Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa

Abstract

Food security is the topmost priority on the global agenda. Accurate agricultural statistics (i.e., cropped area) are essential for decision making and developing appropriate programs to achieve food security. However, derivation of these essential agricultural statistics, especially in developing countries, is fraught with many challenges including financial, logistical and human capacity limitations. This study investigated the use of fractional cover approaches in mapping cropland area in the heterogeneous landscape of West Africa. Discrete cropland areas identified from multi-temporal Landsat data were upscaled to MODIS resolution using random forest regression. Producer's accuracy and user's accuracy of the cropland class in the Landsat scale analysis averaged 95% and 94%, respectively, indicating good separability between crop and non-crop land. Validation of the fractional cropland cover map at MODIS resolution (MODIS_FCM) revealed an overall mean absolute error of 19%. Comparison of MODIS_FCM with the MODIS land cover product (e.g., MODIS_LCP) demonstrate the suitability of the proposed approach to cropped area estimation in smallholder dominant heterogeneous landscapes over existing global solutions. Comparison with official government statistics (i.e., cropped area) revealed variable levels of agreement and partly enormous disagreements, which clearly indicate the need to integrate remote sensing approaches and ground based surveys conducted by agricultural ministries in improving cropped area estimation. The recent availability of a wide range of open access remote sensing data is expected to expedite this integration and contribute missing information urgently required for regional assessments of food security in West Africa and beyond.

Small Reservoirs, Landscape Changes and Water Quality in Sub-Saharan West Africa

“…Reference data for classifying the Landsat images were primarily generated by interpreting various image color composites. The use of color composites (i.e., displaying different spectral bands as red, blue and green) permits the identification of general LULC classes such as the ones considered in this study with reasonable accuracy [60]. In instances where it was difficult to decipher one LULC type from the other (e.g., sparse from dense vegetation), Google Earth images were used.…”

Evapotranspiration in the Tono Reservoir Catchment in Upper East Region of Ghana Estimated by a Novel TSEB Approach from ASTER Imagery

“…In response to the drought conditions, a series of small earth dams and dugouts were constructed. It is within this scheme of dams and dugouts construction that the Tono irrigation dam with 93 million cubic meters storage capacity and a surface area of about 19.6 square kilometers was constructed [48][49][50][51][52]. We used field measured flux data from an eddy covariance system established at Kayoro Dakorenia (10.918100 N, −1.320900 W, 292 m a.s.l.)…”

Mapping Maize Cultivated Area Combining MODIS EVI Time Series and the Spatial Variations of Phenology over Huanghuaihai Plain

“…As a kind of earth observation technology, remote sensing can effectively acquire the spatial distribution and spectral information of ground objects, with wide geographic coverage and the merit of large information capacity, high accuracy, and speed, which provides opportunities for crop identification [5][6][7][8][9]. Different crops have various spectral characteristics in remotely sensed images [10,11].…”