Comparisons between temporal statistical metrics, time series stacks and phenological features derived from NASA Harmonized Landsat Sentinel-2 data for crop type mapping

Liu, Xiaomi; Xie, Sujuan; Yang, Jun; Sun, Lin; Liu, Liangyun; Zhang, Qing; Yang, Chenghai

doi:10.1016/j.compag.2023.108015

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…This finding is supported by the results in Figure 6, where the simultaneous use of the five FCs as five distinct branches in the Pa structure yielded the highest accuracy compared to other scenarios. By utilizing multi-source data, the number of satellite observations per cropland increases, providing more information about crops [21]. In other words, multi-source data contain different aspects of crop types, including spectral, phenological, physical, and structural characteristics [2,14].…”

Section: Proposed Pa-pca-ca Structurementioning

confidence: 99%

“…This is because MS satellites have different temporal resolutions, which provide enhanced phenological information and, consequently, lead to improved classification accuracy. The most common combination of MS data involves the synergistic use of S2 and L8/9 due to their similar characteristics [20,21]. Additionally, including S1 images in combination with MS data in classification models has the potential to enhance crop mapping accuracies [22].…”

Section: Introductionmentioning

confidence: 99%

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A Parallel-Cascaded Ensemble of Machine Learning Models for Crop Type Classification in Google Earth Engine Using Multi-Temporal Sentinel-1/2 and Landsat-8/9 Remote Sensing Data

Abdali,

Valadan Zoej,

Taheri Dehkordi

et al. 2023

Remote Sensing

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The accurate mapping of crop types is crucial for ensuring food security. Remote Sensing (RS) satellite data have emerged as a promising tool in this field, offering broad spatial coverage and high temporal frequency. However, there is still a growing need for accurate crop type classification methods using RS data due to the high intra- and inter-class variability of crops. In this vein, the current study proposed a novel Parallel-Cascaded ensemble structure (Pa-PCA-Ca) with seven target classes in Google Earth Engine (GEE). The Pa section consisted of five parallel branches, each generating Probability Maps (PMs) for different target classes using multi-temporal Sentinel-1/2 and Landsat-8/9 satellite images, along with Machine Learning (ML) models. The PMs exhibited high correlation within each target class, necessitating the use of the most relevant information to reduce the input dimensionality in the Ca part. Thereby, Principal Component Analysis (PCA) was employed to extract the top uncorrelated components. These components were then utilized in the Ca structure, and the final classification was performed using another ML model referred to as the Meta-model. The Pa-PCA-Ca model was evaluated using in-situ data collected from extensive field surveys in the northwest part of Iran. The results demonstrated the superior performance of the proposed structure, achieving an Overall Accuracy (OA) of 96.25% and a Kappa coefficient of 0.955. The incorporation of PCA led to an OA improvement of over 6%. Furthermore, the proposed model significantly outperformed conventional classification approaches, which simply stack RS data sources and feed them to a single ML model, resulting in a 10% increase in OA.

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Section: Proposed Pa-pca-ca Structurementioning

confidence: 99%