Discrimination and monitoring of rice cultural types using dense time series of Sentinel-1 SAR data

Sah, Sonam; Haldar, Dipanwita; Chandra, Subhash; Nain, A. S.

doi:10.1016/j.ecoinf.2023.102136

Cited by 6 publications

(2 citation statements)

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“…The Enhanced Vegetation Index (EVI) is a remotely sensed index for the assessment of surface vegetation cover and growth. It is an improved vegetation index that overcomes the limitations of traditional vegetation indices under high vegetation cover and atmospheric conditions [47]. Compared with the NDVI, the EVI has better sensitivity and dynamic range under high vegetation cover.…”

Section: Sensitivity Analysis Of Vegetation Indicesmentioning

confidence: 99%

“…The validity of the decision tree model for the extraction of fruit trees was confirmed through field survey data and area statistics. However, SAR data can be influenced by topographic factors, and the backscattering coefficients of various features may be relatively similar due to the varying surface properties of lowlands and highlands and the disparate reflection and absorption characteristics of electromagnetic waves [47]. Identifying different features in complex terrain areas using SAR data alone is difficult.…”

Section: Production Of Decision Treementioning

confidence: 99%

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A Novel Approach to Mapping the Spatial Distribution of Fruit Trees Using Phenological Characteristics

Han,

Wang,

et al. 2024

Agronomy

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The lack of high-spectral and high-resolution remote sensing data is impeding the differentiation of various fruit tree species that share comparable spectral and spatial features, especially for evergreen broadleaf trees in tropical and subtropical areas. Here, we propose a novel decision tree approach to map the spatial distribution of fruit trees at a 10 m spatial resolution based on the growth stage features extracted from Sentinel-1A (S-1A) time-series synthetic aperture radar (SAR) data. This novel method was applied to map the spatial distribution of fruit trees in Maoming City, which is known for its vast cultivation of fruit trees, such as litchi, citrus, and longan. The results showed that the key to extracting information on the distribution of fruit trees lies in the fact that the fruit ripening and expansion period attenuates the information on the vegetation of fruit trees, a characteristic of the reproductive period. Under VH polarization, different fruit tree growth stage traits were more separable and easier to distinguish. The optimal features, such as Hv (high valley value of the 14 May, 26 May, and 7 June SAR data), Tb (difference between the 7 June and 14 January SAR data), Cr (high valley value of the 13 July, 25 July, and 6 August SAR data), and Lo (high valley value of the 23 September, 17 October, and 11 November SAR data), were constructed based on the optimal window. The thresholds for these features were set to 1, 1, 1.5, and 1, respectively. The classification model can effectively distinguish different fruit trees and extract distribution information with overall accuracy (OA) of 90.34% and a Kappa coefficient of 0.84. The proposed method extracts the spatial distribution information of different fruit trees more accurately and provides a reference for the extraction of more tropical and subtropical species.

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Section: Sensitivity Analysis Of Vegetation Indicesmentioning

confidence: 99%

Section: Production Of Decision Treementioning

confidence: 99%