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

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

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

A review of the lunar laser ranging technique and contribution of timing systems

The lunar laser ranging (LLR) technique is based on the two-way time-of-flight of laser pulses from an earth station to the retroreflectors that are located on the surface of the moon. We discuss the ranging technique and contribution of the timing systems and its significance in light of the new LLR station currently under development by the Hartebeesthoek Radio Astronomy Observatory (HartRAO). Firstly, developing the LLR station at HartRAO is an initiative that will improve the current geometrical network of the LLR stations which are presently concentrated in the northern hemisphere. Secondly, data products derived from the LLR experiments – such as accurate lunar orbit, tests of the general relativity theory, earth–moon dynamics, interior structure of the moon, reference frames, and station position and velocities – are important in better understanding the earth–moon system. We highlight factors affecting the measured range such as the effect of earth tides on station position and delays induced by timing systems, as these must be taken into account during the development of the LLR analysis software. HartRAO is collocated with other fundamental space geodetic techniques which makes it a true fiducial geodetic site in the southern hemisphere and a central point for further development of space-based techniques in Africa. Furthermore, the new LLR will complement the existing techniques by providing new niche areas of research both in Africa and internationally.The National Research Foundation (NRF), the Department of Science and Technology and Inkaba yeAfrica.http://www.sajs.co.zaam2016Geography, Geoinformatics and Meteorolog

Analysis of the performance of hydrogen maser clocks at the Hartebeesthoek Radio Astronomy Observatory

Hydrogen maser frequency standards are commonly utilised in various space geodetic techniques such as Very Long Baseline Interferometry (VLBI) as local reference clocks. The Hartebeesthoek Radio Astronomy Observatory in South Africa is currently operating two maser frequency standards i.e., an EFOS28 and an iMaser72 for the 15 m and 26 m VLBI radio telescopes respectively, an older EFOS6 is a standby spare. This study utilised the least-squares method to derive clock parameters, which indicates the performance levels of the masers by making use of the offset measurements obtained between hydrogen maser clock 1 PPS and GNSS 1 PPS for a period of 35 days. The masers were also compared using a frequency comparator (VCH-314) for a time period of 100 s. The results indicate that the performances of both Masers are relatively similar to each other, with short-term and long-term results indicating good agreement. The iMaser72 has a better standard error of 0.0039 μs compared to the standard error of 0.0059 μs for the EFOS28 maser clock. In general, both masers performed at an expected level required for radio astronomy and geodetic VLBI applications. The method used in this study proved to be useful in managing local hydrogen maser clocks to ensure accurate VLBI observations are obtained.The National Research Foundation (NRF), the University of Pretoria and the Department of Science and Technology (DST).http://www.sajg.org.za/index.php/sajgam2017Geography, 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 use of remote sensing and GIS for land use and land cover mapping in Eswatini : a review

Remote sensing and GIS are often used to assess spatiotemporal variations for land use/land cover (LULC) monitoring and classification. While LULC monitoring and classification has been undertaken in Eswatini, little attention has been given to ascertaining covered thematic areas, methods of image classification, and approaches and techniques for improving classification accuracy. This paper summarises and synthesizes the progress made in the Kingdom of Eswatini regarding the application of remote sensing and GIS in LULC monitoring and classification. Eight thematic areas (water resources mapping; land degradation; forestry; wildfire detection; urban expansion; crop production; disease surveillance; general mapping) dominate evaluated LULC studies, employing three LULC classification methods (classic; manual; advanced). While some studies include strengths and weaknesses of LULC classification techniques applied, others do not. This review shows that only two advanced classifiers (random forest; object-based) were identified from the reviewed articles. In addition, reviewed studies applied only two approaches (use of multi temporal data; fine spatial resolution data) and three techniques (use of ancillary data; post-classification procedure; the use of multisource data) for improving classification accuracy. Furthermore, the review finds that limited LULC investigations have been covered in Eswatini with a specific focus on the Sustainable Development Goals (SDGs). As such, this review recommends 1) the inclusion of higher resolution imagery for mapping purposes, 2) the adaptation of strengths and weaknesses for any image classification technique employed in future publications, 3) the use of more varied approaches and techniques for improving classification accuracy and area estimates, 4) inclusion of standard errors or confidence intervals for error-adjusted area estimates as part of accuracy assessment reporting, 5) the application of advanced image classifiers, and 6) the application of Earth Observation (EO) Analysis Ready Data (ARD) in the production of information for the support of the SDGs.http://www.sajg.org.za/index.php/sajgam2022Geography, Geoinformatics and Meteorolog

