Demand for agricultural products is increasing as population continues to grow in Africa. To attain a higher crop yield while preserving the environment, appropriate management of macronutrients (i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops are of critical prominence. This paper aims to review the state of art of the use of remote sensing in soil agricultural applications, especially in monitoring NPK availability for widely grown crops in Africa. In this study, we conducted a substantial literature review of the use of airborne imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing spectral information, and advances of these applications in farming practices by the African scientific community. Here we aimed to identify knowledge gaps in this field and challenges related to the acquisition, processing, and analysis of hyperspectral imagery for soil agriculture investigations. To do so, publications over the past 10 years (i.e., 2008–2021) in hyperspectral imaging technology and applications in monitoring macronutrients status for crops were reviewed. In this study, the imaging platforms and sensors, as well as the different methods of processing encountered across the literature, were investigated and their benefit for NPK assessment were highlighted. Furthermore, we identified and selected particular spectral regions, bands, or features that are most sensitive to describe NPK content (both in crop and soil) that allowed to characterize NPK. In this review, we proposed a hyperspectral data-based research protocol to quantify variability of NPK in soil and crop at the field scale for the sake of optimizing fertilizers application. We believe that this review will contribute promoting the adoption of hyperspectral technology (i.e., imaging and spectroscopy) for the optimization of soil NPK investigation, mapping, and monitoring in many African countries.

Africa has the largest population growth rate in the world and an agricultural system characterized by the predominance of smallholder farmers. Improving food security in Africa will require a good understanding of farming systems yields as well as reducing yield gaps (i.e., the difference between potential yield and actual farmer yield). To this end, crop yield gap practices in African countries need to be understood to fill this gap while decreasing the environmental impacts of agricultural systems. For instance, the variability of yields has been demonstrated to be strongly controlled by soil fertilizer use, irrigation management, soil attribute, and the climate. Consequently, the quantitative assessment and mapping information of soil attributes such as nitrogen (N), phosphorus (P), potassium (K), soil organic carbon (SOC), moisture content (MC), and soil texture (i.e., clay, sand and silt contents) on the ground are essential to potentially reducing the yield gap. However, to assess, measure, and monitor these soil yield-related parameters in the field, there is a need for rapid, accurate, and inexpensive methods. Recent advances in remote sensing technologies and high computational performances offer a unique opportunity to implement cost-effective spatiotemporal methods for estimating crop yield with important levels of scalability. However, researchers and scientists in Africa are not taking advantage of the opportunity of increasingly available geospatial remote sensing technologies and data for yield studies. The objectives of this report are to (i) conduct a review of scientific literature on the current status of African yield gap analysis research and their variation in regard to soil properties management by using remote sensing techniques; (ii) review and describe optimal yield practices in Africa; and (iii) identify gaps and limitations to higher yields in African smallholder farms and propose possible improvements. Our literature reviewed 80 publications and covered a period of 22 years (1998-2020) over many selected African countries with a potential yield improvement. Our results found that (i) the number of agriculture yield-focused remote sensing studies has gradually increased, with the largest proportion of studies published during the last 15 years; (ii) most studies were conducted exclusively using multispectral Landsat and Sentinel sensors; and (iii) over the past decade, hyperspectral imagery has contributed to a better understanding of yield gap analysis compared to multispectral imagery; (iv) soil nutrients (i.e., NPK) are not the main factor influencing the studied crop productivity in Africa, whereas clay, SOC, and soil pH were the most examined soil properties in prior papers.

