Comparative quality assessment of digital surface models derived from Pléiades Neo and WorldView-3 tri-stereo imagery for high-resolution 3D mapping of plastic greenhouse agricultural landscapes

With the arrival of the new breed of very high-resolution (VHR) satellites, which have stereo imaging capability and a spatial resolution of approximately 0.3 m, the extraction of highly accurate digital surface models (DSMs) has emerged as a real alternative to aerial photogrammetry for monitoring complex agricultural and urban environments. This study evaluates the quality of unfilled DSMs derived from Pléiades Neo (PNEO) tri-stereo images over an intensive plastic-covered greenhouse agricultural landscape in Spain. A stereo triplet of WorldView-3 (WV3) was used as the benchmark. The methodology involved sensor orientation and 3D reconstruction using a semi-global matching (SGM) approach. The quality assessment, including both completeness and vertical accuracy metrics, was conducted across three land cover types: plastic greenhouses (GH), urban (U), and bare soil (BS). A highly accurate LiDAR-derived DSM was used as ground truth. A statistical analysis of the quality of the tri-stereo DSMs was conducted using the type of sensor (PNEO and WV3) and land cover (GH, U, and BS) as the main factors. It revealed that land cover was the primary factor influencing DSM quality, explaining 51.72% of the variance in completeness and 77.26% in vertical random error measured as the normalized median absolute deviation (NMAD). Conversely, the sensor factor did not exert a statistically significant influence (p < 0.05) on either completeness or NMAD. Robust estimators, such as NMAD and Median, demonstrated a better ability to deal with outliers and yielded more homogeneous results. NMAD values of 0.32, 0.49, and 0.16 m were achieved for GH, U, and BS, respectively, while completeness ranged from 95.66% to 98.74% for the three different land covers. These findings demonstrate that the new generation of 0.3 m tri-stereo spatial sensors offers an efficient and accurate solution for generating 3D data, even in vertically complex environments. Furthermore, the results have significant implications for plastic pollution management and environmental policymaking.