Generation of Spatially and Temporally Fine-Resolution Imagery Using STF Algorithms and CACAO Post-Processing

Abstract

Abstract Spatio-temporal fusion (STF) has been widely used across various remote sensing applications, including environmental monitoring, land cover change detection, and water resource management by integrating multi-sensor data with different spatial and temporal resolutions. The objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring. The study was conducted in a soybean field located in Anseong, South Korea, using Planet SuperDove satellite imagery which has 3 m spatial resolution and near-daily temporal resolution, and Phantom 4 Multispectral Unmanned Aerial Vehicle (UAV) data which has 0.05 m spatial resolution, downscaled to target resolution 0.5 m, and 1–4 week irregular temporal resolution. Relative radiometric normalization was applied, followed by the implementation and comparison of four STF algorithms— Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Fitting, spatial Filtering and residual Compensation (FitFC), Flexible Spatiotemporal Data Fusion (FSDAF), and Variation-based Spatiotemporal Data Fusion (VSDF)—within a 4-fold cross-validation framework. CACAO post-processing was then employed to reconstruct temporally continuous NDVI trajectories, from which growth metrics such as Vegetation Growth Metrics (VGM)85 and VGMmax were derived. The validation results indicated that ESTARFM achieved the highest Normalized Difference Vegetation Index (NDVI) performance among the evaluated algorithms, with an Root Mean Square Error (RMSE) of 0.113 and the Universal Image Quality Index (UIQI) of 0.697, and CACAO further improved the results, with CA-ESTARFM providing the highest NDVI accuracy, with an RMSE of 0.108 and a UIQI of 0.740. NDVI histogram and spatial analyses demonstrated that CA-ESTARFM achieved the most consistent agreement with UAV observations while preserving fine-scale spatial heterogeneity. In addition, intra-field vegetation assessment using VGM85 and VGMmax confirmed that CA-ESTARFM enhanced the reliability of crop growth evaluation compared to simple linear interpolation of UAV observations. The proposed framework demonstrates strong potential for applications in comprehensive crop monitoring, precision agriculture management, and yield forecasting.

Plant phenotyping relevance

衛星・UAV画像の時空間融合アルゴリズムを比較検証し、NDVI軌跡から作物生育指標を抽出する方法が研究の中心であるため、植物フェノタイピング手法として採択する。

Code and data availability

The paper uses Planet and UAV imagery and four STF algorithms, but no authors' own data or code deposit is reported. The URLs in Table 2 are third-party algorithm code repositories (ESTARFM/FSDAF, FitFC, VSDF) from prior work, and Planet Explorer is a generic commercial imagery portal; none is a paper-specific public,

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