different curve parameters during the growing period involves (i) fitting of two logistic curves to the dynamics of the SAR time series, which describe timing and intensity of growth and maturation, respectively; (ii) plotting the associated first and second derivative in order to assist the determination of key stages in the crop development; and (iii) exploring the correlation matrix for the derived indicators and their predictive power for yield. The results show that the day of the year of the maximum VH/VV value was negatively correlated with yield (r = −0.56), and the duration of “full” vegetation was positively correlated with yield (r = 0.61). Significant seasonal variation in the timing of peak vegetation (p = 0.042), the midpoint of growth (p = 0.037), the duration of the growing season (p = 0.039) and yield (p = 0.016) were observed and were consistent with observations of crop phenology. Further research is required to obtain a more detailed picture of the uncertainty of the presented novel methodology, as well as its validity across a wider range of agroecosystems.

Growing cover crops promotes soil health as they retain nutrients during autumn/winter and provide organic matter to the soil biota, which in turn supplies nutrients to the main crop upon mineralisation in spring. Different cover crops have varying impacts on soil biology and nutrient dynamics due to the quantity and quality of plant material returned to the soil. To understand these effects, high‐resolution data on crop responses is required. In this study, remote sensing was used to provide such data. The temporal dynamics of soil nitrogen (N) availability and N uptake in barley were studied in response to different cover crop monocultures and mixtures. This was achieved using high‐resolution multispectral images of the main crop acquired from an unmanned aerial vehicle. Alongside this, in‐situ collected plant and soil parameters were used in this 5‐year cover crop field experiment. The results showed that cover crop legacies significantly affected barley N uptake, biomass, and canopy N content. In early June, at peak canopy N, the highest values were observed in barley grown after vetch‐radish or oat‐radish mixtures (84 kg N/ha) and the lowest in barley grown after fallow (63 kg N) or oat (53 kg N/ha on 23rd of June). At the start of the barley growing season, soil microbial biomass was not significantly affected by the cover crop legacies. However, differential N mineralisation between cover crop legacies can be attributed to differences in microbial activity associated with cover crop quantity and quality. This research demonstrates the potential of remote sensing to monitor and understand temporal and spatial variation of crop canopy N in response to cover crop N mineralisation by the soil biota which is an important component of soil health. This approach can contribute to more efficient N use by enabling fine‐tuning of the type, quantity, timing, and location of fertilisation.

Soil fertility depletion, spatially variable nutrient limitations, and increasing climate variability are major constraints to crop productivity and resource conservation in Sub‑Saharan Africa (SSA). Sustainable nutrient management strategies that simultaneously improve crop yields, enhance soil health, and reduce nutrient losses are urgently needed to strengthen agricultural resilience. This study presents an integrated modelling framework for quantifying gaps in nitrogen (N), phosphorus (P), and potassium (K) fertilization under water‑limited conditions. The aim is to support site‑specific nutrient recommendations that improve fertilizer use efficiency while contributing to soil and water conservation.The framework combines three complementary models. INITIATOR simulates soil nutrient stocks and flows, providing a spatially explicit assessment of nutrient availability and potential depletion risks. WOFOST estimates water‑limited maize yield potential, capturing the influence of rainfall variability and soil moisture dynamics on attainable production. QUEFTS then models nutrient uptake and yield responses, enabling calculation of nutrient requirements that match crop demand without excess application. Together, these components enable a holistic evaluation of nutrient management in soil–crop–water systems on different management levels.The approach was applied to Uganda, a country characterized by strong agro‑ecological diversity, high interannual rainfall variability, and widespread soil degradation. Using 1 km resolution SoilGrids data and climate records from 2001–2024, spatially explicit water‑limited yield potentials were simulated for maize across the country. Fertilizer requirements were calculated based on median attainable yields for the period . Results reveal substantial geographic variation in nutrient availability, nutrient use efficiency, and fertilizer needs. In many areas, low soil nutrient stocks combined with high potential yield estimates lead to large fertilization gaps, while in other regions the gap is smaller due to relatively higher soil fertility or lower potential yields due to moisture constraints.These findings demonstrate that uniform fertilizer recommendations are unlikely to be effective under the diverse soil and climatic conditions of SSA. The proposed modelling framework provides a robust decision‑support tool for developing precision nutrient management strategies that align nutrient inputs with both crop demand and water availability. Such targeted approaches can help reduce nutrient losses to the environment, maintain soil fertility, and support sustainable intensification efforts across smallholder farming systems. This work highlights the potential of integrated soil–crop–climate modelling to guide context‑appropriate nutrient management solutions that enhance productivity while promoting soil and water conservation.

The necessity of soil health monitoring is paramount in reversing soil degradation and promoting sustainable farming. Including cover crops in the crop rotation is one of the sustainable soil management practices contributing to soil health. Cover crops contribute to soil health by nutrient retention and carbon accumulation during their growth and return of organic matter to the soil upon their incorporation. During monitoring, the sampling frequency can change from annual in the case of SOC to weekly or daily for fertilization and irrigation. Remote sensing techniques offer a solution, enabling the monitoring of vegetation over time and space, thereby enhancing our understanding of the impact of cover crops on the main crop. However, this technology makes it possible to see the surface of the field which can assist with the above-ground changes of the system. Process-based modelling and data assimilation can subsequently link the above-ground component with soil functions. In-situ data collection that includes crop characteristics such as biomass and N-uptake is essential both for transforming remote sensing data into crop characteristics and for calibrating models. Using Unmanned Aerial Vehicles (UAVs) can potentially collect data at high frequency, which can be used to enhance soil process modelling. The development of this UAV-based method has the potential to be scaled up to a satellite level in the future.In our research, we have combined the study of nutrient cycling and the effect of cover crops on soil health. To achieve this, we have used the WOFOST-SWAP-ANIMO model to simulate the varying influence of cover crop monocultures and mixtures on Soil Organic Carbon (SOC) and Nitrogen cycling in a 7-year crop rotation on sandy soil. The model simulates vegetation characteristics such as biomass, leaf area index, and yield, as well as soil moisture and mineral Nitrogen concentrations. This will give us a good estimation of the vegetation input into the soil as well as the nutrient uptake from both cover crops and main crops. Soil sampling is also important to model calibration/validation to be able to simulate the N dynamics of biological activity under the surface. Our findings suggest that the model, in conjunction with UAV data and field sensors, can effectively monitor soil health indicators crucial for field management practice selection, such as the Carbon cycle and Nitrogen use efficiency.