Open research questions in Remote Sensing in Agriculture

What the literature leaves open

• The commonly used global linear regression model has limitations in capturing the complex relationship between spectral diversity and biodiversity.

  Geographically Weighted Regression Enhances Spectral Diversity–Biodiversity Relationships in Inner Mongolian Grasslands · 2025 · DOI

• The need to analyze the impact of land use change on vegetation vulnerability shift.

  Prediction and Spatiotemporal Transfer of Vegetation Vulnerability in the South African Coastal Zone Under Different Shared Socioeconomic Pathway (SSP) Scenarios · 2025 · DOI

• It highlights the need for more efficient and effective methods for land cover legend harmonization. The paper also discusses the challenge of ensuring the accuracy and reliability of land cover maps, particularly in the context of high-resolution mapping.

  High-Resolution Global Land Cover Maps and Their Assessment Strategies · 2025 · DOI

• The immense computational demands of large-scale LC mapping can be a limitation.

  High-Resolution Global Land Cover Maps and Their Assessment Strategies · 2025 · DOI

• The challenge of developing a predictive model that can accurately estimate grain yield and protein concentration using multispectral UAV monitoring and machine learning. The need to account for the effects of nitrogen fertilization on plant development and kernel protein concentration.

  Durum Wheat (Triticum durum Desf.) Grain Yield and Protein Estimation by Multispectral UAV Monitoring and Machine Learning Under Mediterranean Conditions · 2025 · DOI

• There is little research related to the prediction of the N nutritional status and its effects on the productivity of durum wheat grown in the Mediterranean environment. The application of multispectral UAV monitoring and machine learning techniques to predict grain yield and protein concentration is a gap in current research.

  Durum Wheat (Triticum durum Desf.) Grain Yield and Protein Estimation by Multispectral UAV Monitoring and Machine Learning Under Mediterranean Conditions · 2025 · DOI

• The need for high-resolution information on soil physical and chemical parameters for detailed decision making. The lack of effective methods to monitor soil fertility changes and optimize fertilization practices.

  Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru · 2025 · DOI

• Limited availability of consistent vegetation index data for crop monitoring and yield estimation. Need for an alternative method for predicting cotton yield maps in large-scale production systems.

  Cotton Yield Map Prediction Using Sentinel-2 Satellite Imagery in the Brazilian Cerrado Production System · 2025 · DOI

• The methodology using a UAV is not accurate for gaps smaller than <1 m. The study did not consider other factors that may affect sugarcane regrowth and productivity. The study was conducted in a specific region and the results may not be applicable to other regions.

  RGB Composition Obtained by a UAV in the Monitoring of Sugarcane Row Gaps Using the Biophysical Index · 2025 · DOI

• The need for a more accurate and efficient method for classifying agronomic traits of soybean genotypes. The lack of research on the application of machine learning models to multispectral variables acquired at different phenological stages.

  High-Precision Phenotyping in Soybeans: Applying Multispectral Variables Acquired at Different Phenological Stages · 2025 · DOI

• The gap in the current methods is the lack of cost-effective alternatives for creating management zones. The study identifies the need for a more detailed understanding of the impact of spatial resolution on classifying multi-temporal vegetation indices.

  Multi-Temporal Normalized Difference Vegetation Index Based on High Spatial Resolution Satellite Images Reveals Insight-Driven Edaphic Management Zones · 2025 · DOI

• The need to understand the conditions influencing high temperature-induced rice sterility. The lack of effective countermeasures to address the issue. The potential of using satellite images and machine learning to estimate sterility rates.

  Mapping Paddy Fields Using Satellite Images and Machine Learning to Identify High Temperature-Induced Sterility in Nankoku, Japan · 2025 · DOI

• The lack of comparative studies on the accuracy of remote sensing platforms for detecting nitrogen doses in maize crops. The need to determine the optimal vegetation index and timing for detecting nitrogen doses using the platforms.

  Comparative Evaluation of the Multispectral Platforms Sentinel-2, CBERS-04A, and UAV for Nitrogen Detection in Maize Crops · 2025 · DOI

• The lack of automated and data-driven technologies to predict and respond to apple growth conditions. The need for efficient and accurate methods to predict fruit diameter and length, which are critical growth indicators. The challenge of developing models that can handle the complexities of apple growth and development.

  Development of Prediction Models for Apple Fruit Diameter and Length Using Unmanned Aerial Vehicle-Based Multispectral Imagery · 2025 · DOI

• Limited accuracy in predicting canopy height and crude protein concentrations. High biomass or exposed soil can decrease the sensitivity of vegetation indices such as NDVI. Need for more accurate and robust models for predicting structural traits and chemical composition of Urochloa decumbens.

  Predicting Structural Traits and Chemical Composition of Urochloa decumbens Using Aerial Imagery and Machine Learning · 2025 · DOI

• Mountain canopies do not follow a single short-term trajectory after compound hot-dry events. Their next observed greenness state is embedded in phenological phase, recent canopy condition, hydroclimatic exposure, and terrain, all of which vary sharply across the Qinling Mountains. Compound events combine thermal and moisture stress in ways that need not be represented by either driver alone.

  LCRA predicts event-aligned canopy responses across unseen Qinling landscapes · 2026 · DOI

• The immediate scientific need is therefore predictive and spatial: identify the subsequent canopy-greenness window for locations not used to fit the model and resolve which vegetation and landscape contexts organize that movement. Global assessments similarly connect vegetation stability to water availability and to the response definition used for evaluation.

