PRISMA Spatial Resolution Enhancement by Fusion With Sentinel-2 Data

Acito, N.; Diani, Marco; Corsini, Giovanni

doi:10.1109/jstars.2021.3132135

Cited by 12 publications

(4 citation statements)

References 54 publications

(64 reference statements)

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“…The high degree of correlation between the visible-NIR band combinations and the yield can be explained by the correlation between the crop growth and the chlorophyll content (Acito et al, 2022), which has also been demonstrated in previous studies (Zhang et al, 2021). However, in most studies, only bands in the range 500 to 900 nm have been used and most other useful bands have been neglected (Zhang et al, 2018;Zhang et al, 2021).…”

Section: Effect Of Various Spectral Bands and Vegetation Indexes On Y...mentioning

confidence: 66%

“…In this study, we found that an increase in either the spectral or spatial resolution leads to an increase in the estimation accuracy (Figure 4). Acito et al (2022) deals with the problem of improving the spatial resolution of hyperspectral data from the PRISMA mission and provides a superresolved image with a spatial resolution of 10 m and the same spectral resolution as the PRISMA hyperspectral sensor. In future, following the work by Acito et al (2022), In future, we also hope to use fused ZY-1 02D and Sentinel-2 data for yield estimation to explore how temporally, spectrally, and spatially rich data perform, in a similar way to how the fusion of ZY-1 02D and multispectral data has been used for land classification (Sun et al, 2020a).…”

Section: Analysis Of the Models And Other Factors Affecting The Yieldmentioning

confidence: 99%

“…Acito et al (2022) deals with the problem of improving the spatial resolution of hyperspectral data from the PRISMA mission and provides a superresolved image with a spatial resolution of 10 m and the same spectral resolution as the PRISMA hyperspectral sensor. In future, following the work by Acito et al (2022), In future, we also hope to use fused ZY-1 02D and Sentinel-2 data for yield estimation to explore how temporally, spectrally, and spatially rich data perform, in a similar way to how the fusion of ZY-1 02D and multispectral data has been used for land classification (Sun et al, 2020a). Compared with yield estimation models on The winter wheat yield plotted against different planting management variables: (A) seeding rate, (B) farming method (Subsoiling-Tillage-Rotatillage-Rototilling), (C) the rate of fertilizer application, and (D) the amount of irrigation.…”

Section: Analysis Of the Models And Other Factors Affecting The Yieldmentioning

confidence: 99%

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Wheat yield estimation using remote sensing data based on machine learning approaches

et al. 2022

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Accurate predictions of wheat yields are essential to farmers’production plans and to the international trade in wheat. However, only poor approximations of the productivity of wheat crops in China can be obtained using traditional linear regression models based on vegetation indices and observations of the yield. In this study, Sentinel-2 (multispectral data) and ZY-1 02D (hyperspectral data) were used together with 15709 gridded yield data (with a resolution of 5 m × 5 m) to predict the winter wheat yield. These estimates were based on four mainstream data-driven approaches: Long Short-Term Memory (LSTM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Regression (SVR). The method that gave the best estimate of the winter wheat yield was determined, and the accuracy of the estimates based on multispectral and hyperspectral data were compared. The results showed that the LSTM model, for which the RMSE of the estimates was 0.201 t/ha, performed better than the RF (RMSE = 0.260 t/ha), GBDT (RMSE = 0.306 t/ha), and SVR (RMSE = 0.489 t/ha) methods. The estimates based on the ZY-1 02D hyperspectral data were more accurate than those based on the 30-m Sentinel-2 data: RMSE = 0.237 t/ha for the ZY-1 02D data, which is about a 5% improvement on the RSME of 0.307 t/ha for the 30-m Sentinel-2 data. However, the 10-m Sentinel-2 data performed even better, giving an RMSE of 0.219 t/ha. In addition, it was found that the greenness vegetation index SR (simple ratio index) outperformed the traditional vegetation indices. The results highlight the potential of the shortwave infrared bands to replace the visible and near-infrared bands for predicting crop yields Our study demonstrates the advantages of the deep learning method LSTM over machine learning methods in terms of its ability to make accurate estimates of the winter wheat yield.

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Section: Effect Of Various Spectral Bands and Vegetation Indexes On Y...mentioning

confidence: 66%

Section: Analysis Of the Models And Other Factors Affecting The Yieldmentioning

confidence: 99%

Section: Analysis Of the Models And Other Factors Affecting The Yieldmentioning

confidence: 99%

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Wheat yield estimation using remote sensing data based on machine learning approaches

et al. 2022

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“…Frontiers in Remote Sensing frontiersin.org imagery with the Sentinel-2 data to improve classification results has been described in several studies (Chan and Yokoya, 2016;Yokoya et al, 2016;Acito et al, 2022). For this study, the Smoothing Filterbased Intensity Modulation hypersharpening method (SFIM-HS), which formulates the relationship between solar radiation and land surface reflectance, was used to fuse the existing EnMAP image with the resampled Sentinel-2 image (Yokoya et al, 2017).…”

Section: Description and Pre-processing Of Satellite Datamentioning

confidence: 99%

Spectral imaging of grass species in arid ecosystems of Namibia

Bantelmann,

Wyss,

Pius

et al. 2024

Front. Remote Sens.

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Grasslands across the African continent are under pressure from climate change and human activities, particularly in arid ecosystems. From a remote sensing perspective, these ecosystems have not received much scientific attention, especially in Namibia. To address this knowledge gap, various remote sensing methods were implemented using new generation spaceborne imaging spectrometers amongst others. Therefore, this research provides a first methodological approach aimed at mapping and evaluating the distribution of grasslands within two private nature reserves, namely, the NamibRand Nature Reserve (NRNR) and ProNamib Nature Reserve (PNNR) with surrounding farmlands on the edge of Namib Sand Sea. The multi-sensor approach utilizes Mixture Tuned Matched Filtering (MTMF) and incorporated spectral information collected in the field to analyze grasslands. The research involves a sensor comparison of multispectral Sentinel-2 and PlanetScope data, hyperspectral data from Environmental Mapping and Analysis Programme (EnMAP) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) and an additional data fusion product derived from Sentinel-2 and EnMAP imagery based on a Smoothing Filter-based Intensity Modulation Hypersharpening method (SFIM-HS). Additionally, a unique spectral library of collected field spectra was established and inter-species spectral separability and intra-species spectral homogeneity was analyzed. This library presents newly published spectra of individual species. Due to dry initial conditions, the calculated spectral separability of individual grasses is limited, making only a mean endmember feasible for partial unmixing. The validation results of satellite comparison show that data fusion products (R2 = 0.51 with Normalized Difference Vegetation Index (NDVI); R2 = 0.66 with Soil Adjusted Vegetation Index (SAVI)) are more suitable for mapping arid grasslands than multispectral or hyperspectral data (all R2 < 0.35). More research is required and potential methodological adjustments are discussed to further investigate the spatio-temporal dynamics of arid grasslands and to aid conservation efforts in the Greater Sossusvlei-Namib Landscape in line with the United Nations Decade of Restoration.

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