Precise adjustments of farm management activities, such as irrigation and soil treatment according to site-specific conditions, are crucial. With advances in smart agriculture and sensors, it is possible to reduce the cost of water and soil treatment inputs but still realize optimal yields and high-profit returns. However, achieving precise application requirements cannot be efficiently practiced with spatially disjointed information. This study assessed the potential of using an electromagnetic induction device (EM38-MK) to cover this gap. An EM38-MK was used to measure soil apparent electrical conductivity (ECa) as a covariate to determine soil salinity status and soil water content θ post irrigation at four depth layers (Hz: 0–0.25 m; Hz: 0–0.75 m; Vz: 0.50–1 m). The inverse distance weighting method was used to generate the spatial distribution thematic layers of electrical conductivity. The statistical measures showed an R² = 0.87; r > 0.7 and p ≤ 0.05 on correlation of ECa and SWC. Based on the South African salinity class of soils, the area was not saline ECa < 200 mS/m. The EM38-MK can be used to estimate soil salinity and SWC variability using ECa as a proxy, allowing precise estimations with depths and in space. These findings provide key information that can aid in irrigation scheduling and soil management.

Remote sensing data play a crucial role in precision agriculture and natural resource monitoring. The use of unmanned aerial vehicles (UAVs) can provide solutions to challenges faced by farmers and natural resource managers due to its high spatial resolution and flexibility compared to satellite remote sensing. This paper presents UAV and spectral datasets collected from different provinces in South Africa, covering different crops at the farm level as well as natural resources. UAV datasets consist of five multispectral bands corrected for atmospheric effects using the PIX4D mapper software to produce surface reflectance images. The spectral datasets are filtered using a Savitzky–Golay filter, corrected for Multiplicative Scatter Correction (MSC). The first and second derivatives and the Continuous Wavelet Transform (CWT) spectra are also calculated. These datasets can provide baseline information for developing solutions for precision agriculture and natural resource challenges. For example, UAV and spectral data of different crop fields captured at spatial and temporal resolutions can contribute towards calibrating satellite images, thus improving the accuracy of the derived satellite products.

Monitoring crop growth conditions during the growing season provides information on available soil nutrients and crop health status, which are important for agricultural management practices. Crop growth frequently varies due to site-specific climate and farm management practices. These variations might arise from sub-field-scale heterogeneities in soil composition, moisture levels, sunlight, and diseases. Therefore, soil properties and crop biophysical data are useful to predict field-scale crop development. This study investigates soil data and spectral indices derived from multispectral Unmanned Aerial Vehicle (UAV) imagery to predict crop height at two winter wheat farms. The datasets were investigated using Gaussian Process Regression (GPR), Ensemble Regression (ER), Decision tree (DT), and Support Vector Machine (SVM) machine learning regression algorithms. The findings showed that GPR (R² = 0.69 to 0.74, RMSE = 15.95 to 17.91 cm) has superior accuracy in all models when using vegetation indices (VIs) to predict crop growth for both wheat farms. Furthermore, the variable importance generated using the GRP model showed that the RedEdge Normalized Difference Vegetation Index (RENDVI) had the most influence in predicting wheat crop height compared to the other predictor variables. The clay, calcium (Ca), magnesium (Mg), and potassium (K) soil properties have a moderate positive correlation with crop height. The findings from this study showed that the integration of vegetation indices and soil properties predicts crop height accurately. However, using the vegetation indices independently was more accurate at predicting crop height. The outcomes from this study are beneficial for improving agronomic management within the season based on crop height trends. Hence, farmers can focus on using cost-effective VIs for monitoring particular areas experiencing crop stress.

Monitoring crop height during different growth stages provides farmers with valuable information important for managing and improving expected yields. The use of synthetic aperture radar Sentinel-1 (S-1) and Optical Sentinel-2 (S-2) satellites provides useful datasets that can assist in monitoring crop development. However, studies exploring synergetic use of SAR S-1 and optical S-2 satellite data for monitoring crop biophysical parameters are limited. We utilized a time-series of monthly S-1 satellite data independently and then used S-1 and S-2 satellite data synergistically to model wheat-crop height in this study. The polarization backscatter bands, S-1 polarization indices, and S-2 spectral indices were computed from the datasets. Optimized Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), Decision Tree Regression (DTR), and Neural Network Regression (NNR) machine-learning algorithms were applied. The findings show that RFR (R² = 0.56, RMSE = 21.01 cm) and SVM (R² = 0.58, RMSE = 20.41 cm) produce a low modeling accuracy for crop height estimation with S-1 SAR data. The S-1 and S-2 satellite data fusion experiment had an improvement in accuracy with the RFR (R² = 0.93 and RMSE = 8.53 cm) model outperforming the SVM (R² = 0.91 and RMSE = 9.20 cm) and other models. Normalized polarization (Pol) and the radar vegetation index (RVI_S1) were important predictor variables for crop height retrieval compared to other variables with S-1 and S-2 data fusion as input features. The SAR ratio index (SAR RI 2) had a strong positive and significant correlation (r = 0.94; p < 0.05) with crop height amongst the predictor variables. The spatial distribution maps generated in this study show the viability of data fusion to produce accurate crop height variability maps with machine-learning algorithms. These results demonstrate that both RFR and SVM can be used to quantify crop height during the growing stages. Furthermore, findings show that data fusion improves model performance significantly. The framework from this study can be used as a tool to retrieve other wheat biophysical variables and support decision making for different crops.

Accurate estimation of crop water use, which is expressed as evapotranspiration (ET) is an important task for effective irrigation and agricultural water management. Although direct field measurement of actual evapotranspiration (ETa) is the most reliable method, practical and economic limitations often make it difficult to acquire, especially in developing countries. Consequently, crop evapotranspiration (ETc) is calculated using reference evapotranspiration (ETo) and crop-specific coefficients (Kc) to support irrigation water management practices. Several ETo models have been developed to address varying environmental conditions; however, their transferability to new environments often leads to under or over estimation of ETo, which has an impact on ETc estimation. This study evaluated the accuracy of 30 ETo micrometeorological models to estimate ETc under different seasonal and micro-climatic conditions using ETa data directly measured using a smart field weighing lysimeter as a benchmark. Local Kc values were derived from field-based measurements, while statistical metrics were applied for the evaluation process. A cumulative ranking approach was used to assess the accuracy and consistency of the models across four cropping seasons. Results demonstrated the Penman–Monteith model to be the most consistent model in estimating ETc, which outperformed other models across all cropping seasons. The performance of alternative models differed significantly with seasonal conditions, indicating their susceptibility to seasonality. The findings demonstrated the Penman–Monteith model as the most reliable approach for estimating ETc, which justifies its application role as a benchmark for validating other ETo models in data-limited areas. The study emphasizes the importance of site-specific validation and calibration of ETo models to improve their accuracy, applicability, and reliability in diverse environmental conditions.