Land Suitability Assessment Based on Feature-Level Fusion of Sentinel-1 and Sentinel-2 Imagery: A Case Study of the Honam Region of Iran

Optimal use of agricultural land leads to increased productivity and paves the way for sustainable agriculture. The land suitability assessment (LSA) is known as the basic scientific solution in this regard and has been widely used. This study takes a new approach to LSA based on the cultivated crop...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2024, Vol.17, p.14777-14789
Main Authors: Khaki, Bahare Delsous, Chatrenour, Mansour, Navidi, Mir Naser, Soleimani, Masoud, Mirzaei, Saham, Pignatti, Stefano
Format: Article
Language:English
Subjects:
Accuracy
Adaptive optics
Agricultural land
Agriculture
Alfalfa
Algorithms
Barley
Case studies
Chickpeas
Crop production
Crop type mapping
Crops
Food plants
Garlic
Imagery
land production potential (LPP)
land suitability assessment (LSA)
Legumes
Maximum sustainable yield
Optical imaging
Optical sensors
Physical properties
Production
Radar imaging
remote sensing
SAR (radar)
sentinel imagery
Sentinel-1
Soil
Soil physical properties
Soil properties
Soil structure
Soil temperature
Support vector machines
Sustainable agriculture
Synthetic aperture radar
Wheat
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Summary:Optimal use of agricultural land leads to increased productivity and paves the way for sustainable agriculture. The land suitability assessment (LSA) is known as the basic scientific solution in this regard and has been widely used. This study takes a new approach to LSA based on the cultivated crops and their land production potential (LPP). The Honam region of Lorestan Province in western Iran was selected as a case study, where various plants including orchards and crop types (e.g., rainfed wheat, irrigated wheat, barley, chickpea, alfalfa, clover, and garlic) were cultivated. A map of crop types was first produced using the time series of Sentinel-1 synthetic aperture radar and Sentinel-2 optical imagery in the context of a feature-level fusion classification approach based on the support vector machine (SVM) algorithm. The LSA was then performed using the Food and Agriculture Organization method, and the LPP for plants was estimated using climate, soil, and landscape requirements. The SVM-derived crop type map gave acceptable performance with an overall accuracy of 92% and 86% kappa coefficient. Meanwhile, the clover and alfalfa had the highest (96%) and lowest (66%) accuracy, respectively. The LSA showed that slope, temperature, and soil physical properties such as texture, structure, and coarse fragments resulted in a significant reduction in LPP compared to potential yield. These limitations lowered the land suitability for chickpea, wheat, and garlic and made the land unsuitable for chickpea. Despite the limitations, over 60% of the studied lands for alfalfa, clover, barley, and wheat were in the suitable and moderately suitable classes.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2024.3437689