Abstract
Abstract. Most traditional methods for rice mapping with remote sensing data are effective when they are applied to the initial growing stage of rice, as the practice of flooding during this period makes the spectral characteristics of rice fields more distinguishable. In this study, we propose a sequential classifier training approach for rice mapping that can be used over the whole growing period of rice for monitoring various growth stages. Rice fields are firstly identified during the initial flooding period. The identified rice fields are used as training data to train a classifier that separates rice and non-rice pixels. The classifier is then used as a priori knowledge to assist the training of classifiers for later rice growing stages. This approach can be applied progressively to sequential image data, with only a small amount of training samples being required from each image. In order to demonstrate the effectiveness of the proposed approach, experiments were conducted at one of the major rice-growing areas in Australia. The proposed approach was applied to a set of multitemporal remote sensing images acquired by the Sentinel-2A satellite. Experimental results show that, compared with traditional spectral-indexbased algorithms, the proposed method is able to achieve more stable and consistent rice mapping accuracies and it reaches higher than 80% during the whole rice growing period.
Highlights
Continued mapping of rice fields with remote sensing data provides critical information for irrigation water budgeting and yield prediction (Kuenzer and Knauer, 2013, Mosleh et al, 2015)
Discriminant rules have been set for the relationships between Land Surface Water Index (LSWI) and biomass-related indices, such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), to distinguish rice fields from other crops
Water surface contributes a significant amount of reflected solar radiance to the sensor. This leads to an increase in Land Surface Water Index (LSWI) and decreases in Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)
Summary
Continued mapping of rice fields with remote sensing data provides critical information for irrigation water budgeting and yield prediction (Kuenzer and Knauer, 2013, Mosleh et al, 2015). Previous studies have shown that rice fields can be identified by detecting their unique optical features during the initial flooding period (Xiao et al, 2005) These spectral-index-based methods have been developed and applied for mapping rice distributions (Li et al, 2016, Zhang et al, 2015, Qin et al, 2015, Jin et al, 2016, Zhou et al, 2016, Dong et al, 2016). Discriminant rules have been set for the relationships between LSWI and biomass-related indices, such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), to distinguish rice fields from other crops. These methods are only applicable to the initial flooding period of rice. We propose a sequential classifier training approach for rice mapping that can be applied to the whole growing period
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