Mapping and estimation of above-ground grass biomass using sentinel 2A satellite data

Abstract

Above-Ground Grass Biomass (AGGB) mapping and estimation is one of the important parameters for environmental ecosystem and grazing-lands management, particularly for livestock farming. However, previous models for estimation of AGGB with satellite imagery has some difficulty in choosing a particular satellite and vegetation index that can build a good estimation model at a higher accuracy. This study explores the potentiality of Sentinel 2A data to derive a satellite-based model for AGGB mapping and estimation. The study area was Skudai, Johor in Malaysia Peninsular. Grass parameters of forty grass sample units were measured in the field and their corresponding AGGB was later measured in the laboratory. The samples were used for modelling and assessment. Four indices were tested for their fitness in modelling AGGB from the satellite data. The result from the grass allometric analysis indicates that grass height and volume demonstrate good relationship with the measured AGGB (R² = 0.852 and 0.837 respectively). Vegetation Index Number (VIN) has the best fit for modeling AGGB (R2 = 0.840) compared to other vegetation indices. The derived satellite AGGB estimate was validated with the assessment field and allometry derived AGGB at RMSE = 15.89g and 44.45g, respectively. This study demonstrate that VIN derived from Sentinel 2A MSI satellite data can be used to model AGGB estimation at a good accuracy. Therefore, it will contribute to providing reliable information on AGGB of grazing lands for sustainable livestock farming.