Peer-Reviewed Journal Details
Mandatory Fields
Ali, Iftikhar; Cawkwell, Fiona; Dwyer, Edward; Green, Stuart
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Modeling Managed Grassland Biomass Estimation by Using Multitemporal Remote Sensing Data—A Machine Learning Approach
Scopus: 16 ()
Optional Fields
Fuzzy reasoning Geophysical techniques Land cover Land use Learning (artificial intelligence) Neural nets Regression analysis Remote sensing by radar Vegetation Ad 2001 to 2012 Alos-2 Anfis model Grange Ireland Modis product Moorepark Radarsat2 Sentinel Tandem-x Terrasar-x Adaptive neuro-fuzzy inference system Agricultural land Artificial neural network Dairy farming Grassland biomass estimation In situ measurement Livestock industry Machine learning approach Multiple linear regression Multitemporal remote sensing data Pasture Satellite remote sensing data Spaceborne sensors Vegetation index Winter fodder Agriculture Biological system modeling Biomass Estimation Monitoring Remote sensing Satellites Biomass estimation Machine learning Managed grassland Time series
More than 80% of agricultural land in Ireland is grassland, which is a major feed source for the pasture based dairy farming and livestock industry. Many studies have been undertaken globally to estimate grassland biomass by using satellite remote sensing data, but rarely in systems like Ireland's intensively managed, but small-scale pastures, where grass is grazed as well as harvested for winter fodder. Multiple linear regression (MLR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed to estimate the grassland biomass (kg dry matter/ha/day) of two intensively managed grassland farms in Ireland. For the first test site (Moorepark) 12 years (2001-2012) and for second test site (Grange) 6 years (2001- 2005, 2007) of in situ measurements (weekly measured biomass) were used for model development. Five vegetation indices plus two raw spectral bands (RED=red band, NIR=Near Infrared band) derived from an 8-day MODIS product (MOD09Q1) were used as an input for all three models. Model evaluation shows that the ANFIS (RM2moorepark = 0.85, RMSEMoorepark = 11.07; RGrange2 = 0.76, RMSEGrange = 15.35) has produced improved estimation of biomass as compared to the ANN and MLR. The proposed methodology will help to better explore the future inflow of remote sensing data from spaceborne sensors for the retrieval of different biophysical parameters, and with the launch of new members of satellite families (ALOS-2, Radarsat2, Sentinel, TerraSAR-X, TanDEM-X/L) the development of tools to process large volumes of image data will become increasingly important.
Grant Details