Detection of magnetite in the Roossenekal area of the Eastern Bushveld Complex, South Africa, using multispectral remote sensing data

Multispectral sensors, along with common and advanced algorithms, have become efficient tools for routine lithological discrimination and mineral potential mapping. It is with this paradigm in mind that this paper sought to evaluate and discuss the detection and mapping of magnetite on the Eastern Limb of the Bushveld Complex, using high spectral resolution multispectral remote sensing imagery and GIS techniques. Despite the wide distribution of magnetite, its economic importance, and its potential as an indicator of many important geological processes, not many studies had looked at the detection and exploration of magnetite using remote sensing in this region. The Maximum Likelihood and Support Vector Machine classification algorithms were assessed for their respective ability to detect and map magnetite using the PlanetScope Analytic data. A K-fold cross-validation analysis was used to measure the performance of the training as well as the test data. For each classification algorithm, a thematic landcover map was created and an error matrix, depicting the user’s and producer’s accuracies as well as kappa statistics, was derived. A pairwise comparison test of the image classification algorithms was conducted to determine whether the two classification algorithms were significantly different from each other. The Maximum Likelihood Classifier significantly outperformed the Support Vector Machine algorithm, achieving an overall classification accuracy of 84.58% and an overall kappa value of 0.79. Magnetite was accurately discriminated from the other thematic landcover classes with a user’s accuracy of 76.41% and a producer’s accuracy of 88.66%. The overall results of this study illustrated that remote sensing techniques are effective instruments for geological mapping and mineral investigation, especially iron oxide mineralization in the Eastern Limb of the Bushveld Complex.http://sajg.geoscienceworld.orgam2021Geography, Geoinformatics and MeteorologyGeolog

Comparison of site velocities derived from collocated GPS, VLBI and SLR techniques at the Hartebeesthoek Radio Astronomy Observatory (Comparison of site velocities)

Space geodetic techniques provide highly accurate methods for estimating bedrock stability at subcentimetre level. We utilize data derived from Satellite Laser Ranging (SLR), Very Long Baseline Interferometry (VLBI) and Global Positioning Systems (GPS) techniques, collocated at the Hartebeesthoek Radio Astronomy Observatory, to characterise local plate motion and compare the solutions from the three techniques. Data from the GNSS station were processed using the GAMIT/GLOBK (version 10.4) software, data from the SLR station (MOBLAS-6)were processed using the Satellite Laser Ranging Data Analysis Software (SDAS) and the VLBI data sets were processed using the Vienna VLBI Software (VieVS) software. Results show that there is a good agreement between horizontal and vertical velocity components with a maximum deviation of 1.7 mm/yr, 0.7 mm/yr and 1.3 mm/yr between the North, East and Up velocity components respectively for the different techniques. At HartRAO there is no significant trend in the vertical component and all the techniques used are consistent with the a-priori velocities when compared with each other. This information is crucial in monitoring the local motion variations since geodetic instruments require a very stable base to minimise measurement errors. These findings demonstrate that station coordinate time-series derived with different techniques and analysis strategies provide comparable results.The University of Pretoria, Vienna University of Technology, the National Research Foundation (NRF) of South Africa, Hart-RAO and Inkaba yeAfrica.http://www.degruyter.com/view/j/jog

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