Total nitrogen (TN) is a critical nutrient for plant growth, and its monitoring in agricultural soil is vital for farm managers. Traditional methods of estimating soil TN levels involve laborious and costly chemical analyses, especially when applied to large areas with multiple sampling points. Remote sensing offers a promising alternative for identifying, tracking, and mapping soil TN levels at various scales, including the field, landscape, and regional levels. Spaceborne hyperspectral sensing has shown effectiveness in reflecting soil TN levels. This study evaluates the efficiency of spectral reflectance at visible near-infrared (VNIR) and shortwave near-infrared (SWIR) regions to identify the most informative hyperspectral bands responding to the TN content in agricultural soil. In this context, we used PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral imagery with ensemble learning modeling to identify N-specific absorption features. This ensemble consisted of three multivariate regression techniques, partial least square (PLSR), support vector regression (SVR), and Gaussian process regression (GPR) learners. The soil TN data (n = 803) were analyzed against a hyperspectral PRISMA imagery to perform spectral band selection. The 803 sampled data points were derived from open-access soil property and nutrient maps for Africa at a 30 m resolution over a bare agricultural field in southern Morocco. The ensemble learning strategy identified several bands in the SWIR in the regions of 900–1300 nm and 1900–2200 nm. The models achieved coefficient-of-determination values ranging from 0.63 to 0.73 and root-mean-square error values of 0.14 g/kg for PLSR, 0.11 g/kg for SVR, and 0.12 g/kg for GPR, which had been boosted to an R² of 0.84, an RMSE of 0.08 g/kg, and an RPD of 2.53 by the ensemble, demonstrating the model’s accuracy in predicting the soil TN content. These results underscore the potential for using spaceborne hyperspectral imagery for soil TN estimation, enabling the development of decision-support tools for variable-rate fertilization and advancing our understanding of soil spectral responses for improved soil management.

Soil organic carbon (SOC), pH and calcium carbonate content have an important role in the availability of nutrients for plants. Estimation of soil pH, calcium carbonate equivalent (CCE) and SOC (in the case of low variation) using satellite multi and hyperspectral data is still a challenging issue. Hyperspectral data acquired by new-generation spaceborne imagers like PRISMA and EnMAP offer new opportunities to accurately quantify soil properties. In this research the capability of Gaussian Process Regression (GPR) algorithm for SOC, pH and CCE retrieval from different pre-treated PRISMA spectra has been evaluated. To cover a wide topsoil variability, three different study areas in Italy were selected: Jolanda di Savoia (Lat. 44.87°N, Lon. 11.97°E), Maccarese (Lat. 41.87°N, Lon. 12.22°E) and Pignola (Lat. 40.56°N, Lon. 5.76°E). Soil samples were collected according to a 30 m squares elementary sample unit scheme and the SOC (n=635, min =0.19%, max=6.4%, std=1.55), CCE (n=518, min=0%, max=15.1, std=4.614) and the pH (n=460, min=5.035, max=8.075, std=0.769) was measured. The pH values of the samples show a -0.57 and 0.55 correlation with SOC and CCE, respectively. An overall total of 46 clear sky PRISMA images, acquired between 2019 and 2023, were used for this study. The L2D images were co-registered by the AROSICS algorithm which uses the Sentinel-2 image acquired at the closest date, to assure the co-registration (of about 0.5pixel of RMS). Noisy spectral bands and those affected by atmospheric water absorption in PRISMA images were removed, leaving a total of 173 spectral bands. The spectra were smoothed using a Savitzky-Golay filter (SG) with a second-order polynomial and a filter length of 7. To minimize the impact of the soil moisture (SM) effects, the spectra of 198 soil samples, at different SM levels, were acquired in our laboratory using a FieldSpec 4 spectroradiometer and then resampled to the PRISMA bands to be used for developing the external parameter orthogonalization (EPO) of the reflectance. A Principal Component Analysis (PCA) was also applied on the pre-treatments of the reflectance dataset (i.e., reflectance, first derivative reflectance, and EPO-projected reflectance). The first 10 PCs were selected and used for training the GPR Machine Learning (ML) models. A k-fold (k=10) cross-validation method was applied for SOC, pH and CCE modelling. The results indicate that optimal performance is achieved for SOC (R2=0.84, RMSE=0.618%) and CCE (R2=0.70, RMSE=2.527%) by employing the first derivative of EPO-projected reflectance. In the case of pH, the use of reflectance yields the most favorable outcomes (R2=0.72, RMSE=0.411). Improving the accuracy in estimating the SOC, pH and CCE soil properties, which are critical components of productive soils, is very important to allow for an efficient allocation of resources, agricultural management, and the maintenance of fertile soils for an optimal crop growth and many other purposes. Future work will include a much wider range of soil types in different soil moisture conditions.