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Open research questions in Remote Sensing and Land Use

149 gap statements mined from Remote Sensing and Land Use papers in our 4.5M-paper local library, which holds 762 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • The original study by Guth et al. relies on subjective estimates of tolerance to determine whether differences between DEMs are significant. The study has a limited sample size, representing parts of the USA and western Europe. The FUV approach proposed by Guth et al. has high sensitivity.

    Comment on Guth et al. Benchmarking Elevation Plus Land Surface Parameters Finds FathomDEM and Copernicus DEM Win as Best Global DEMs. Remote Sens. 2025, 17, 3919 · 2026 · DOI

  • Data processing and validation are challenging due to the complex nature of coastal ecosystems and the need for advanced technologies.

    Remote-Sensing Indicators and Methods for Coastal-Ecosystem Health Assessment: A Review of Progress, Challenges, and Future Directions · 2025 · DOI

  • It also highlights the need to understand the spatiotemporal dynamics and influencing factors of crop phenology.

    Mapping the Main Phenological Spatiotemporal Changes of Summer Maize in the Huang-Huai-Hai Region Based on Multiple Remote Sensing Indices · 2025 · DOI

  • Traditional detection methods are constrained by susceptibility to environmental interference, limited robustness, and inadequate generalization capabilities. Existing deep learning-based detection models face issues such as insufficient feature extraction for small targets and difficulties in deployment due to their need for large numbers of parameters.

    A Lightweight Algorithm for Detection and Grading of Olive Ripeness Based on Improved YOLOv11n · 2025 · DOI

  • Further research can explore the application of UAV multispectral data in other crop types. Future studies can investigate the integration of UAV multispectral data with other remote sensing technologies. Research can focus on developing more advanced machine learning models for wheat growth monitoring.

    Integration of UAV Multispectral Remote Sensing and Random Forest for Full-Growth Stage Monitoring of Wheat Dynamics · 2025 · DOI

  • The dataset size is limited with only 270 samples. The hyperspectral data contains a large number of spectral bands with significant redundancy and interference. The need to prevent data leakage and overfitting caused by small datasets.

    Estimation of Wheat Leaf Water Content Based on UAV Hyper-Spectral Remote Sensing and Machine Learning · 2025 · DOI

  • Traditional crop water measurement methods are labor-intensive and destructive. There is a need for rapid and efficient monitoring of leaf water content in winter wheat. Existing methods may not provide accurate and non-destructive estimation of wheat leaf water content.

    Estimation of Wheat Leaf Water Content Based on UAV Hyper-Spectral Remote Sensing and Machine Learning · 2025 · DOI

  • Precise prediction of soybean yields before harvest remains a formidable challenge. The study mentions the limitations of traditional yield prediction methods.

    Prediction of Soybean Yield at the County Scale Based on Multi-Source Remote-Sensing Data and Deep Learning Models · 2025 · DOI

  • Traditional yield prediction methods can be mainly classified into three types: empirical statistical models, crop growth models, and expert systems. These methods have limitations, and the study aims to address this gap. The research seeks to develop a model that can accurately predict soybean yields on a large scale.

    Prediction of Soybean Yield at the County Scale Based on Multi-Source Remote-Sensing Data and Deep Learning Models · 2025 · DOI

  • Sensor limitations, such as the limited ability to capture traits without spectrally observable properties. Cultivar variability, which can affect the accuracy of RS-based measurements. Confounding environmental factors, such as drought, disease, or nutrient deficiency, which can impact plant vitality and productivity.

    Monitoring Agricultural Land Use Intensity with Remote Sensing and Traits · 2025 · DOI

  • The lack of a comprehensive method for evaluating environmental comfort levels. The need for a method that can accurately assess the interactions among environmental factors.

    Evaluation of Environmental Quality in Northern Winter Fattening Pig Houses Based on AHP-EWM · 2025 · DOI

  • A lack of refined, micro-level studies on heritage canal landscapes from a multidimensional perspective of visual preference. Limited understanding of the relationships between visual preferences and environmental characteristics in heritage canal landscapes.

    Multidimensional Visual Preferences and Sustainable Management of Heritage Canal Waterfront Landscape Based on Panoramic Image Interpretation · 2025 · DOI

  • There is a need to examine the spatial–temporal trends and driving mechanisms of soil salinization for improved resource management. The study identifies a gap in the quantitative analysis of driving factors of soil salinization. The research aims to address the gap by employing a piecewise structural equation model to analyze the driving factors of soil salinization.

    Monitoring of Soil Salinization and Analysis of Driving Factors in the Oasis Zone of South Xinjiang · 2025 · DOI

  • There has been limited attention paid to the assessment of water production and soil conservation at the provincial level. The distribution patterns of ecosystem services under different environmental gradients in hilly regions have not been revealed.

