Drought is one of the natural hazards that have negatively affected the agricultural sector worldwide. The aims of this study were to track drought characteristics (duration (DD), severity (DS), and frequency (DF)) in South Africa between 2002 and 2021 and to evaluate its impact on wheat production. Climate data were collected from the South African Weather Service (SAWS) along with wheat yield data from the Department of Agriculture, Forestry and Fisheries (2002–2021). The standard precipitation index (SPI) was calculated on 3-, 6-, 9-, and 12-month time scales, and the trend was then tracked using the Mann–Kendall (MK) test. To signify the climatic effects on crop yield, the standardized yield residual series (SYRS) was computed along with the crop-drought resilience factor (CR) on a provincial scale (2002–2021). The output of the SPI analysis for 32 stations covering all of South Africa indicates a drought tendency across the country. On a regional scale, western coastal provinces (WES-C and NR-C) have been more vulnerable to meteorological droughts over the past 20 years. Positive correlation results between SYRS and wheat yield indicate that the WES-C province was highly influenced by drought during all stages of wheat growth (Apr–Nov). Historical drought spells in 2003, 2009, and 2010 with low CR = 0.64 caused the province to be highly impacted by the negative impacts of droughts on yield loss. Overall, drought events have historically impacted the western part of the country and dominated in the coastal area. Thus, mitigation plans should be commenced, and priority should be given to this region. These findings can assist policymakers in budgeting for irrigation demand in rainfed agricultural regions.

<p>Accurate geographical and temporal information is provided in large part by remote sensing. Advanced crop protection plans can be created by gathering and analysing data at various scales and resolutions to create emergency models, identification patterns, and site mapping. Recent developments in remote sensing enable the analysis and diagnosis of crop problems based on reflectance data through visible, multispectral, or hyperspectral detection utilizing very high-resolution satellites.</p> <p>The strenuous physical removal of weed species based on field scouting is one management technique. The optimization method based on remote sensing predictions, fed by meteorological data, but also using vegetation information from several high-resolution remote sensing products and spectral data from different sensor types, combining them by data assimilation, is a novel aspect of the research. This method is used to optimize accurate weed detection and reliable discrimination between weeds and crop plants. By examining the spatial and spectral properties of the agricultural field, I will analyse the function of LIDAR and other time series remote sensing data in the field scouting (partly based on field surveys at the Hungarian case study site). The findings will establish a link between water, energy, and food production in agriculture and serve as the foundation for the creation of practical strategies for gathering data on target areas and making spatially selective weed control decisions.</p>