Delineating smallholder maize farms from Sentinel-1 coupled with Sentinel-2 data using machine learning

Rural communities rely on smallholder maize farms for subsistence agriculture, the main driver of local economic activity and food security. However, their planted area estimates are unknown in most developing countries. This study explores the use of Sentinel-1 and Sentinel-2 data to map smallholder maize farms. The random forest (RF), support vector (SVM) machine learning algorithms and model stacking (ST) were applied. Results show that the classification of combined Sentinel-1 and Sentinel-2 data improved the RF, SVM and ST algorithms by 24.2%, 8.7%, and 9.1%, respectively, compared to the classification of Sentinel-1 data individually. Similarities in the estimated areas (7001.35 ± 1.2 ha for RF, 7926.03 ± 0.7 ha for SVM and 7099.59 ± 0.8 ha for ST) show that machine learning can estimate smallholder maize areas with high accuracies. The study concludes that the single-date Sentinel-1 data were insufficient to map smallholder maize farms. However, single-date Sentinel-1 combined with Sentinel-2 data were sufficient in mapping smallholder farms. These results can be used to support the generation and validation of national crop statistics, thus contributing to food security.The Agricultural Research Council, the National Research Foundation and the University of Pretoria.https://www.mdpi.com/journal/sustainabilitydm2022Geography, Geoinformatics and Meteorolog

Mapping smallholder maize farms using multi-temporal Sentinel-1 data in support of the sustainable development goals

Reducing food insecurity in developing countries is one of the crucial targets of the Sustainable Development Goals (SDGs). Smallholder farmers play a crucial role in combating food insecurity. However, local planning agencies and governments do not have adequate spatial information on smallholder farmers, and this affects the monitoring of the SDGs. This study utilized Sentinel-1 multi-temporal data to develop a framework for mapping smallholder maize farms and to estimate maize production area as a parameter for supporting the SDGs. We used Principal Component Analysis (PCA) to pixel fuse the multi-temporal data to only three components for each polarization (vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV/VH), which explained more than 70% of the information. The Support Vector Machine (SVM) and Extreme Gradient Boosting (Xgboost) algorithms were used at model-level feature fusion to classify the data. The results show that the adopted strategy of two-stage image fusion was sufficient to map the distribution and estimate production areas for smallholder farms. An overall accuracy of more than 90% for both SVM and Xgboost algorithms was achieved. There was a 3% difference in production area estimation observed between the two algorithms. This framework can be used to generate spatial agricultural information in areas where agricultural survey data are limited and for areas that are affected by cloud coverage. We recommend the use of Sentinel-1 multi-temporal data in conjunction with machine learning algorithms to map smallholder maize farms to support the SDGs.The Agricultural Research Council, University of Pretoria and National Research Foundation.http://www.mdpi.com/journal/remotesensingpm2022Geography, Geoinformatics and Meteorolog

Modeling the spatial distribution of soil nitrogen content at smallholder maize farms using machine learning regression and Sentinel-2 data

Nitrogen is one of the key nutrients that indicate soil quality and an important component for plant development. Accurate knowledge and management of soil nitrogen is crucial for food security in rural communities, especially for smallholder maize farms. However, less research has been done on generating digital soil nitrogen maps for these farmers. This study examines the utility of Sentinel-2 satellite data and environmental variables to map soil nitrogen at smallholder. maize farms. Three machine learning algorithms—random forest (RF), gradient boosting (GB), and extreme gradient boosting (XG) were investigated for this purpose. The findings indicate that the RF (R 2 = 0.90, RMSE = 0.0076%) model performs slightly better than the GB (R2 = 0.88, RMSE = 0.0083%) and XG (R2 = 0.89, RMSE = 0.0077%) models. Furthermore, the variable importance measure showed that the Sentinel-2 bands, particularly the red and red-edge bands, have a superior performance in comparison to the environmental variables and soil indices. The digital maps generated in this study show the high capability of Sentinel-2 satellite data to generate accurate nitrogen content maps with the application of machine learning. The developed framework can be implemented to map the spatial pattern of soil nitrogen. This will also contribute to soil fertility interventions and nitrogen fertilization management to improve food security in rural communities. This application contributes to Sustainable Development Goal number 2.The Agricultural Research Council, the National Research Foundation and the University of Pretoria.https://www.mdpi.com/journal/sustainabilityGeography, Geoinformatics and Meteorolog

