HP-LSP: A reference of land surface phenology from fused Harmonized Landsat and Sentinel-2 with PhenoCam data

Tran, Khuong H.; Zhang, Xiaoyang; Ye, Yongchang; Shen, Yu; Gao, Shuai; Liu, Yuxia; Richardson, Andrew

doi:10.1038/s41597-023-02605-1

Cited by 7 publications

(1 citation statement)

References 77 publications

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“…However, the free availability of 10 m Sentinel-2 data and an advanced processing platform allows for efficient processing of high spatial resolution data, making crop area maps feasible [7,181,182]. The high spatial resolution satellite allows for fewer mixed pixels in these smallholder agricultural landscapes, resulting in mosaics of fields that are often heterogeneously mixed at lower resolution satellite data [183][184][185]. In this study, the high-resolution freely available sentinel data were evaluated, and the performances of commonly used MLAs were tested concerning the small and fragmented farmlands.…”

Section: Discussionmentioning

confidence: 99%

Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia

Mengesha,

Desta,

Gamba

et al. 2024

Land

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In most developing countries, smallholder farms are the ultimate source of income and produce a significant portion of overall crop production for the major crops. Accurate crop distribution mapping and acreage estimation play a major role in optimizing crop production and resource allocation. In this study, we aim to develop a spatio–temporal, multi-spectral, and multi-polarimetric LULC mapping approach to assess crop distribution mapping and acreage estimation for the Oromia Region in Ethiopia. The study was conducted by integrating data from the optical and radar sensors of sentinel products. Supervised machine learning algorithms such as Support Vector Machine, Random Forest, Classification and Regression Trees, and Gradient Boost were used to classify the study area into five first-class common land use types (built-up, agriculture, vegetation, bare land, and water). Training and validation data were collected from ground and high-resolution images and split in a 70:30 ratio. The accuracy of the classification was evaluated using different metrics such as overall accuracy, kappa coefficient, figure of metric, and F-score. The results indicate that the SVM classifier demonstrates higher accuracy compared to other algorithms, with an overall accuracy for Sentinel-2-only data and the integration of optical with microwave data of 90% and 94% and a kappa value of 0.85 and 0.91, respectively. Accordingly, the integration of Sentinel-1 and Sentinel-2 data resulted in higher overall accuracy compared to the use of Sentinel-2 data alone. The findings demonstrate the remarkable potential of multi-source remotely sensed data in agricultural acreage estimation in small farm holdings. These preliminary findings highlight the potential of using multi-source active and passive remote sensing data for agricultural area mapping and acreage estimation.

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