Relationship Between Normalized Difference Vegetation Index (NDVI) and Rice Growth Phases in Danda Jaya Swamp Irrigation Area Regency Barito Kuala

Nafarin, Naufal Adlie; Novitasari, Novitasari

doi:10.1088/1755-1315/1184/1/012019

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…The purpose of cluster analysis is to group data based on their similarity [35]. To achieve this, the algorithm automatically normalizes the input numerical attributes and employs Euclidean distance to measure distances between groups, aiming to minimize the variation (distances) within the groups [10].…”

Section: Cluster Generationmentioning

confidence: 99%

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

Medina Medina,

Salas López,

Zabaleta Santisteban

et al. 2024

Agronomy

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One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management.

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Section: Cluster Generationmentioning

confidence: 99%

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

Medina Medina,

Salas López,

Zabaleta Santisteban

et al. 2024

Agronomy

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“…The NDVI is one of the commonly used indicators to characterize vegetation coverage [54,55]. Green vegetation has a low reflectance in the red band and a high reflectivity in the near-infrared band because it absorbs incoming solar energy in the red band and scatters it in the near-infrared band.…”

Section: Remote Sensing Ecological Monitoring Indicatorsmentioning

confidence: 99%

Ecological Environment Quality Assessment of Arid Areas Based on Improved Remote Sensing Ecological Index—A Case Study of the Loess Plateau

Shi,

Lin,

Jing

et al. 2023

Sustainability

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Ecosystems in arid and semi-arid areas are delicate and prone to different erosive effects. Monitoring and evaluating the environmental ecological condition in such areas contribute to the governance and restoration of the ecosystem. Remote sensing ecological indices (RSEIs) are widely used as a method for environmental monitoring and have been extensively applied in various regions. This study selects the arid and semi-arid Loess Plateau as the research area, in response to existing research on ecological monitoring that predominantly uses vegetation indices as monitoring indicators for greenness factors. A fluorescence remote sensing ecological index (SRSEI) is constructed by using monthly synthesized sun-induced chlorophyll fluorescence data during the vegetation growth period as a new component for greenness and combining it with MODIS product data. The study generates the RSEI and SRSEI for the research area spanning from 2001 to 2021. The study compares and analyzes the differences between the two indices and explores the evolution patterns of the ecosystem quality in the Loess Plateau over a 21-year period. The results indicate consistent and positively correlated linear fitting trend changes in the RSEI and SRSEI for the research area between 2001 and 2021. The newly constructed ecological index exhibits a higher correlation with rainfall data, and it shows a more significant decrease in magnitude during drought occurrences, indicating a faster and stronger response of the new index to drought in the research area. The largest proportions are found in the research area’s regions with both substantial and minor improvements, pointing to an upward tendency in the Loess Plateau’s ecosystem development. The newly constructed environmental index can effectively evaluate the quality of the ecosystem in the research area.

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Analysis of Rice Plant Age Based on NDVI Value Using MODIS Imagery (Case Study of Koto Tangah District, Padang City)

Safitri,

Yanti,

Irsyad

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Determining the growth phase of rice is an estimation technique that has been carried out universally using satellite images. However, an estimation model cannot applied in other areas due to different regional characteristics. Lowland areas have different environmental factors than highland areas. Padang City with the characteristics of a coastal area dan including the capital of West Sumatra Province has productive rice fields as one of the rice supplies in Padang City. This study aims to determine a model for estimating the growth phase of rice plants in Padang City using MODIS Imagery. The data used were MODIS image recordings from January 2021 to July 2024 with 60 image output data. Observation locations with grids or image pixels with rice field areas ≥ 50%. Based on the results of image analysis, a model was obtained for estimating the age of rice plants in Padang City, namely y = −0.000095x2+ 0.013743x + 0.310743 with y being the NDVI value and x is the age of the rice plant with a coefficient of determination (R2) value of 0.9554 in the forceful category and an RMSE value of 0.0383. The range of NDVI values for each phase of rice growth in Koto Tangah District, Padang City, is water phase < 0.5063; vegetative phase (1) 0.5063-0.7515; vegetative phase (2) 0.7515-0.8022; generative phase (1) 0.8022-0.7546; generative phase (2) 0.7546-0.5134 and fallow phase 0.5134-0.3198.

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Relationship Between Normalized Difference Vegetation Index (NDVI) and Rice Growth Phases in Danda Jaya Swamp Irrigation Area Regency Barito Kuala

Cited by 5 publications

References 6 publications

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

Ecological Environment Quality Assessment of Arid Areas Based on Improved Remote Sensing Ecological Index—A Case Study of the Loess Plateau

Analysis of Rice Plant Age Based on NDVI Value Using MODIS Imagery (Case Study of Koto Tangah District, Padang City)

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