As satellite observation technology develops and the number of Earth observation (EO) satellites increases, satellite observations have become essential to developments in the understanding of the Earth and its environment. However, the current impacts to the remote sensing community of different EO satellite data and possible future trends of EO satellite data applications have not been systematically examined. In this paper, we review the impacts of and future trends in the use of EO satellite data based on an analysis of data from 15 EO satellites whose data are widely used. Articles that reference EO satellite missions included in the Web of Science core collection for 2020 were analyzed using scientometric analysis and meta-analysis. We found the following: (1) the number of publications and citations referencing EO satellites is increasing exponentially; however, the number of articles referencing AVHRR, SPOT, and TerraSAR is tending to decrease; (2) papers related to EO satellites are concentrated in a small number of journals: 43.79% of the articles that were reviewed were published in only 13 journals; and (3) remote sensing impact factor (RSIF), a new impact index, was constructed to measure the impacts of EO satellites and to predict future trends in applications of their data. Landsat, Sentinel, MODIS, Gaofen, and WorldView were found to be the most significant current EO satellite missions and MODIS data to have the widest range of applications. Over the next five years (2021–2025), it is expected that Sentinel will become the satellite mission with the greatest influence.

The growing population in China has led to an increasing importance of crop area (CA) protection. A powerful tool for acquiring accurate and up-to-date CA maps is automatic mapping using information extracted from high spatial resolution remote sensing (RS) images. RS image information extraction includes feature classification, which is a long-standing research issue in the RS community. Emerging deep learning techniques, such as the deep semantic segmentation network technique, are effective methods to automatically discover relevant contextual features and get better image classification results. In this study, we exploited deep semantic segmentation networks to classify and extract CA from high-resolution RS images. WorldView-2 (WV-2) images with only Red-Green-Blue (RGB) bands were used to confirm the effectiveness of the proposed semantic classification framework for information extraction and the CA mapping task. Specifically, we used the deep learning framework TensorFlow to construct a platform for sampling, training, testing, and classifying to extract and map CA on the basis of DeepLabv3+. By leveraging per-pixel and random sample point accuracy evaluation methods, we conclude that the proposed approach can efficiently obtain acceptable accuracy (Overall Accuracy = 95%, Kappa = 0.90) of CA classification in the study area, and the approach performs better than other deep semantic segmentation networks (U-Net/PspNet/SegNet/DeepLabv2) and traditional machine learning methods, such as Maximum Likelihood (ML), Support Vector Machine (SVM), and RF (Random Forest). Furthermore, the proposed approach is highly scalable for the variety of crop types in a crop area. Overall, the proposed approach can train a precise and effective model that is capable of adequately describing the small, irregular fields of smallholder agriculture and handling the great level of details in RGB high spatial resolution images.

The greenhouse is the fastest growing food production approach and has become the symbol of protected agriculture with the development of agricultural modernization. Previous studies have verified the effectiveness of remote sensing techniques for mono-temporal greenhouse mapping. In practice, long-term monitoring of greenhouse from remote sensing data is vital for the sustainable management of protected agriculture and existing studies have been limited in understanding its spatiotemporal dynamics. This study aimed to generate multi-temporal greenhouse maps in a typical protected agricultural region (Shouguang region, north China) from 1990 to 2018 using Landsat imagery and the Google Earth Engine and quantify its spatiotemporal dynamics that occur as a consequence of the development of protected agriculture in the study area. The multi-temporal greenhouse maps were produced using random forest supervised classification at seven-time intervals, and the overall accuracy of the results greater than 90%. The total area of greenhouses in the study area expanded by 1061.94 ${\mathrm{km}}^2$ from 1990 to 2018, with the largest growth occurring in 1995–2010. And a large number of increased greenhouses occurred in 10–35 km northwest and 0–5 km primary roads buffer zones. Differential change trajectories between the total area and number of patches of greenhouses were revealed using global change metrics. Results of five landscape metrics showed that various landscape patterns occurred in both spatial and temporal aspects. According to the value of landscape expansion index in each period, the growth mode of greenhouses was from outlying to edge-expansion and then gradually changed to infilling. Spatial heterogeneity, which measured by Shannon’s entropy, of the increased greenhouses was different between the global and local levels. These results demonstrated the advantage of utilizing Landsat imagery and Google Earth Engine for monitoring the development of greenhouses in a long-term period and provided a more intuitive perspective to understand the process of this special agricultural production approach than relevant social science studies.

