Development of capabilities for remote sensing estimate of Leaf Area Index from MODIS data

Shabanov, Nikolay V.; Барталев, С.А.; Ерошенко, Ф В; Плотников, Д.Е.

doi:10.21046/2070-7401-2018-15-4-166-178

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…District LAI annual courses were constructed using IKI MODIS (Moderate Resolution Imaging Spectroradiometer) LAI products from VEGA-Science [19]. The process of building the IKI MODIS LAI information product involved building weekly composite LAI images through calculating daily LAI values [20]. The atmospheric corrected values from the standard product MOD09 [21,22] in the red (620-670 nm) and near infrared (841-876 nm) bands were used to calculate the daily LAI values.…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

Application of LAI and NDVI to model soybean yield in the regions of the Russian Far East

Dubrovin

Aseeva²

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Soybean yield modeling using remote sensing is an essential task in the south of the Russian Far East and makes it possible to plan sowing areas at the municipal level. This article presents a comparative assessment of the regression models’ accuracy, where the seasonal maxima of the LAI (Leaf Area Index) and NDVI (Normal Difference Vegetation Index), as well as the number of growing days (days with an average daily air temperature above 10°C), were considered as predictors. For four districts of the Amur Region and the Jewish Autonomous Region, MODIS (Moderate-resolution Imaging Spectroradiometer) data obtained from the arable land mask were used, using the Vega-Science web service, as well as soybean yield in 2010-2017. It was found that the maximum values of LAI and NDVI fall on weeks 31 to 33, which corresponds to the first half of August. In 2010-2017, the LAI-based models’ MAPE (Mean Absolute Percentage Error) was in the range 4.1 – 9.0%, and the RMSE (Root Mean Squared Error) was 0.06 to 0.13 t/ha. The corresponding errors of the regression model with NDVI were quite similar: MAPE 4.8 to 10.4%, RMSE 0.06 to 0.15 t/ha. This approach was evaluated with a ‘leave-one-year-out’ cross-validation procedure. There were no significant differences in the forecast error (APE) when using LAI and NDVI; at the same time, it was found that the quality of the regression model in the Tambovskiy and Oktyabrskiy districts is higher than in the Leninskiy and Mikhailovskiy districts. The median APE for Tambovskiy district was 7.2% for LAI and 8.8% for NDVI, for Oktyabrskiy the corresponding figures were 7.5% and 6.1%, for Leninskiy – 14.2% and 13.7%, and for Mikhailovskiy – 10.8% and 12.3%, respectively.

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Section: Data Acquisition and Processingmentioning

confidence: 99%

Application of LAI and NDVI to model soybean yield in the regions of the Russian Far East

Dubrovin

Aseeva²

2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…Automated workflows for KMSS data processing are aimed at imagery georeferencing, clouds, and shadows detection, as well as atmospheric correction to provide seasonal and multiyear time series of surface reflectance and vegetation indices over northern Eurasia, including the grain belt of Russia [8]. KMSS Level-2 data prepared with the abovementioned workflow were shown to be qualitatively compatible with similar products of other sensors in this domain [8], indicating their interoperability with standard algorithms and developed workflows for time series analysis, satellite-based land use land cover (LULC) mapping and vegetation parameters retrieval [9][10][11][12][13][14][15][16]. Specifically, atmospherically corrected and gap-free time series of KMSS imagery were used for cropland thematic mapping over the Southern Federal District of Russia and to identify crop parcels using earlier developed routines [17].…”

Section: Introductionmentioning

confidence: 96%

Accuracy Assessment of Atmospheric Correction of KMSS-2 Meteor-M #2.2 Data over Northern Eurasia

Plotnikov,

Kolbudaev,

Matveev

et al. 2023

Remote Sensing

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Atmospheric correction of satellite remote sensing data is a prerequisite for a large variety of applications, including time series analysis and quantitative assessment of the Earth’s vegetation cover. It was earlier reported that an atmospherically corrected KMSS-M (Meteor-M #2) dataset was produced for Russia and neighboring countries. The methodology adopted for atmospheric correction was based on localized histogram matching of target KMSS-M and MODIS reference gap-free and date-matching imagery. In this paper, we further advanced the methodology and quantitatively assessed Level-2 surface reflectance analysis-ready datasets, operatively produced for KMSS-2 instruments over continental scales. Quantitative assessment was based on accuracy, precision, and uncertainty (APU) metrics produced for red and near-infrared bands of the KMSS-2 instrument based on a reference derived from a MODIS MOD09 reconstructed surface reflectance. We compared error distributions at 5%, 20%, and 50% levels of cloudiness and indicated that the cloudiness factor has little impact on the robustness of the atmospheric correction regardless of the band. Finally, the spatial and temporal gradients of accuracy metrics were investigated over northern Eurasia and across different seasons. It was found that for the vast majority of observations, accuracy falls within the −0.010–0.035 range, while precision and uncertainty were below 0.06 for any band. With the successful launch of the most recent Meteor-M #2.3 with a new KMSS-2 instrument onboard, the efficiency and interoperability of the constellation are expected to increase.

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“…Study of vegetation dynamics and assessment of biophysical parameters of vegetation by methods of Earth remote probing (hereinafter ERP) is of interest for a range of scientific and applied tasks [1]. Analysis of current satellite images and data from multiyear field observations of plants allows us to assess changes of volcanic landscapes and vegetation recovery speed.…”

Section: Introductionmentioning

confidence: 99%

Possibility of using satellite-based monitoring for large-scale mapping and research of dynamics of mud volcanic landscapes

Копанина

Shvidskaya

2021

IOP Conf. Ser.: Earth Environ. Sci.

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Currently Earth remote probing to study vegetation dynamics and monitor volcanic activity is of great scientific interest. The purpose of this study is to create a large-scale outline map of Yuzhno-Sakhalinsk mud volcano which will include the topography objects, mud fields of eruptions of various years and gryphons, and to perform semi-automatic classification of Yuzhno-Sakhalinsk mud volcano. Work was performed with QGIS software using the following modules: «QuickMapServices», «Freehandrastergeoreference», «LatLanTools», and «Semi-AutomaticClassificationPlugin». We developed an outline map of Yuzhno-Sakhalinsk mud volcano on a scale of 1:10000, which shows how the mud flows have changed directions over the last 70 years, as well as mud fields have been formed over the last 20 years. Using semi-automatic classification of satellite images from Sentinel-2A satellite in various color channel sets, we obtained two premaps of Yuzhno-Sakhalinsk mud volcano vegetation on a scale of 1:50 000. Satellite monitoring of YuSMV activity allows us to track the eruptive activity of the volcano, and assess its impact on vegetation.

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Development of capabilities for remote sensing estimate of Leaf Area Index from MODIS data

Cited by 6 publications

References 10 publications

Application of LAI and NDVI to model soybean yield in the regions of the Russian Far East

Application of LAI and NDVI to model soybean yield in the regions of the Russian Far East

Accuracy Assessment of Atmospheric Correction of KMSS-2 Meteor-M #2.2 Data over Northern Eurasia

Possibility of using satellite-based monitoring for large-scale mapping and research of dynamics of mud volcanic landscapes

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