Abstract
The high temporal resolution (4-day) charge-coupled device (CCD) cameras onboard small environment and disaster monitoring and forecasting satellites (HJ-1A/B) with 30 m spatial resolution and large swath (700 km) have substantially increased the availability of regional clear sky optical remote sensing data. For the application of dynamic mapping of rice growth parameters, leaf area index (LAI) and aboveground biomass (AGB) were considered as plant growth indicators. The HJ-1 CCD-derived vegetation indices (VIs) showed robust relationships with rice growth parameters. Cumulative VIs showed strong performance for the estimation of total dry AGB. The cross-validation coefficient of determination ( R C V 2 ) was increased by using two machine learning methods, i.e., a back propagation neural network (BPNN) and a support vector machine (SVM) compared with traditional regression equations of LAI retrieval. The LAI inversion accuracy was further improved by dividing the rice growth period into before and after heading stages. This study demonstrated that continuous rice growth monitoring over time and space at field level can be implemented effectively with HJ-1 CCD 10-day composite data using a combination of proper VIs and regression models.
Highlights
Rice is recognized as one of the most important crops in the world and the staple food for more than half of the world’s population [1]
Lu made a review of the potential and challenge of remote sensing-based forest biomass estimation; Koppe et al did a study on the estimation of winter wheat biomass using 30 m resolution data from Hyperion on regional scale; and Liu et al
aboveground biomass (AGB), we extracted the value of remote sensing data corresponding to the field campaign date
Summary
Rice is recognized as one of the most important crops in the world and the staple food for more than half of the world’s population [1]. Acquisition of crop information on LAI and AGB is vital for the design of dynamic maps of rice growth parameters. Such data could support agronomic decisions that ensure plant health, yield stability, and optimization of economic benefits. Remote sensing techniques offer a feasible tool for parameter regionalization. Lu made a review of the potential and challenge of remote sensing-based forest biomass estimation; Koppe et al did a study on the estimation of winter wheat biomass using 30 m resolution data from Hyperion on regional scale; and Liu et al
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