Black soil is fertile, abundant with organic matter (OM) and is exceptional for farming. The black soil zone in northeast China is the third-largest black soil zone globally and produces a quarter of China’s commodity grain. However, the soil organic matter (SOM) in this zone is declining, and the quality of cultivated land is falling off rapidly due to overexploitation and unsustainable management practices. To help develop an integrated protection strategy for black soil, this study aimed to identify the primary factors contributing to SOM degradation. The geographic detector, which can detect both linear and nonlinear relationships and the interactions based on spatial heterogeneous patterns, was used to quantitatively analyze the natural and anthropogenic factors affecting SOM concentration in northeast China. In descending order, the nine factors affecting SOM are temperature, gross domestic product (GDP), elevation, population, soil type, precipitation, soil erosion, land use, and geomorphology. The influence of all factors is significant, and the interaction of any two factors enhances their impact. The SOM concentration decreases with increased temperature, population, soil erosion, elevation and terrain undulation. SOM rises with increased precipitation, initially decreases with increasing GDP but then increases, and varies by soil type and land use. Conclusions about detailed impacts are presented in this paper. For example, wind erosion has a more significant effect than water erosion, and irrigated land has a lower SOM content than dry land. Based on the study results, protection measures, including conservation tillage, farmland shelterbelts, cross-slope ridges, terraces, and rainfed farming are recommended. The conversion of high-quality farmland to non-farm uses should be prohibited.

Agricultural land abandonment and retirement are important and lead to different types of land use and cover change. Generally, abandonment and retirement are caused by different social and environmental factors and result in different ecological and economic benefits and costs. Faced with the complexity of agricultural land change over time, this study aims to develop a new framework to distinguish between agricultural land abandonment and retirement and detect the extent and exact year of abandonment and retirement using Google Earth Engine (GEE). We tested our approach for three typical regions in the northern China crop-pasture band, where agricultural land abandonment and retirement are widespread. First, based on the spectral features obtained from Landsat images, annual land-cover maps were obtained with sample migration and random forest from 1998 to 2019 (0.87 overall accuracy). Second, a temporal consistency check method was proposed to further improve the classification performance (0.92 overall accuracy). Third, a trajectory-based change detection approach was developed to identify abandonment and retirement (F1 score for abandonment: 0.74, retirement: 0.83). Our results indicate that the spatiotemporal patterns of abandonment and retirement in the study area greatly differed. Overlapping of the topography and climate data showed that agricultural land with steep slopes ( $> 10^{\circ }$ ) was more likely to be retired and that abandonment was more likely to occur in areas with less precipitation. Overall, the methods used herein are robust for agricultural land abandonment and retirement monitoring and may be extended to other land-cover change studies.

Hyperspectral target detection (HTD) identifies objects of interest from complex backgrounds at the pixel level, playing a vital role in Earth observation. However, the limited target priors constrain the ability to obtain sufficient features or patterns for background-target discrimination, and spectral variation further exacerbates the difficulty of achieving reliable and robust performance. To address these challenges, this paper proposes an efficient self-supervised HTD method with a pyramid state space model (SSM), named HTD-Mamba, which employs spectrally contrastive learning to distinguish between target and background based on the similarity measurement of intrinsic features. Specifically, to obtain sufficient training samples and leverage spatial contextual information, we propose a spatial-encoded spectral augmentation technique that encodes all surrounding pixels within a patch into a transformed view of the center pixel. Additionally, to explore global band correlations, we divide pixels into continuous group-wise spectral embeddings and introduce Mamba to HTD for the first time to model long-range dependencies of the spectral sequence with linear complexity. Furthermore, to alleviate spectral variation and enhance robust representation, we propose a pyramid SSM as a backbone to capture and fuse multiresolution spectral-wise intrinsic features. Extensive experiments conducted on four public datasets demonstrate that the proposed method outperforms state-of-the-art methods in both quantitative and qualitative evaluations. Code is available at https://github.com/shendb2022/HTD-Mamba.