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.