Exploring the potential of using remote sensing data to model agricultural systems in data-limited areas
| dc.contributor.advisor | Crespo, Olivier | |
| dc.contributor.author | Dlamini, Luleka | |
| dc.date.accessioned | 2020-09-11T15:19:37Z | |
| dc.date.available | 2020-09-11T15:19:37Z | |
| dc.date.issued | 2020 | |
| dc.date.updated | 2020-09-11T13:55:48Z | |
| dc.description.abstract | Crop models (CMs) can be a key component in addressing issues of global food security as they can be used to monitor and improve crop production. Regardless of their wide utilization, the employment of these models, particularly in isolated and rural areas, is often limited by the lack of reliable input data. This data scarcity increases uncertainties in model outputs. Nevertheless, some of these uncertainties can be mitigated by integrating remotely sensed data into the CMs. As such, increasing efforts are being made globally to integrate remotely sensed data into CMs to improve their overall performance and use. However, very few such studies have been done in South Africa. Therefore, this research assesses how well a crop model assimilated with remotely sensed data compares with a model calibrated with actual ground data (Maize_control). Ultimately leading to improved local cropping systems knowledge and the capacity to use CMs. As such, the study calibrated the DSSAT-CERES-Maize model using two generic soils (i.e. heavy clay soil and medium sandy soil) which were selected based on literature, to measure soil moisture from 1985 to 2015 in Bloemfontein. Using the data assimilation approach, the model's soil parameters were then adjusted based on remotely sensed soil moisture (SM) observations. The observed improvement was mainly assessed through the lens of SM simulations from the original generic set up to the final remotely sensed informed soil profile set up. The study also gave some measure of comparison with Maize_control and finally explored the impacts of this specific SM improvement on evapotranspiration (ET) and maize yield. The result shows that when compared to the observed data, assimilating remotely sensed data with the model significantly improved the mean simulation of SM while maintaining the representation of its variability. The improved SM, as a result of assimilation of remotely sensed data, closely compares with the Maize_control in terms of mean but there was no improvement in terms of variability. Data assimilation also improved the mean and variability of ET simulation when compared that of Maize_control, but only with heavy clay soil. However, maize yield was not improved in comparison. This confirms that these outputs were influenced by other factors aside from SM or the soil profile parameters. It was concluded that remote sensing data can be used to bias correct model inputs, thus improve certain model outputs. | |
| dc.identifier.apacitation | Dlamini, L. (2020). <i>Exploring the potential of using remote sensing data to model agricultural systems in data-limited areas</i>. (). ,Faculty of Science ,Department of Environmental and Geographical Science. Retrieved from http://hdl.handle.net/11427/32239 | en_ZA |
| dc.identifier.chicagocitation | Dlamini, Luleka. <i>"Exploring the potential of using remote sensing data to model agricultural systems in data-limited areas."</i> ., ,Faculty of Science ,Department of Environmental and Geographical Science, 2020. http://hdl.handle.net/11427/32239 | en_ZA |
| dc.identifier.citation | Dlamini, L. 2020. Exploring the potential of using remote sensing data to model agricultural systems in data-limited areas. . ,Faculty of Science ,Department of Environmental and Geographical Science. http://hdl.handle.net/11427/32239 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Dlamini, Luleka AB - Crop models (CMs) can be a key component in addressing issues of global food security as they can be used to monitor and improve crop production. Regardless of their wide utilization, the employment of these models, particularly in isolated and rural areas, is often limited by the lack of reliable input data. This data scarcity increases uncertainties in model outputs. Nevertheless, some of these uncertainties can be mitigated by integrating remotely sensed data into the CMs. As such, increasing efforts are being made globally to integrate remotely sensed data into CMs to improve their overall performance and use. However, very few such studies have been done in South Africa. Therefore, this research assesses how well a crop model assimilated with remotely sensed data compares with a model calibrated with actual ground data (Maize_control). Ultimately leading to improved local cropping systems knowledge and the capacity to use CMs. As such, the study calibrated the DSSAT-CERES-Maize model using two generic soils (i.e. heavy clay soil and medium sandy soil) which were selected based on literature, to measure soil moisture from 1985 to 2015 in Bloemfontein. Using the data assimilation approach, the model's soil parameters were then adjusted based on remotely sensed soil moisture (SM) observations. The observed improvement was mainly assessed through the lens of SM simulations from the original generic set up to the final remotely sensed informed soil profile set up. The study also gave some measure of comparison with Maize_control and finally explored the impacts of this specific SM improvement on evapotranspiration (ET) and maize yield. The result shows that when compared to the observed data, assimilating remotely sensed data with the model significantly improved the mean simulation of SM while maintaining the representation of its variability. The improved SM, as a result of assimilation of remotely sensed data, closely compares with the Maize_control in terms of mean but there was no improvement in terms of variability. Data assimilation also improved the mean and variability of ET simulation when compared that of Maize_control, but only with heavy clay soil. However, maize yield was not improved in comparison. This confirms that these outputs were influenced by other factors aside from SM or the soil profile parameters. It was concluded that remote sensing data can be used to bias correct model inputs, thus improve certain model outputs. DA - 2020_ DB - OpenUCT DP - University of Cape Town KW - Environmental and Geographical Science LK - https://open.uct.ac.za PY - 2020 T1 - Exploring the potential of using remote sensing data to model agricultural systems in data-limited areas TI - Exploring the potential of using remote sensing data to model agricultural systems in data-limited areas UR - http://hdl.handle.net/11427/32239 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/32239 | |
| dc.identifier.vancouvercitation | Dlamini L. Exploring the potential of using remote sensing data to model agricultural systems in data-limited areas. []. ,Faculty of Science ,Department of Environmental and Geographical Science, 2020 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/32239 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Environmental and Geographical Science | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Environmental and Geographical Science | |
| dc.title | Exploring the potential of using remote sensing data to model agricultural systems in data-limited areas | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |