Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

Abstract

Abstract:In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation productivity is directly related to phenology, most approaches to estimate such risks do not explicitly take into account the actual timing of vegetation growth and development. The date of the start of the season (SOS) or of the peak canopy density can be assessed by remote sensing techniques in a timely manner during the growing season. However, there is limited knowledge about the relationship between vegetation biomass production and these variables at the regional scale. This study describes the first attempt to increase our understanding of such a relationship through the analysis of phenological variables retrieved from SPOT-VEGETATION time series of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Two key phenological variables (growing season length (GSL); timing of SOS) and the OPEN ACCESSRemote Sens. 2014, 6 5869 maximum value of FAPAR attained during the growing season (Peak) are analyzed as potentially related to a proxy of biomass production (CFAPAR, the cumulative value of FAPAR during the growing season). GSL, SOS and Peak all show different spatial patterns of correlation with CFAPAR. In particular, GSL shows a high and positive correlation with CFAPAR over the whole Sahel (mean r = 0.78). The negative correlation between delays in SOS and CFAPAR is stronger (mean r = −0.71) in the southern agricultural band of the Sahel, while the positive correlation between Peak FAPAR and CFAPAR is higher in the northern and more arid grassland region (mean r = 0.75). The consistency of the results and the actual link between remote sensing-derived phenological parameters and biomass production were evaluated using field measurements of aboveground herbaceous biomass of rangelands in Senegal. This study demonstrates the potential of phenological variables as indicators of biomass production. Nevertheless, the strength of the relation between phenological variables and biomass production is not universal and indeed quite variable geographically, with large scattered areas not showing a statistically significant relationship.

Fodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series

“…A significant relationship (p < 0.001) between the field measurements of herbaceous biomass and the cumulated FAPAR over the growing season was found by [34] in Senegal, although only 34% of the in situ biomass was explained by the linear regression. The study focused only on herbaceous biomass, however, and recently [36] highlighted the importance of woody plants in the satellite time series of vegetation indices in the Sahelian ecosystems.…”

“…Many authors have endorsed the applicability of these phenological metrics for crop [9] and grassland [32] phenological monitoring, particularly for analyzing African farming systems [33]. Recently, phenological metrics based on the FAPAR time series were recommended by [34] for providing relevant early information on biomass production in the Sahel. When associated with ground biomass data, such variables would enable early warning models to be established and would improve the use of early warning systems (EWS) in planning emergency responses and food aid interventions in the case of a forthcoming crisis [35].…”

Biomass estimation to support pasture management in Niger

“…CFAPAR is defined as the integral of FAPAR during the growing season subtracted by the area under the baseline (Fig. 3), as proposed by Meroni et al (2014a). The time interval for integration is dynamically adjusted for every site and every year based on the start and end of the season (SOS, EOS).…”

A Simple Fusion Method for Image Time Series Based on the Estimation of Image Temporal Validity

“…A simple and intuitive fusion function that satisfies the previously stated desired property is a weighted average (Equation (4)) between the L and H images, where the weights are a function that depend on the validities as computed using Equation (2).…”

“…[1], biomass production study [2] and land cover classification [3]. Monitoring agricultural activities is crucial for the assessment of productivity and food supplies, and it requires information that highly varies in space and time [4].…”