Evaluating spectral indices for winter wheat health status monitoring in Bloemfontein using Lsat 8 data

Monitoring wheat growth under different weather and ecological conditions is vital for a reliable supply of wheat yield estimations. Remote sensing techniques have been applied in the agricultural sector for monitoring crop biophysical properties and predicting crop yields. This study explored the application of Land Surface Temperature (LST)-vegetation index relationships for winter wheat in order to determine indices that are sensitive to changes in the wheat health status. The indices were derived from Landsat 8 scenes over the wheat growing area across Bloemfontein, South Africa. The vegetation abundance indices evaluated were the Normalised Difference Vegetation Index (NDVI) and the Green Normalised Difference Vegetation Index (GNDVI). The moisture indices evaluated were the Normalised Difference Water Index (NDWI) and the Normalised Difference Moisture Index (NDMI). The results demonstrated that LST exhibited an opposing trend with the vegetation abundance indices and an analogous trend with the moisture indices. Furthermore, NDVI proved to be a better index for winter wheat abundance as compared to the GNDVI. The NDWI proved to be a better index for determining water stress in winter wheat as compared to the NDMI. These results indicate that NDVI and NDWI are very sensitive to LST. These indices can be comprehensive indicators for winter wheat health status. These pilot results prove that LST-vegetation index relationships can be used for agricultural applications with a high level of accuracy

Mapping GPS multipath : a case study for the lunar laser ranger timing antenna at HartRAO

Accounting for multipath in Global Navigation Satellite Systems (GNSS) is a difficult task and an important one, especially during the pre-investigation phase for the installation of a permanent GNSS station for positioning or timing applications. Sites with a high level of multipath can cause positioning errors or timing errors resulting in the quality of GNSS products (position or timing) becoming degraded by several metres or nanoseconds. We investigate and attempt to map multipath as part of the site investigation for the installation of the timing antenna for lunar laser ranging applications at the Hartebeesthoek Radio Astronomy Observatory (HartRAO). A high-resolution wavelet power spectrum and a standard deviation parameter are used to map multipath in both the time and frequency domain as well as spatial variations on the sky plot. The high standard deviation values on the sky map are attributed to reflections due to shrubs or trees on the site, while smaller standard deviation areas are attributed to bare soil or less vegetated as this would give constant reflection over time provided the ground has constant moisture. We conclude that the site is suitable for installation of the timing antenna and that a mask of 15°-20° elevation angle will be applied to the timing antenna to minimise multipath at lower elevations.The National Research Foundation (NRF) and the Department of Science and Technology.http://www.sajg.org.za/index.php/sajgam2017Geography, Geoinformatics and Meteorolog

Design of a web-based GNSS data management system at HartRAO : preliminary results

The Space Geodesy Programme of the Hartebeesthoek Radio Astronomy Observatory (HartRAO) is actively engaged in improving the African Earth and Ocean Monitoring Network (sub-project 1.1 of Inkaba yeAfrica) by installing geodetic, oceanographic and geophysics stations across the Sub-Saharan region and Southern ocean. This forms part of the drive to monitor different geophysical parameters via denser networks and with increasing accuracies, so as to better our understanding of the Earth system. The instruments being deployed include Global Navigation Satellite Systems (GNSS) stations, tide-gauges, seismic stations and meteorological units. There are four main space geodetic techniques collocated at HartRAO, making it a fiducial site in Africa. These techniques are GNSS, Satellite Laser Ranging (SLR), Very Long Baseline Interferometry (VLBI) and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS). It is important to ensure that all the collected raw scientific data as well as the derived data products are accessible in a user-friendly manner. Additionally, a new data management system needs to be implemented at HartRAO in order to ensure data integrity. This paper focuses on the implementation of a GNSS data management system. The automated system for the pre-processing and post-processing of GNSS data and other derived products are presented. The data products are then visualized utilizing an interactive web-based map. These scientific products are important in understanding processes that occur on planet Earth such as plate motion, crustal deformation and weather patterns. The implementation of this data management system will facilitate the monitoring of these processes.The Hartebeesthoek Radio Astronomy Observatory (HartRAO) and Inkaba yeAfrica.http://sajg.geoscienceworld.orgam2017Geography, Geoinformatics and Meteorolog