  LCRA predicts event-aligned canopy responses across unseen Qinling landscapes · 2026 · DOI

• Investigating the use of higher-resolution hyperspectral data to alleviate the effect of limited spectral information. Examining the underlying ecological patterns that may cause the inverted relationship between biodiversity and SD. Developing more accurate models that can explain a larger proportion of the variance in biodiversity.

  Geographically Weighted Regression Enhances Spectral Diversity–Biodiversity Relationships in Inner Mongolian Grasslands · 2025 · DOI

• The number of manually delineated ROIs was relatively limited (175 ROIs), - The study was conducted in a single location (central Louisiana) and may not be generalizable to other regions, - The experiment was designed as a randomized complete block with four replications, which may not be sufficient to capture all sources…

  Integrating UAV-based multispectral imagery and machine learning to classify glyphosate-resistant palmer amaranth and assess herbicide program efficacy in soybean · 2026 · DOI

• Further research is needed to evaluate the effectiveness of UAV-based multispectral imagery and machine learning in different environments and locations, - Studies could investigate the use of other machine learning algorithms for weed classification, - Research could focus on integrating UAV-based multispectral imagery with other data sources, such as soil or weather data, to improve weed…

  Integrating UAV-based multispectral imagery and machine learning to classify glyphosate-resistant palmer amaranth and assess herbicide program efficacy in soybean · 2026 · DOI

• The study identifies the challenge of accurately measuring sugarcane row gaps using UAV images, particularly for small gaps. The study also highlights the challenge of comparing UAV measurements with manual measurements, given the differences in measurement methodologies.

  RGB Composition Obtained by a UAV in the Monitoring of Sugarcane Row Gaps Using the Biophysical Index · 2025 · DOI

• The cost of soil laboratory analyses and proximal sensor systems often hinders the practical use of these approaches in developing nations, - The study area was limited to an area of ~4 ha cropland and an area of ~2 ha orchard, - The study was conducted in a specific region (western Türkiye) with a hot-summer Mediterranean (Csa) climate…

  Multi-Temporal Normalized Difference Vegetation Index Based on High Spatial Resolution Satellite Images Reveals Insight-Driven Edaphic Management Zones · 2025 · DOI

• The high cloud cover characteristic of the Amazon limited the number of available images, - The limited number of images available during the experimental…

  Spatiotemporal Characterization of Solar Radiation in a Green Dwarf Coconut Intercropping System Using Tower and Remote Sensing Data · 2025 · DOI

• Further research on the use of satellite images for estimating radiation balance components, - Investigation of the effects of cloud cover on radiation…

  Spatiotemporal Characterization of Solar Radiation in a Green Dwarf Coconut Intercropping System Using Tower and Remote Sensing Data · 2025 · DOI

• Multispectral sensor limitations were highlighted, - The need for hyperspectral sensors was suggested, - Limited accuracy for canopy height and crude protein…

  Predicting Structural Traits and Chemical Composition of Urochloa decumbens Using Aerial Imagery and Machine Learning · 2025 · DOI

Most-cited papers in Remote Sensing in Agriculture

• The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019 · Earth system science data · 2021 · 3,501 citations
• A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production · BioScience · 2004 · 2,050 citations
• Review on Convolutional Neural Networks (CNN) in vegetation remote sensing · ISPRS Journal of Photogrammetry and Remote Sensing · 2021 · 1,753 citations
• A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing · Journal of Forestry Research · 2020 · 1,547 citations
• Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine · Remote Sensing · 2020 · 1,472 citations
• Applications of Remote Sensing in Precision Agriculture: A Review · Remote Sensing · 2020 · 1,415 citations
• Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture · Remote Sensing · 2020 · 1,231 citations
• GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery · Earth system science data · 2021 · 999 citations
• A unified vegetation index for quantifying the terrestrial biosphere · Science Advances · 2021 · 741 citations
• Landsat's Role in Ecological Applications of Remote Sensing · BioScience · 2004 · 623 citations

Most recent work

• AgriFM: A multi-source temporal remote sensing foundation model for Agriculture mapping · Remote Sensing of Environment · 2026
• The global extent of the grassland biome and implications for the terrestrial carbon sink · Nature Ecology & Evolution · 2026
• Artificial intelligence in plant science: from image-based phenotyping to yield and trait prediction · Frontiers in Plant Science · 2026
• Plant stress detection using multimodal imaging and machine learning: from leaf spectra to smartphone applications · Frontiers in Plant Science · 2026
• A CNN-Transformer Hybrid Network With Boundary Guidance for Mapping Cropland Field Parcels From High-Resolution Remote Sensing Imagery · IEEE Transactions on Geoscience and Remote Sensing · 2026
• Evaluation of the MODIS-based vegetation fractional cover product · CSIRO · 2026
• Satellite Remote Sensing for Crop Yield Prediction: A Review · Agriculture · 2026
• Epistemic and aleatoric uncertainty in optical vegetation trait retrieval: Concepts, Methods, and Outlook · ISPRS Journal of Photogrammetry and Remote Sensing · 2026
• Enhanced forest management rather than afforestation has dominated China’s carbon sink over recent decades · Communications Earth & Environment · 2026
• A radiometrically and spatially consistent super-resolution framework for Sentinel-2 · Remote Sensing of Environment · 2026

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