**1. Introduction**

Karst land landscape accounts for about 12% of the global land area, and the environment is very fragile [1,2]. Karst landform accounts for more than 1/3 of China's land area [3], with strong karstification, which has always been a research focus [4–6]. In view of the increasingly prominent ecological problems in karst areas, the pressure of land use change on ecology gradually emerged. Therefore, it is of great significance to obtain more efficient and accurate land use classification methods for optimizing the allocation of land resources and realizing ecological restoration in fragile karst mountainous areas.

**Citation:** Zhang, Y.; Shen, C.; Zhou, S.; Yang, R.; Luo, X.; Zhu, G. Applicability Analysis of GF-2PMS and PLANETSCOPE Data for Ground Object Recognition in Karst Region. *Land* **2023**, *12*, 33. https:// doi.org/10.3390/land12010033

Academic Editor: Giuseppe Modica

Received: 7 November 2022 Revised: 7 December 2022 Accepted: 12 December 2022 Published: 22 December 2022

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

High-spatial-resolution data can provide rich spatial information, geometric structure, and texture information of ground objects and other details. High-temporal-resolution data can provide continuous changes of surface elements in time and space and play an irreplaceable role in regional ecological environment monitoring [7]. However, due to the limitations of satellite launch cost, technical conditions, and satellite revisit cycle, the remote sensing image of a single satellite has the problem of mutual restriction between spatial resolution and temporal resolution [8–12]. As a result, the accuracy of land use classification is not high, which limits the practical application of remote sensing data [9,13,14]. At the same time, optical remote sensing images are easily affected by atmospheric conditions such as clouds, which reduces the availability of data and further hinders the acquisition of time-continuous high-spatial-resolution images [15]. A cost-effective way to solve this problem is to develop a data fusion model. High-temporal and low-resolution data are combined with high-resolution and low-temporal data to obtain remote sensing images with high spatial resolution and high temporal resolution [16–18].

There are five main categories of spatiotemporal fusion algorithms: decompositionbased methods, weight-function-based methods, Bayesian-based methods, learning-based methods, and hybrid-based methods [19]. Decomposition-based methods employ linear spectral mixing theory in analyzing the composition of coarse pixels and decomposing them to estimate the value of fine pixels, including algorithms such as STDFA. This type of algorithm is simple in principle and easy to operate, but it cannot obtain good decomposition results in mixed areas with many land cover types [20]. The method based on the weight function estimates the fine pixel value by combining the information of all input images with the weight function, mainly including STARFM, STAARCH, ESTARFM, and other algorithms. Most of these methods involve empirical functions, and the fusion accuracy is poor when there are too many types of land cover or when abnormal changes such as land cover mutation occur [21]. The Bayesian-based data fusion method combines the time-related information in the image time series to transform the fusion problem into an estimation problem, mainly including BME, unified, and other algorithms. These methods lead to lower prediction accuracy when the land cover type changes [22,23]. Learningbased methods use machine learning to model the relationships between observed image pairs and predict unobserved images, mainly including algorithms such as SPSTFM and EBSPTM. This type of method can capture the main features in the prediction, including land cover type changes, etc., but cannot accurately preserve the shape of the predicted objects, especially irregular-shaped ground objects [24]. There are also some spatiotemporal fusion methods that combine the advantages of decomposition methods, Bayesian theory, weight functions, and learning methods to pursue better fusion effects, such as FSDAF algorithms. This type of method can deal with different land cover type change problems through the combination of multiple methods, which improves the prediction accuracy of the model, but also increases the complexity of the algorithm [25]. In addition, Wang et al. proposed a method by combining regression model fitting, spatial filtering, and residual compensation [26]. This method has some shortcomings in capturing image structure and texture, but it has a good fusion effect when the terrain changes greatly. It has great application value for remote monitoring of environment, agriculture, and ecology [27].

At present, most of the spatiotemporal fusion algorithms use Landsat data and MODIS, MERIS, and other medium- and low-spatial-resolution data for fusion, meaning that the fused data are far from fulfilling the actual needs. With the development of satellite technology and the improvements in sensor technology, the demand for high spatial resolution is increasing. However, research on spatiotemporal fusion using high-resolution images is scarce; in particular, the accuracy of the high-resolution fusion images is unknown [28–32]. At the same time, there is no research on the fusion accuracy of different models in different land use types in the current spatiotemporal fusion research. Therefore, this study will fill the gap in the current field of spatiotemporal fusion to facilitate better use of satellite remote sensing data.

As the images of PS satellite constellation have high temporal resolution and high spatial resolution, GF-2 has the highest resolution among Chinese civil land observation satellites. Therefore, in this paper, FSDAF, STDFA, and Fit\_FC models are used to fuse high-spatial-resolution GF-2 and high-temporal-resolution PS data, and the fusion accuracy of each model is analyzed at the same time. This provides a new idea for fine classification of land use in karst areas, and analyzes the applicability of GF-2 and PS data for feature recognition in the Karst region. This can provide a scientific basis for further application research based on high-spatial-resolution satellites such as time series GF-2.

#### **2. Materials and Methods**
