Bhat et al. (2025) Space and Geo-informatics for Climate Smart Agriculture- An Overview

Identification

• Journal: Journal of Advance Agricultural Research
• Year: 2025
• Date: 2025-12-21
• Authors: S. Karthik Bhat, Sameera Qayoom, Aijaz Nazir, Bilal Ahmad Lone, Fayaz Ahmad Bahar, Marifa Gulzar, Bisma Nazir, Chinmayo Sahoo, Nilotpal Das, Atin Kumar
• DOI: 10.65525/jaar.v1i4.21

Research Groups

• Division of Agronomy, SKUAST-K, Srinagar, India
• Division of Agrometeorology, SKUAST-K, Srinagar, India
• Division of Vegetable Science, SKUAST-K, Srinagar, India
• Department of Agronomy, Dr. Rajender Prasad Central Agricultural University Pusa, Samastipur, Bihar, India
• School of Agriculture, Uttaranchal University, Dehradun, Uttarakhand, India
• Faculty of Agriculture Sciences, GLA University, Mathura, Uttar Pradesh, India

Short Summary

This review article synthesizes the role of space technology and geo-informatics in Climate-Smart Agriculture (CSA), demonstrating how Earth observation satellites, remote sensing, GIS, and GPS enhance agricultural productivity, sustainability, and resilience against climate change. It highlights their applications in crop monitoring, resource management, and disaster mitigation, while also addressing implementation challenges.

Objective

• To provide an overview of the role, benefits, and challenges of integrating space technology and geo-informatics in modern Climate-Smart Agriculture (CSA) for enhancing sustainability, productivity, and resilience.

Study Configuration

• Spatial Scale: Global to regional (e.g., India-specific initiatives like ISRO's programs, national-level crop monitoring, district-level yield prediction, field-specific precision agriculture).
• Temporal Scale: Review of literature from 2000 to 2024, with a focus on increased research post-2010, and real-time monitoring capabilities of discussed technologies.

Methodology and Data

• Models used: Crop yield models, climate models, hydrological models, AI-based radar data analysis models, Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Back Propagation Neural Network (BPNN), Deep Convolutional Neural Networks (CNNs), transfer learning, YOLO-like object detectors, logistic regression.
• Data sources: Earth observation satellites (EOS SAT-1, ECOSTRESS, Cartosat, Resourcesat, RISAT, Oceansat, MODIS, INSAT-3D, Sentinel-2, Landsat-8), Global Positioning Systems (GPS), ground sensors, meteorological observations, socioeconomic data, peer-reviewed publications, technical reports, policy documents (FAO, ISRO, NASA).

Main Results

• Space technology and geo-informatics significantly enhance Climate-Smart Agriculture (CSA) across multiple domains, including high-resolution crop mapping and monitoring, soil health assessment, and precision agriculture.
• Earth observation satellites (e.g., EOS SAT-1, ECOSTRESS, ISRO's Cartosat/Resourcesat/RISAT) provide critical data for real-time insights into soil moisture, vegetation health, plant water stress, and all-weather imaging for disaster monitoring.
• Remote sensing, combined with initiatives like India's NADAMS and FASAL projects, enables accurate drought and flood forecasting, early pest and disease detection (e.g., wheat rust prediction with 86.2% accuracy using Sentinel-2 data), and crop yield prediction (e.g., CAPE project achieving 90% accuracy).
• GPS-enabled precision agriculture facilitates site-specific resource management, optimizing fertilizer, pesticide, and water application, thereby reducing waste and environmental impact, and improving water use efficiency.
• Despite advancements, challenges remain, including limited satellite coverage, dependency on clear weather for optical data, moderate spatial resolution of some sensors, high initial costs and technical skill requirements for precision agriculture, and the need for real-time validation and multi-source data integration.
• Quantitative examples from the reviewed literature include glacier thinning from 0.4 m/yr in 1990 to a projected 0.95 m/yr by 2050, sea levels rising from 0.02 m in 1950 to 0.09 m in 2009 (expected 0.16 m by 2050), and freshwater withdrawals increasing from 3.5 x 10^12 m³ in 1990 to 4.43 x 10^12 m³ in 2000.

Contributions

• Provides a comprehensive overview and synthesis of the diverse applications of space technology and geo-informatics in Climate-Smart Agriculture (CSA).
• Systematically categorizes and evaluates the strengths, limitations, and future improvement scopes of key technologies like Earth observation satellites (EOS SAT-1, ECOSTRESS, ISRO missions) and GPS in the context of CSA.
• Highlights specific Indian initiatives (e.g., Cartosat, Resourcesat, RISAT, NADAMS, CAPE, FASAL) as case studies for successful implementation of these technologies.
• Identifies critical research gaps and future directions, emphasizing the need for multi-source data integration, scalability of precision agriculture, accessibility for smallholder farmers, and advanced AI/ML applications.

Funding

No specific funding projects, programs, or reference codes were mentioned in the provided paper text.

Citation

@article{Bhat2025Space,
  author = {Bhat, S. Karthik and Qayoom, Sameera and Nazir, Aijaz and Lone, Bilal Ahmad and Bahar, Fayaz Ahmad and Gulzar, Marifa and Nazir, Bisma and Sahoo, Chinmayo and Das, Nilotpal and Kumar, Atin},
  title = {Space and Geo-informatics for Climate Smart Agriculture- An Overview},
  journal = {Journal of Advance Agricultural Research},
  year = {2025},
  doi = {10.65525/jaar.v1i4.21},
  url = {https://doi.org/10.65525/jaar.v1i4.21}
}