    Evaluate Water Yield and Soil Conservation and Their Environmental Gradient Effects in Fujian Province in South China Based on InVEST and Geodetector Models · 2025 · DOI

  • Traditional yield estimation methods are labor-intensive and time-consuming. They vary depending on the observer. They are not scalable to meet the needs of modern precision agriculture.

    Research on an Apple Recognition and Yield Estimation Model Based on the Fusion of Improved YOLOv11 and DeepSORT · 2025 · DOI

  • Laborers in hilly and mountainous regions have shifted to nonagricultural industries in search of higher-paying jobs, further exacerbating the farmland abandonment problem.

    Monitoring of Farmland Abandonment Based on Google Earth Engine and Interpretable Machine Learning · 2025 · DOI

  • Remote-sensing technology still faces challenges such as sensor limitations, environmental interference, and data processing and validation. The unique atmospheric variations and high water vapor content in coastal areas significantly affect electromagnetic radiation signal transmission quality.

    Remote-Sensing Indicators and Methods for Coastal-Ecosystem Health Assessment: A Review of Progress, Challenges, and Future Directions · 2025 · DOI

  • Further research on the application of the proposed method to other crops - Investigation of the use of other machine learning algorithms for rice LAI and SPAD…

    Rice Growth Parameter Estimation Based on Remote Satellite and Unmanned Aerial Vehicle Image Fusion · 2025 · DOI

  • The high cost of multispectral sensors and the low spatial resolution of satellite remote sensing. The limitations of using only spectral information or textural features to estimate rice growth parameters.

    Rice Growth Parameter Estimation Based on Remote Satellite and Unmanned Aerial Vehicle Image Fusion · 2025 · DOI

  • The study is limited to the hilly and mountainous areas of southern China - The sample points were randomly divided into training (70%) and testing (30%) subsets - The study used a three-year time sliding-window algorithm to determine abandoned…

    Monitoring of Farmland Abandonment Based on Google Earth Engine and Interpretable Machine Learning · 2025 · DOI

  • Emerging technologies such as hyperspectral imaging, solar-induced fluorescence, radar, artificial intelligence, and semantic data integration are discussed as promising pathways - Integrating management practices, plant and soil traits, RS observables, validation needs, and policy…

    Monitoring Agricultural Land Use Intensity with Remote Sensing and Traits · 2025 · DOI

  • The complexity of the XGBoost model, which contains a large number of parameters and connections - The lack of transparency in the prediction process due to the 'black box' nature of machine learning - The need to identify key factors affecting carbon emissions from residential land and to develop an effective strategy to decrease…

    Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHAP Model · 2025 · DOI

  • The XGBoost model has a lack of transparency in the prediction process due to the complexity of the model - The model cannot explain the causal relationships involved, resulting in limited…

    Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHAP Model · 2025 · DOI

  • Investigating spatial differences in landscape preferences among river sections in different urban locations - Exploring the factors influencing visual preferences for heritage canal landscapes - Further research on the relationships between landscape visual preferences and environmental…

    Multidimensional Visual Preferences and Sustainable Management of Heritage Canal Waterfront Landscape Based on Panoramic Image Interpretation · 2025 · DOI

  • Further research may focus on the temporal dynamics in the importance and influence mechanisms of driving factors across different periods, - Constructing two cropland productivity metrics: nutrition-based calorie productivity and cropland economic productivity, - Applying the OPGD-SHAP framework to analyze the drivers of cropland productivity for a more comprehensive understanding of its spatial heterogeneity…

    Analysis of Driving Factors of Cropland Productivity in Northeast China Using OPGD-SHAP Framework · 2025 · DOI

Most-cited papers in Remote Sensing and Land Use

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Open questions raised by papers listed on this page include: (1) Future studies can investigate the integration of UAV multispectral data with other remote sensing technologies (“Integration of UAV Multispectral Remote Sensing and Random Forest for…”, 2025); (2) There is a need for rapid and efficient monitoring of leaf water content in winter wheat (“Estimation of Wheat Leaf Water Content Based on UAV Hyper-Spectral Remote…”, 2025); (3) The lack of a comprehensive method for evaluating environmental comfort levels (“Evaluation of Environmental Quality in Northern Winter Fattening Pig Houses…”, 2025); (4) A lack of refined, micro-level studies on heritage canal landscapes from a multidimensional perspective of visual preference (“Multidimensional Visual Preferences and Sustainable Management of Heritage…”, 2025); (5) There is a need to examine the spatial–temporal trends and driving mechanisms of soil salinization for improved resource management (“Monitoring of Soil Salinization and Analysis of Driving Factors in the Oasis…”, 2025). 149 gap statements have been mined from Remote Sensing and Land Use papers in our 4.5M-paper local library, which holds 762 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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