Abstract
Abstract. The objective of this study was to investigate the application of multi-temporal optical and polarimetric synthetic aperture radar (PolSAR) Earth observations for crop characterization. Crop dry biomass, Leaf Area Index (LAI), and Plant Water Content (PWC) were estimated and assessed using Machin learning approaches. An accurate estimation of crop parameters provides essential information to increased food production and plays a crucial role in the management of agricultural lands. Multispectral and PolSAR data provide valuable observations of spectral and structural properties which are essential for crops parameter modelling. The Earth observations used in this paper were collected by RapidEye satellites and Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system in the summer of 2012, over an agriculture area in Winnipeg, Manitoba, Canada. The RapidEye vegetation indices (VIs) and UAVSAR polarimetric parameters were used as inputs in artificial neural network (ANN) and support vector regression (SVR) models for canola biophysical parameters estimation. The best models were provided by SVR for canola. Also combining optical VIs and polarimetric features appeared as a powerful tool for crop parameters estimation in agricultural lands.
Highlights
Crop forecasting helps to predict crop yields and production before harvesting, which is an essential factor for national food security, food trade, market management and evaluation of supply and demand (J. Liu, Pattey, and Jégo 2012; Wiseman et al 2014; Yue et al 2017)
Results showed that the support vector regression (SVR) modelled leaf area index (LAI), biomass and plant water content (PWC) are more accurate than artificial neural network (ANN) for canola
The accuracies and scatterplots of canola parameters modelling by ANN and SVR are reported in Table 2 and Fig 2
Summary
Crop forecasting helps to predict crop yields and production before harvesting, which is an essential factor for national food security, food trade, market management and evaluation of supply and demand (J. Liu, Pattey, and Jégo 2012; Wiseman et al 2014; Yue et al 2017). Crop forecasting helps to predict crop yields and production before harvesting, which is an essential factor for national food security, food trade, market management and evaluation of supply and demand Liu, Pattey, and Jégo 2012; Wiseman et al 2014; Yue et al 2017). Information on crop yield can be used to support decisions made by national and regional agencies regarding the allocation of agricultural products and commercial institutions for investment in various sectors (Hoogenboom 2000). Various sensors have unique advantage and cons, using suitable data is very important for modelling and mapping in agricultural lands. Multispectral, SAR and hyperspectral data were used in many studies about agricultural such as biophysical parameters estimation, crop mapping, crop phenology estimation, etc. Wiseman et al (2014) assessed RADARSAT-2 Cband polarimetric SAR for agricultural production monitoring
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