Evaluating spectral indices for winter wheat health status monitoring in Bloemfontein using Lsat 8 data

Monitoring wheat growth under different weather and ecological conditions is vital for a reliable supply of wheat yield estimations. Remote sensing techniques have been applied in the agricultural sector for monitoring crop biophysical properties and predicting crop yields. This study explored the application of Land Surface Temperature (LST)-vegetation index relationships for winter wheat in order to determine indices that are sensitive to changes in the wheat health status. The indices were derived from Landsat 8 scenes over the wheat growing area across Bloemfontein, South Africa. The vegetation abundance indices evaluated were the Normalised Difference Vegetation Index (NDVI) and the Green Normalised Difference Vegetation Index (GNDVI). The moisture indices evaluated were the Normalised Difference Water Index (NDWI) and the Normalised Difference Moisture Index (NDMI). The results demonstrated that LST exhibited an opposing trend with the vegetation abundance indices and an analogous trend with the moisture indices. Furthermore, NDVI proved to be a better index for winter wheat abundance as compared to the GNDVI. The NDWI proved to be a better index for determining water stress in winter wheat as compared to the NDMI. These results indicate that NDVI and NDWI are very sensitive to LST. These indices can be comprehensive indicators for winter wheat health status. These pilot results prove that LST-vegetation index relationships can be used for agricultural applications with a high level of accuracy.The Agricultural Research Councilhttp://www.sajg.org.za/index.php/sajgam2017Geography, Geoinformatics and Meteorolog

Earth observation systems and pasture modeling : a bibliometric trend analysis

An Earth observation system (EOS) is essential in monitoring and improving our understanding of how natural and managed agricultural landscapes change over time or respond to climate change and overgrazing. Such changes can be quantified using a pasture model (PM), a critical tool for monitoring changes in pastures driven by the growing population demands and climate changerelated challenges and thus ensuring a sustainable food production system. This study used the bibliometric method to assess global scientific research trends in EOS and PM studies from 1979 to 2019. This study analyzed 399 published articles from the Scopus indexed database with the search term “Earth observation systems OR pasture model”. The annual growth rate of 19.76% suggests that the global research on EOS and PM has increased over time during the survey period. The average growth per article is n = 74, average total citations (ATC) = 2949 in the USA, is n = 37, ATC = 488, in China and is n = 22, ATC = 544 in Italy). These results show that the field of the study was inconsistent in terms of ATC per article during the study period. Furthermore, these results show three countries (USA, China, and Italy) ranked as the most productive countries by article publications and the Netherlands had the highest average total citations. This may suggest that these countries have strengthened research development on EOS and PM studies. However, developing counties such as Mexico, Thailand, Sri Lanka, and other African countries had a lower number of publications during the study period. Moreover, the results showed that Earth observation is fundamental in understanding PM dynamics to design targeted interventions and ensure food security. In general, the paper highlights various advances in EOS and PM studies and suggests the direction of future studies.The National Research Foundation, University Fort Hare, Alice, Eastern Cape Province, South Africa.https://www.mdpi.com/journal/ijgiam2022Geography, Geoinformatics and Meteorolog

