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Rapid population growth in West Africa has led to expansion in croplands due to the need to grow more food to meet the rising food demand of the burgeoning population. These expansions negatively impact the sub-region's ecosystem, with implications for water and soil quality, biodiversity and climate. In order to appropriately monitor the changes in croplands and assess its impact on the ecosystem and other environmental processes, accurate and up-to-date information on agricultural land use is required. But agricultural land use mapping (i.e. mapping the spatial distribution of crops and croplands) in West Africa has been challenging due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. This study, therefore, investigated the possibilities of improving agricultural land use mapping by utilizing optical satellite images with higher spatial and temporal resolution as well as images from Synthetic Aperture Radar (SAR) systems which are near-independent of weather conditions. The study was conducted at both watershed and regional scales.
At watershed scale, classification of different crop types in three watersheds in Ghana, Burkina Faso and Benin was conducted using multi-temporal: (1) only optical images (RapidEye) and (2) optical plus dual polarimetric (VV/VH) SAR images (TerraSAR-X). In addition, inter-annual or short term (2-3 years) changes in cropland area in the past ten years were investigated using historical Landsat images. Results obtained indicate that the use of only optical images to map different crop types in West Africa can achieve moderate classification accuracies (57% to 71%). Overlaps between the cropping calendars of most crops types and certain inter-croppings pose a challenge to optical images in achieving an adequate separation between those crop classes. Integration of SAR images, however, can improve classification accuracies by between 8 and 15%, depending on the number of available images and their acquisition dates. The sensitivity of SAR systems to different crop canopy architectures and land surface characteristics improved the separation between certain crop types. The VV polarization of TerraSAR-X was found to better discrimination between crop types than the VH. Images acquired between August and October were found to be very useful for crop mapping in the sub-region due to structural differences in some crop types during this period.
At the regional scale, inter-annual or short term changes in cropland area in the Sudanian Savanna agro-ecological zone in West Africa were assessed by upscaling historical cropland information derived at the watershed scale (using Landsat imagery) unto a coarse spatial resolution, but geographically large, satellite imagery (MODIS) using regression based modeling. The possibility of using such regional scale cropland information to improve government-derived agricultural statistics was investigated by comparing extracted cropland area from the fractional cover maps with district-level agricultural statistics from Ghana The accuracy of the fractional cover maps (MAE between 14.2% and 19.1%) indicate that the heterogeneous agricultural landscape of West Africa can be suitably represented at the regional or continental scales by estimating fractional cropland cover on low resolution Analysis of the results revealed that cropland area in the Sudanian Savanna zone has experienced inter-annual or short term fluctuations in the past ten years due to a variety of factors including climate factors (e.g. floods and droughts), declining soil fertility, population increases and agricultural policies such as fertilizer subsidies. Comparison of extracted cropland area from the fractional cover maps with government's agricultural statistics (MoFA) for seventeen districts (second administrative units) in Ghana revealed high inconsistencies in the government statistics, and highlighted the potential of satellite derived cropland information at regional scales to improve national/sub-national agricultural statistics in West Africa.
The results obtained in this study is promising for West Africa, considering the recent launch of optical (Landsat 8) and SAR sensors (Sentinel-1) that will provide free data for crop mapping in the sub-region. This will improve chances of obtaining adequate satellite images acquired during the cropping season for agricultural land use mapping and bolster opportunities of operationalizing agricultural land use mapping in West Africa. This can benefit a wide range of biophysical and economic models and improve decision making based on their results.
Remote sensing time series is the collection or acquisition of remote sensing data in a
fixed equally spaced time period over a particular area or for the whole world. Near
daily high spatial resolution data is very much needed for remote sensing applications
such as agriculture monitoring, phenology change detection, environmental
monitoring and so on. Remote sensing applications can produce better and accurate
results if they are provided with dense and accurate time series of data. The current
remote sensing satellite architecture is still not capable of providing near daily
or daily high spatial resolution images to fulfill the needs of the above mentioned
remote sensing applications. Limitations in sensors, high development, operational
costs of satellites and presence of clouds blocking the area of observation are some
of the reasons that makes near daily or daily high spatial resolution optical remote
sensing data highly challenging to achieve. With developments in the optical sensor
systems and well planned remote sensing satellite constellations, this condition
can be improved but it comes at a cost. Even then the issue will not be completely
resolved and thus the growing need for high temporal and high spatial resolution
data cannot be fulfilled entirely. Because the data collection process relies on satellites
which are physical system, these can fail unpredictably due to various reasons
and cause a complete loss of observation for a given period of time making a gap
in the time series. Moreover, to observe the long term trend in phenology change
due to rapidly changing environmental conditions, the remote sensing data from
the present is not just sufficient, the data from the past is also important. A better
alternative solution for this issue can be the generation of remote sensing time series
by fusing data from multiple remote sensing satellite which has different spatial and
temporal resolutions. This approach will be effective and efficient. In this method
a high temporal low spatial resolution image from a satellite such as Sentinel-2 can
be fused with a low temporal and high spatial resolution image from a satellite such
as the Sentinel-3 to generate a synthetic high temporal high spatial resolution data.
Remote sensing time series generation by data fusion methods can be applied to
the satellite images captured currently as well as the images captured by the satellites
in the past. This will provide the much needed high temporal and high spatial
resolution images for remote sensing applications. This approach with its simplistic
nature is cost effective and provides the researchers the means to generate the
data needed for their application on their own from the limited source of data available
to them. An efficient data fusion approach in combination with a well planned
satellite constellation can offer a solution which will ensure near daily time series of
remote sensing data with out any gap. The aim of this research work is to develop
an efficient data fusion approaches to achieve dense remote sensing time series.