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2022 | OriginalPaper | Chapter

3. Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Authors : Rubaiya Binte Mustafiz, Ryozo Noguchi, Tofael Ahamed

Published in: Remote Sensing Application

Publisher: Springer Nature Singapore

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Abstract

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning for agricultural sectors. However, misinformed land selection decisions limit crop yields and increase production-related costs to farmers. Therefore, the purpose of this research was to develop a land suitability assessment system using satellite remote sensing-derived soil-vegetation indicators. A multicriteria decision analysis was conducted by integrating weighted linear combinations and fuzzy multicriteria analyses in a GIS platform for suitability assessment using the following eight criteria: elevation, slope, LST, and vegetation indices (SAVI, ARVI, SARVI, MSAVI, and OSAVI). The relative priorities of the indicators were identified using a fuzzy expert system. Furthermore, the results of the land suitability assessment were evaluated by ground truth yield data. In addition, a yield estimation method was developed using indices representing influential factors. The analysis utilizing equal weights showed that 43% of the land (1832 km²) was highly suitable, 41% of the land (1747 km²) was moderately suitable, and 10% of the land (426 km²) was marginally suitable for improved yield productions. Alternatively, expert knowledge was also considered, along with references, when using the fuzzy membership function; as a result, 48% of the land (2045 km²) was identified as being highly suitable; 39% of the land (2045 km²) was identified as being moderately suitable, and 7% of the land (298 km²) was identified as being marginally suitable. Additionally, 6% (256 km²) of the land was described as not suitable by both methods. Moreover, the yield estimation using SAVI (R² = 0.773), ARVI (R² = 0.689), SARVI (R² = 0.711), MSAVI (R² = 0.745), and OSAVI (R² = 0.812) showed a good predictive ability. Furthermore, the combined model using these five indices reported the highest accuracy (R² = 0.839); this model was then applied to develop yield prediction maps for the corresponding years (2017–2020). This research suggests that satellite remote sensing methods in GIS platforms are an effective and convenient way for agricultural land use planners and land policy makers to select suitable cultivable land areas with potential for increased agricultural production.

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previous chapter Calorie-Based Seasonal Multicrop Land Suitability Analysis Using GIS and Remote Sensing for Regional Food Nutrition Security in Bangladesh

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Title: Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices
Authors: Rubaiya Binte Mustafiz
Ryozo Noguchi
Tofael Ahamed
Publisher: Springer Nature Singapore
Book: Remote Sensing Application
Print ISBN: 978-981-19-0212-3

Electronic ISBN: 978-981-19-0213-0

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-981-19-0213-0_3

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