Integration of Hyperspectral Imaging and AI Techniques for Crop Type Mapping: Present Status, Trends, and Challenges

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(9), P. 1574 - 1574

Published: April 29, 2025

Accurate and efficient crop maps are essential for decision-makers to improve agricultural monitoring management, thereby ensuring food security. The integration of advanced artificial intelligence (AI) models with hyperspectral remote sensing data, which provide richer spectral information than multispectral imaging, has proven highly effective in the precise discrimination types. This systematic review examines evolution platforms, from Unmanned Aerial Vehicle (UAV)-mounted sensors space-borne satellites (e.g., EnMAP, PRISMA), explores recent scientific advances AI methodologies mapping. A protocol was applied identify 47 studies databases peer-reviewed publications, focusing on sensors, input features, classification architectures. analysis highlights significant contributions Deep Learning (DL) models, particularly Vision Transformers (ViTs) hybrid architectures, improving accuracy. However, also identifies critical gaps, including under-utilization limited multi-sensor need modeling approaches such as Graph Neural Networks (GNNs)-based methods geospatial foundation (GFMs) large-scale type Furthermore, findings highlight importance developing scalable, interpretable, transparent maximize potential imaging (HSI), underrepresented regions Africa, where research remains limited. provides valuable insights guide future researchers adopting HSI reliable mapping, contributing sustainable agriculture global

Language: Английский

Estimating Soil Attributes for Yield Gap Reduction in Africa Using Hyperspectral Remote Sensing Data with Artificial Intelligence Methods: An Extensive Review and Synthesis

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(9), P. 1597 - 1597

Published: April 30, 2025

Africa’s rapidly growing population is driving unprecedented demands on agricultural production systems. However, yields in Africa are far below their potential. One of the challenges leading to low productivity Africa‘s poor soil quality. Effective fertility management an essential key factor for optimizing while ensuring environmental sustainability. Key properties—such as organic carbon (SOC), nutrient levels (i.e., nitrogen (N), phosphorus (P), potassium (K), moisture retention (MR) or content (MC), and texture (clay, sand, loam fractions)—are critical factors influencing crop yield. In this context, study conducts extensive literature review use hyperspectral remote sensing technologies, with a particular focus freely accessible data (e.g., PRISMA, EnMAP), well evaluation advanced Artificial Intelligence (AI) models analyzing processing spectral map attributes. More specifically, examined progress applying technologies monitoring mapping properties over last 15 years (2008–2024). Our results demonstrated that (i) only very few studies have explored high-resolution sensors satellite sensors) property Africa; (ii) there considerable value AI approaches estimating attributes, strong recommendation further explore potential deep learning techniques; (iii) despite advancements AI-based methodologies availability sensors, combined application remains underexplored African context. To our knowledge, no yet integrated these Africa. This also highlights adopting encompassing both imaging spectroscopy) enhance accurate Africa, thereby constituting base addressing question yield gap.

Language: Английский

Design and On-Orbit Performance of Ku-Band Phased-Array Synthetic-Aperture Radar Payload System

Sensors, Journal Year: 2024, Volume and Issue: 24(20), P. 6741 - 6741

Published: Oct. 20, 2024

The current emphasis in the advancement of space-based synthetic-aperture radar (SAR) is on lightweight payloads under 100 kg with resolutions surpassing 1 m. This focus directed toward meeting launch criteria for multiple satellites a single rocket and cutting costs. article discusses creation progress Ku-band SAR payload Taijing-4(03) satellite, launched 23 January 2024 accompanied by four other satellites. design was customized to meet demands micro-nano satellite platform, resulting lightweight, flat weighing less than 80 kg, seamlessly integrated plate-shaped platform. also introduces beam optimization strategy phased array antenna, significantly boosting system's performance. provides various operating modes like slide-spot, strip, Scan 1, 2, others, maximum achievable resolution exceeding Extensive in-orbit testing produced numerous high-quality images potential uses emergency disaster mitigation, safeguarding ecosystems, monitoring forests, managing crops, tracking sea ice, more.

Language: Английский