Precipitation Patterns in West Africa

Motha, Raymond P.; LeDuc, Sharon K.; Steyaert, Louis T.; Sakamoto, Clarence M.; Strommen, N. D.

doi:10.1175/1520-0493(1980)108<1567:ppiwa>2.0.co;2

Cited by 39 publications

(22 citation statements)

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“…Creating 30-m cloud-free imagery composition for entire Africa is a challenging task because of west and east African monsoon [65], which cause constant clouds in Gulf of Guinea and east Mozambique most of the time. As a result, we established the Sentinel-2 composite during two periods (period 1: January-June 2016, and period 2: July-December 2015), each coinciding with the two main crop growing seasons [24,44,[66][67][68][69] in Africa. All data were resampled to 30-m using the average value of all involved pixels.…”

Section: Cloud-free Satellite Imagery Composition At 30-m Resolutionmentioning

confidence: 99%

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

et al. 2017

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A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of

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Section: Cloud-free Satellite Imagery Composition At 30-m Resolutionmentioning

confidence: 99%

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

et al. 2017

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“…The risk that fictitious discontinuities might arise in time series is very limited. The climatic units Three techniques are used for the regionalization of rainfall fields in Africa: (a) factor analytical techniques such as principal component analysis, used by Klaus (1978), Ceron et al (1984), Janicot (1985) and Chaire (1988); (b) similarities in the seasonal and annual distribution of rainfall, and some other variables, used by Lamb (1978), Nicholson (1980Nicholson ( , 1981, Nicholson et al (1988), Motha et al (1980), and Farmer (1988); and (c) division of the space in regular small areas, used by Lambergeon (1977) (nine square areas) and Janicot (1990Janicot ( , 1992) (four square areas).…”

Section: Data Collection and Qualitymentioning

confidence: 99%

“…Many studies focused on the Sahelian areas (Winstanley, 1973;Bunting et al, 1976;Lamb, 1978Lamb, , 1982Nicholson, 1979;Olivry, 1983Olivry, ,1987Farmer, 1988;Lamb & Peppier, 1992;Hulme, 1998). Authors also compared Sahelian rainfall with rainfall over other African regions, especially in West Africa, but also in Central Africa (Sircoulon, 1976(Sircoulon, , 1985Lambergeon, 1977; Open for discussion until 1 October 2001 Motha et al, 1980;Nicholson, 1980Nicholson, , 1983Camberlin, 1990;Hulme, 1992). Rainfall fields were divided into smaller regions to describe better the local variations, and also to allow correlations with other climatic parameters (Nicholson, 1981;Nicholson & Entekhabi, 1986;Nicholson el ah, 1988;Janicot, 1990Janicot, , 1992.…”

Section: Introductionmentioning

confidence: 99%

Trends and discontinuities in regional rainfall of West and Central Africa: 1951–1989

Mahé¹,

L'Hôte

Olivry

et al. 2001

Hydrological Sciences Journal

101

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Standardized regional mean annual rainfall series are analysed over the period 1951-1989 from a data set of 891 rainfall stations which covers 23 countries of West and Central Africa. Missing values are estimated by using regionalized indexes computed on the basis of a morpho-climatic delimitation of 44 homogeneous climatic units. Searches for statistical discontinuities in rainfall series show no discontinuity for most units of Central Africa. For several units of West Africa the first discontinuity occurs at the end of the 1950s. The main discontinuity period occurs between 1968 and 1970, followed by a second one at the beginning of the 1980s. Rainfall deficit is greater north of 10°N, and is also important in the Guinean Mountains and on the northern coast of the Gulf of Guinea, west of the Atakora Mountains. Regions leeward of mountainous areas experienced moderate rainfall decrease.

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“…Cependant ces propositions diffèrent de celles de Lough (1986), qui postule plusieurs causes à la sécheresse sahélienne, ou de Nicholson (1981b) et des chercheurs ayant travaillé directement sur les champs de précipitations (Motha et al, 1980 par exemple, sans rappeler Tanaka et al 1975), qui insistent aussi sur la modification de l'intensité de la convection associée aux circulations méridiennes (Hadley) ou zonales (Walker). Lough confirme les résultats de Lamb sur la période 1948-1972, à savoir une corrélation entre le «dipôle» de l'Atlantique tropical et les précipitations sahéliennes (corrélation de -0,66), mais montre que ceux-ci ne sont pas vérifiés sur la période 1911-1939 (corrélation de -0,09).…”

Section: Le Forçage Des Températures De Surface Des Océans Aux Latituunclassified

L'évolution des idées sur la variabilité interannuelle récente du cycle de l'eau atmosphérique en Afrique de l'ouest

Janicot¹,

Fontaine²

1993

Météorologie

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Precipitation Patterns in West Africa

Cited by 39 publications

References 0 publications

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

Trends and discontinuities in regional rainfall of West and Central Africa: 1951–1989

L'évolution des idées sur la variabilité interannuelle récente du cycle de l'eau atmosphérique en Afrique de l'ouest

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