DISEÑO DE INTERFACES GRÁFICAS

Wetland regions are highly dynamic systems. Lack of maps describing the allocation of different land cover types throughout the year is a major impediment to managing wetland activities. Knowledge of land cover types and their occurrence time within the year is required to monitor water availability and devise techniques of increasing production to improve food security and ensure sustainable use of wetlands. Land cover dynamics maps act as spatial records that aid in decision planning with respect to land uses. Documenting the short-term land cover patterns, however, aids in informed decision making on seasonal land use planning. Groundwater provides base flow for rivers and is a source of water for agricultural production in the face of changing climate. Understanding the land use-groundwater relationship gives insight into periods over which alternative water sources are required to ensure continued agricultural production. The current study sought to generate land cover maps from optical imagery over the study area and qualitatively examine the effect of land cover on depth to groundwater. Presence of clouds poses a challenge in mapping from optical images. Inconsistency in usable optical images due to cloud cover and limited data availability of single sensor systems motivated the use of a multi-sensor approach in mapping land cover whereby three sensors; RapidEye, Sentinel-2, and Landsat, for the periods 2013 to 2016 was adopted.

The cloud coverage challenge prompted the use of all-weather, free access Sentinel-1 SAR imagery. However, for the period under study, consistent acquisitions were only available as single polarized images for 2015 and 2016. The study therefore assessed the performance of multiple classification algorithms on consistently acquired Sentinel-1 images.

Understanding soil moisture variation in space and time at the plot scale requires high resolution spatial and temporal measurements subsequently aiding in decision making concerning farm management practices. With the planned SAGCOT agricultural intensification strategies, knowledge on field-specific soil properties is crucial in fostering precise farming activities such as in application of fertilizers, whereby infield variations are put into consideration to ensure enhanced crop growth since applications are as per the soil and crop needs (Milder et al. 2013). Subsequently, increased yields improve food security as well as the livelihoods of the population. In situ soil moisture data is also required for validation of satellite-based soil moisture products. Satellite-based soil moisture is in form of pixels whereby soil moisture within the pixel is averaged to one value. In situ soil moisture, on the other hand, could have several measurements within the pixel coverage. Discrepancies in spatial resolution between the satellite footprint and field point data prompt the determination of number of sample points required to adequately capture the mean within a defined areal coverage (Jacobs 2004, Famiglietti et al. 2008). Identification of fields whose average is stable in time reduces the number of measurement points in future field campaigns for validating satellite-based soil moisture products (Cosh et al. 2008, Brocca et al. 2009).

10

Extending the soil moisture assessment to a larger site prompts the application of SAR imagery in soil moisture retrieval due to its vast coverage as soil moisture point data collection is time-consuming and impractical for large areas. Soil moisture and soil roughness influence backscatter recorded by SAR over bare soils. Soil roughness data collection is time-consuming and requires skills to analyze. Moreover, the small farms with varying management practices in Ifakara instigate the development of an a priori soil roughness parameter. In this study, a semi-empirical model by (Baghdadi et al. 2016) generated by incorporating data from multifrequency SAR data for soil moisture retrieval is inverted to derive the roughness parameter (Baghdadi et al. 2016). Soil moisture data scarcity necessitates the application of remote sensing based monitoring techniques and physically based land surface models over the Kilombero catchment. Due to different retrieval algorithms, the available independent soil moisture products are characterized by systematic differences. The reliability of the products is impossible to evaluate due to lack of in situ soil moisture networks. This prompts the generation of merged soil moisture products that take into account the discrepancy estimates between the products.

Estimating variability in the relation between NDVI and soil moisture portrays the impact soil moisture has on vegetation dynamics. Continued monitoring of soil moisture from globally available products to determine the SOS offers a solution to the confusion in planting dates that the farmers experience due to seasonal differences in timing and amount of rainfall, caused by climate variability and climate change (Brown et al. 2010). Ultimately, precision in planting times ensures maximum utilization of rainwater in meeting the plant water requirements thus ensuring maximum yields are obtained. Moreover, a delay in SOS is an indicator of declined crop production, which is critical in food security monitoring (Brown and de Beurs 2008).

This study was executed under the BMBF funded GlobE project “Wetlands in East Africa – Reconciling future food production with environmental protection” whose goal is assessing the potential of utilizing wetlands for increased and sustainable food production (https://www.wetlands-africa.uni-bonn.de/). The multi-disciplinary project consists of a series of diverse groups with backgrounds in agronomy, hydrology, economics, ecology, social sciences and remote sensing.

The research presented herein is within the remote sensing discipline. The main purpose of the research was to assess land cover changes within the tropical floodplain in the Kilombero wetland and evaluate the soil moisture information based on in situ data, SAR and global datasets derived from different sources.

The research seeks to:

i. Assess the land cover dynamics to understand the land cover changes throughout the year using optical data.

ii. Examine the performance of VV single polarized backscatter images, grey level co-occurrence matrix (GLCM) texture images and their PCA derivatives in monitoring land cover at high spatial and temporal resolution.

11

iii. Explore the variation of soil moisture over space and time. Derive a roughness parameter as an input for estimating soil moisture from SAR data.

iv. Evaluate the performance of global soil moisture products and estimate the variability in the relation between soil moisture and SOS

The hypotheses guiding the study are as follows:

i. Depth to groundwater has an influence on spatiotemporal land cover dynamics.

ii. Single polarized SAR images have sufficient information enabling land cover classification.

iii. There exists a variation in soil physical properties along a hydrological gradient within the Kilombero floodplain.

iv. Surface roughness has a big effect on the accuracy of soil moisture derived from SAR imagery. v. Global soil moisture products can be utilized to infer the start of planting seasons.