Weed detection in rainfed maize crops using UAV and planetscope imagery

DATA AVAILABILITY STATEMENT : The PlanetScope data were obtained from the Planet website for academic research.Weed invasion of crop fields, such as maize, is a major threat leading to yield reductions or crop right-offs for smallholder farming, especially in developing countries. A synoptic view and timeous detection of weed invasions can save the crop. The sustainable development goals (SDGs) have identified food security as a major focus point. The objectives of this study are to: (1) assess the precision of mapping maize-weed infestations using multi-temporal, unmanned aerial vehicle (UAV), and PlanetScope data by utilizing machine learning algorithms, and (2) determine the optimal timing during the maize growing season for effective weed detection. UAV and PlanetScope satellite imagery were used to map weeds using machine learning algorithms—random forest (RF) and support vector machine (SVM). The input features included spectral bands, color space channels, and various vegetation indices derived from the datasets. Furthermore, principal component analysis (PCA) was used to produce principal components (PCs) that served as inputs for the classification. In this study, eight experiments are conducted, four experiments each for UAV and PlanetScope datasets spanning four months. Experiment 1 utilized all bands with the RF classifier, experiment 2 used all bands with SVM, experiment 3 employed PCs with RF, and experiment 4 utilized PCs with SVM. The results reveal that PlanetScope achieves accuracies below 49% in all four experiments. The best overall performance was observed for experiment 1 using the UAV based on the highest mean accuracy score (>0.88), which included the overall accuracy, precision, recall, F1 score, and cross-validation scores. The findings highlight the critical role of spectral information, color spaces, and vegetation indices in accurately identifying weeds during the mid-to-late stages of maize crop growth, with the higher spatial resolution of UAV exhibiting a higher precision in the classification accuracy than the PlanetScope imagery. The most optimal stage for weed detection was found to be during the reproductive stage of the crop cycle based on the best F1 scores being indicated for the maize and weeds class. This study provides pivotal information about the spatial distribution of weeds in maize fields and this information is essential for sustainable weed management in agricultural activities.The Agricultural Research Council-Natural Resources and Engineering (ARC-NRE), Department of Science and Innovation, Council for Scientific and Industrial Research; the National Research Foundation; the Department of Agriculture, Land Reform and Rural Development (DALRRD); and the University of Pretoria.https://www.mdpi.com/journal/sustainabilityam2024Geography, Geoinformatics and MeteorologySDG-02:Zero HungerSDG-12:Responsible consumption and productio

The potential of Sentinel-1 and Sentinel-2 remote sensing products for monitoring smallholder maize farms in support of the Sustainable Development Goals

Food security is an issue of global concern; this has mandated research on the development of systems for monitoring of agriculture using cost effective techniques such as remote sensing. Smallholder maize farms are dominant in Africa; they produce 80% of the maize in the region. The majority of the African population lives in rural areas and their livelihoods are dependent on smallholder agriculture particularly maize production. Thus, smallholder maize production plays a vital role in combating food insecurity in rural areas. Targeting food insecurity in developing countries is one of the important objectives of the Sustainable Development Goals (SDGs). However, local planning agencies and governments do not have adequate spatial information on smallholder farmers, and this affects the monitoring of the SDGs. Additionally, these farmers are faced with economic and environmental constraints that limit their productivity. Furthermore, the estimates of total planted area are unknown in most developing countries. Techniques for undertaking such estimates are either absent or very unreliable. This study explores the use of Sentinel-1 and Sentinel-2 data products for mapping and monitoring smallholder farms with machine learning. Findings suggest that the multi-temporal approach with the application of support vector machine and extreme gradient boosting is the recommended method for mapping smallholder maize farms in comparison to single date imagery based on lower standard deviation errors. The random forest model was suitable for estimating soil nitrogen. Furthermore, the findings suggest that maize yields can be accurately predicted from two months before harvest. The frameworks developed in this study can be used to generate spatial agricultural information in areas where agricultural survey data are limited. We recommend the use of Sentinel-1 and Sentinel-2 in conjunction with machine learning algorithms to map smallholder maize farms to support the SDGs.Thesis (PhD (Geoinformatics))--University of Pretoria, 2021.Centre for Geo-Information ScienceGeography, Geoinformatics and MeteorologyPhD (Geoinformatics)Unrestricte