# Remote Sensing in Agriculture

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/remote-sensing-in-agriculture/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the use of remote sensing technology, particularly MODIS and Landsat data, for monitoring vegetation dynamics, phenology, and biomass estimation in response to global change and climate variability. The papers also explore the application of machine learning techniques for land cover classification and the assessment of ecological responses to environmental change. |
| Domain | Physical Sciences |
| Field | Environmental Science |
| OpenAlex ID | t10111 |
| Works | 322 |

## Topic papers all

Showing 15 of 322.

- [Plant phenology and global climate change: Current progresses and challenges](https://scholariq.org/papers/plant-phenology-and-global-climate-change-current-progresses-and-challenges/)
- [Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture](https://scholariq.org/papers/recent-advances-of-hyperspectral-imaging-technology-and-applications-in/)
- [Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review](https://scholariq.org/papers/support-vector-machine-versus-random-forest-for-remote-sensing-image/)
- [Machine Learning Applications for Precision Agriculture: A Comprehensive Review](https://scholariq.org/papers/machine-learning-applications-for-precision-agriculture-a-comprehensive-review/)
- [Deep learning based multi-temporal crop classification](https://scholariq.org/papers/deep-learning-based-multi-temporal-crop-classification/)
- [Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation](https://scholariq.org/papers/estimating-chlorophyll-content-from-hyperspectral-vegetation-indices-modeling/)
- [Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives](https://scholariq.org/papers/unmanned-aerial-vehicle-remote-sensing-for-field-based-crop-phenotyping-current/)
- [Monitoring plant diseases and pests through remote sensing technology: A review](https://scholariq.org/papers/monitoring-plant-diseases-and-pests-through-remote-sensing-technology-a-review/)
- [Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art](https://scholariq.org/papers/multisource-and-multitemporal-data-fusion-in-remote-sensing-a-comprehensive/)
- [Leaf onset in the northern hemisphere triggered by daytime temperature](https://scholariq.org/papers/leaf-onset-in-the-northern-hemisphere-triggered-by-daytime-temperature/)
- [How deep learning extracts and learns leaf features for plant classification](https://scholariq.org/papers/how-deep-learning-extracts-and-learns-leaf-features-for-plant-classification/)
- [Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau](https://scholariq.org/papers/altitude-and-temperature-dependence-of-change-in-the-spring-vegetation-green-up/)
- [An advanced deep learning models-based plant disease detection: A review of recent research](https://scholariq.org/papers/an-advanced-deep-learning-models-based-plant-disease-detection-a-review-of/)
- [The Visual Object Tracking VOT2017 Challenge Results](https://scholariq.org/papers/the-visual-object-tracking-vot2017-challenge-results/)
- [Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV)](https://scholariq.org/papers/assessment-of-vineyard-water-status-variability-by-thermal-and-multispectral/)

## Topic primary papers

Showing 15 of 99.

- [Plant phenology and global climate change: Current progresses and challenges](https://scholariq.org/papers/plant-phenology-and-global-climate-change-current-progresses-and-challenges/)
- [Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture](https://scholariq.org/papers/recent-advances-of-hyperspectral-imaging-technology-and-applications-in/)
- [Deep learning based multi-temporal crop classification](https://scholariq.org/papers/deep-learning-based-multi-temporal-crop-classification/)
- [Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation](https://scholariq.org/papers/estimating-chlorophyll-content-from-hyperspectral-vegetation-indices-modeling/)
- [Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives](https://scholariq.org/papers/unmanned-aerial-vehicle-remote-sensing-for-field-based-crop-phenotyping-current/)
- [Monitoring plant diseases and pests through remote sensing technology: A review](https://scholariq.org/papers/monitoring-plant-diseases-and-pests-through-remote-sensing-technology-a-review/)
- [Leaf onset in the northern hemisphere triggered by daytime temperature](https://scholariq.org/papers/leaf-onset-in-the-northern-hemisphere-triggered-by-daytime-temperature/)
- [Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau](https://scholariq.org/papers/altitude-and-temperature-dependence-of-change-in-the-spring-vegetation-green-up/)
- [Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV)](https://scholariq.org/papers/assessment-of-vineyard-water-status-variability-by-thermal-and-multispectral/)
- [Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai-Tibetan Plateau](https://scholariq.org/papers/influences-of-temperature-and-precipitation-before-the-growing-season-on-spring/)
- [Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery](https://scholariq.org/papers/efficient-corn-and-soybean-mapping-with-temporal-extendability-a-multi-year/)
- [Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging](https://scholariq.org/papers/identification-of-yellow-rust-in-wheat-using-in-situ-spectral-reflectance/)
- [Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories](https://scholariq.org/papers/integration-of-optical-and-synthetic-aperture-radar-sar-imagery-for-delivering/)
- [LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes](https://scholariq.org/papers/less-large-scale-remote-sensing-data-and-image-simulation-framework-over/)
- [A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images](https://scholariq.org/papers/a-deep-learning-based-approach-for-automated-yellow-rust-disease-detection-from/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.