Comparison of NDVI and RVI Vegetation Indices Using Satellite Images

Gonenc, Abdurrahman; Özerdem, Mehmet Siraç; Acar, Emrullah

doi:10.1109/agro-geoinformatics.2019.8820225

Cited by 20 publications

(8 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other studies only analysed the optical and SAR index relationships without comparing them to plant parameters: Filgueiras et al [38] examined different algorithms to directly model NDVI for maize and soybeans from S1 data but did not address the underlying mechanisms. Gonenc et al [39] compared a Radar Vegetation Index (RVI) from fully polarized Radarsat-2 with NDVI from Landsat 8 and found a good correlation. Each of the three studies focused on one study site within one region, which is not enough for area-wide applications.…”

Section: Introductionmentioning

confidence: 99%

Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring

et al. 2020

View full text Add to dashboard Cite

Agricultural vegetation development and harvest date monitoring over large areas requires frequent remote sensing observations. In regions with persistent cloud coverage during the vegetation season this is only feasible with active systems, such as SAR, and is limited for optical data. To date, optical remote sensing vegetation indices are more frequently used to monitor agricultural vegetation status because they are easily processed, and the characteristics are widely known. This study evaluated the correlations of three Sentinel-2 optical indices with Sentinel-1 SAR indices over agricultural areas to gain knowledge about their relationship. We compared Sentinel-2 Normalized Difference Vegetation Index, Normalized Difference Water Index, and Plant Senescence Radiation Index with Sentinel-1 SAR VV and VH backscatter, VH/VV ratio, and Sentinel-1 Radar Vegetation Index. The study was conducted on 22 test sites covering approximately 35,000 ha of four different main European agricultural land use types, namely grassland, maize, spring barley, and winter wheat, in Lower Saxony, Germany, in 2018. We investigated the relationship between Sentinel-1 and Sentinel-2 indices for each land use type considering three phenophases (growing, green, senescence). The strength of the correlations of optical and SAR indices differed among land use type and phenophase. There was no generic correlation between optical and SAR indices in our study. However, when the data were split by land use types and phenophases, the correlations increased remarkably. Overall, the highest correlations were found for the Radar Vegetation Index and VH backscatter. Correlations for grassland were lower than for the other land use types. Adding auxiliary data to a multiple linear regression analysis revealed that, in addition to land use type and phenophase information, the lower quartile and median SAR values per field, and a spatial variable, improved the models. Other auxiliary data retrieved from a digital elevation model, Sentinel-1 orbit direction, soil type information, and other SAR values had minor impacts on the model performance. In conclusion, despite the different nature of the signal generation, there were distinct relationships between optical and SAR indices which were independent of environmental variables but could be stratified by land use type and phenophase. These relationships showed similar patterns across different test sites. However, a regional clustering of landscapes would significantly improve the relationships.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Through calculation, this paper found that the red band (band 4), the near-infrared band (band 5), and shortwave infrared band 1 (band 6) were most suitable for simultaneous classification. Furthermore, vegetation is the main food source and suitable living environment for locusts [35][36][37], and NDVI is the best factor to reflect the vegetation health situation [38], so this study took NDVI as a band to be a part of the classification. In summary, the final features involved in the classification were red, nearred, shortwave infrared band 1, and NDVI.…”

Section: Habitat Classification Methodsmentioning

confidence: 99%

Study on the Forming Mechanism of the High-Density Spot of Locust Coupled with Habitat Dynamic Changes and Meteorological Conditions Based on Time-Series Remote Sensing Images

et al. 2022

View full text Add to dashboard Cite

The outbreak of the Asian migratory locust (Locusta migratoria migratoria) (AML) can deal a great blow to agriculture and grassland farming. The emergence of high-density locusts facilitates the outbreak of locusts. Understanding the forming mechanism of the high-density spot of locust (HDSL) is very important for locust monitoring and control. To achieve this goal, this paper took Nong’an County, which used to form an HDSL in 2017, as the study area. Firstly, based on the habitat classification system, support vector machine (SVM), random forest (RF), and maximum likelihood (ML) methods were employed to explore the best classification method for locust habitats. Then, the optimal method was applied to monitor habitat dynamic changes from 2014 to 2017 in the HDSL in Nong’an. Finally, the HDSL forming mechanism was clarified coupled with habitat dynamic changes and meteorological data. The results showed that the SVM method was the optimal method, with an accuracy of 95.28%, which is higher than the RF and ML methods by 0.25% and 8.52%, respectively. The annual increased barren land and sufficient reeds provided adequate suitable habitats for the breeding of AML. From 2014 to 2016, the temperatures during the overwintering and hatching periods were higher than the 2010–2018 average, and the precipitation during the spawning period was lower than the 2010–2018 average. The precipitation during the growing period in 2017 was 30.8 mm less than the average from 2010 to 2018. All these characteristics were conducive to the reproduction of locusts. We concluded that the suitable habitat and meteorological conditions increased the locust quantity yearly, resulting in the formation of HDSL. These results are instrumental for monitoring potential high-risk outbreak areas, which is important to improve locust control and ensure food security.

show abstract

“…Given its frequent utilization as a vegetation index derived from Synthetic Aperture Radar (SAR) data for vegetation growth [27][28][29][30] and its correlation with the Normalized Difference Vegetation Index (NDVI), 31,32 we investigated its utility for harvest detection. The range of RVI values from 0.3 to 0.45 was tested to determine the optimal threshold for identifying the harvesting by minimizing MAE in days.…”

Section: Radar Vegetation Indexmentioning

confidence: 99%

Using Sentinel-1 data for soybean harvest detection in Vojvodina province, Serbia

Marković,

Živaljević,

Mimić

et al. 2023

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV

View full text Add to dashboard Cite

Information on crop harvest events has become valuable input for models related to food security and agricultural management and optimization. Precise large scale harvest detection depends on temporal resolution and satellite images availability. Synthetic Aperture Radar (SAR) data are more suitable than optical, since the images are not affected by clouds. This study compares two methods for harvest detection of soybean in Vojvodina province (Serbia), using the C-band of Sentinel-1. The first method represents a maximum difference of ascending VH polarization backscatter (σ V H ) between consecutive dates of observation. The second method uses a Radar Vegetation Index (RVI) threshold value of 0.39, optimized to minimize Mean Absolute Error (MAE). The training data consisted of 50 m point buffers' mean value with ground-truth harvest dates (n=100) from the 2018 and 2019 growing seasons. The first method showed better performance with Pearson correlation coefficient r=0.85 and MAE=5 days, whereas the calculated metrics for the RVI threshold method were r=0.69 and MAE=8 days. Therefore, validation was performed only for the method of maximum VH backscatter difference where mean values of parcels with ground-truth harvest dates for 2020 had generated the validation dataset (n=67). Performance metrics (r=0.83 and MAE=3 days) confirmed the suitability for accurate harvest detection. Ultimately, a soybean harvest map was generated on a parcel level for Vojvodina province.

show abstract

Comparison of NDVI and RVI Vegetation Indices Using Satellite Images

Cited by 20 publications

References 2 publications

Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring

Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring

Study on the Forming Mechanism of the High-Density Spot of Locust Coupled with Habitat Dynamic Changes and Meteorological Conditions Based on Time-Series Remote Sensing Images

Using Sentinel-1 data for soybean harvest detection in Vojvodina province, Serbia

Contact Info

Product

Resources

About