Crop Area Identification Based on Time Series EVI2 and Sparse Representation Approach: A Case Study in Shandong Province, China

Xun, Lan; Zhang, Jiahua; Cao, Dan; Zhang, Sha; Yao, Fengmei

doi:10.1109/access.2019.2949799

Cited by 6 publications

(1 citation statement)

References 54 publications

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“…Soil Adjusted Vegetation Index (SAVI) [34] Atmospherically Resistant Vegetation Index (ARVI) [45] Transformed Soil Adjusted Vegetation Index (TSAVI) [35,36] Normalized Difference Index (NDI 45) [46] Modified Soil Adjusted Vegetation Index (MSAVI) [37] Meris Terrestrial Chlorophyll Index (MTCI) [47] Second Modified Soil Adjusted Vegetation Index (MSAVI 2) [37] Modified Chlorophyll Absorption Ratio Index (MCARI) [48] Difference Vegetation Index (DVI) [38] Sentinel-2 Red-Edge Position Index (S2REP) [49] Ratio Vegetation Index (RVI) [39] Inverted Red-Edge Chlorophyll Index (IECI) [50] Perpendicular Vegetation Index (PVI) [40] Pigment Specific Simple Ratio (PSSR) [51] Infrared Percentage Vegetation Index (IPVI) [41] Normalized Difference Vegetation Index (NDVI) [52] Weighted Difference Vegetation Index (WDVI) [42] Modified Red Edge Normalized Difference Vegetation Index (NDVI 705 ) [53] Transformed Normalized Difference Vegetation Index (TNDVI) [38] Enhanced Vegetation Index (EVI) [54] Green Normalized Difference Vegetation Index (GNDVI) [43] 2-Band Enhanced Vegetation Index (EVI2) [55] Global Environmental Monitoring Index (GEMI) [44] https://doi.org/10.1371/journal.pone.0272300.t004…”

Section: Vegetation Index Reference Vegetation Index Referencementioning

confidence: 99%

Using time series vector features for annual cultivated land mapping: A trial in northern Henan, China

Zhou²,

Zhang³

et al. 2022

PLoS ONE

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Annual monitoring of the spatial distribution of cultivated land is important for maintaining the ecological environment, achieving a status quo of land resource management, and guaranteeing agricultural production. With the gradual development of remote sensing technology, it has become a common practice to obtain cultivated land boundary information on a large scale with the help of satellite Earth observation images. Traditional land use classification methods are affected by multiple types of land cover, which leads to a decrease in the accuracy of cultivated land mapping. In contrast, although the current advanced methods (such as deep learning) can obtain more accurate cultivated land mapping results than traditional methods, such methods often require the use of a massive amount of training samples, large computing power, and highly complex model tuning processes, increasing the cost of mapping and requiring the involvement of more professionals. This has hindered the promotion of related methods in mapping institutions. This paper proposes a method based on time series vector features (MTVF), which uses vector thinking to establish the features. The advantage of this method is that the introduction of vector features enlarges the differences between the different land cover types, which overcomes the loss of mapping accuracy caused by the influences of the spectra of different ground objects and ensures the calculation efficiency. Moreover, the MTVF uses a traditional method (random forest) as the classification core, which makes the MTVF less demanding than advanced methods in terms of the number of training samples. Sentinel-2 satellite images were used to carry out cultivated land mapping for 2020 in northern Henan Province, China. The results show that the MTVF has the potential to accurately identify cultivated land. Furthermore, the overall accuracy, producer accuracy, and user accuracy of the overall study area and four sub-study areas were all greater than 90%. In addition, the cultivated land mapping accuracy of the MTVF is significantly better than that of the maximum likelihood, support vector machine, and artificial neural network methods.

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