While satellite remote sensing offers widely accessible spectral images at a medium and low spatial resolution (10-30 m resolution) from platforms like Sentinel-2, Landsat 8 OLI and TIRS, Landsat ETM+, and Moderate-resolution Imaging Spectroradiometer (MODIS), this resolution is often insufficient for capturing the detailed spatial distribution of spectral properties. Moreover, satellite images are frequently disrupted by clouds, causing spatiotemporal discontinuities. These gaps in spatial information likely impede the accurate estimation of filed-scale \(V^{25}_{cmax}\). In contrast, the unmanned aerial vehicle (UAV) can capture high-resolution images even in cloudy conditions, offering better capability to describe the spatial distribution of spectral information. Therefore, UAV imagery holds great potential for extracting valuable spatial information to enhance field-scale \(V^{25}_{cmax}\) estimation. Due to the high-dimensional characteristics of UAV imagery, extracting meaningful information using traditional methods poses challenges. There are several approaches to extracting the spatial information from different perspectives. The standard deviation of the pixel values is the most straightforward indicator and is commonly used to quantify landscape heterogeneity [26]. However, it fails to provide other valuable information, such as the spatial distribution patterns. Texture analysis is a widely used approach in image processing. Texture is defined as the measure of coarseness, contrast, directionality, line-likeness, regularity, and roughness [27]. Various texture feature extraction methods have been proposed, including statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropy-based approaches [28]. Among these, the gray-level co-occurrence matrix (GLCM) is the most commonly used method [29] and has been widely applied in quantifying landscape patterns [30], as well as estimating various properties on underlying surfaces, such as leaf area index and biomass, from remote sensing images [31; 32]. Other texture features, such as Fourier spectrum texture [33] and local binary pattern (LBP) texture [34; 35], have also shown strong performance in quantifying specific landscape features, including leaf area index and orchard citrus fruits [36; 37]. Despite the explicit and interpretable information extracted through texture analysis, this method may overlook large potential information in the raw images. The breakthrough in deep learning techniques, especially convolutional neural networks (CNNs), vastly enhances the capacity to extract image information. CNN utilizes the convolution and pooling operation to automatically detect the patterns across the entire image and thus can make full use of image information. CNN became a hot spot for its unparalleled performance regarding the image recognition issue [38]. Inspired by its success, the CNN technique is also employed to interpret remote sensing images. Notably, the combination of CNN and high-resolution UAV imagery shows a powerful capacity for retrieving valuable variables, such as crop yield [39] and leaf area index [40]. A few pioneering works have explored the retrieval of photosynthesis traits from images. [PERSON] et al. [41] utilized several texture features, including the GLCM texture feature, LBP texture feature, and wavelet transform texture features, to monitor the leaf-scale net photosynthetic rate of potatoes. [PERSON] et al. [42] proposed the optimal deep learning architecture to estimate the leaf-scale \(V^{25}_{cmax}\) from hyperspectral images. These works suggest the potential of images for accurate estimation of photosynthetic traits. However, to the best of our knowledge, few studies have examined the effect of spatial information on field-scale \(V^{25}_{cmax}\) estimation.