Biophysical parameters mapping within the SPOT-5 Take 5 initiative

Shelestov, Andrii; Kolotii, Andrii; Skakun, Sergii; Baruth, Bettina; López-Lozano, Raúl; Yailymov, Bohdan

doi:10.1080/22797254.2017.1324743

Cited by 16 publications

(5 citation statements)

References 38 publications

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“…Adding other spectral bands, namely green and SWIR1, did not improve the model performance for winter wheat yields. These results confirm previous findings for winter wheat yields studies, where NIR and red play the most important role in capturing the biophysical parameters of winter wheat [12,61]. For other crops, for example, corn and soybean, a similar approach can be applied to identify spectral bands that maximize the correlation with crop yields.…”

Section: Discussionsupporting

confidence: 88%

Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models

et al. 2019

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A combination of Landsat 8 and Sentinel-2 offers a high frequency of observations (3-5 days) at moderate spatial resolution (10-30 m), which is essential for crop yield studies. Existing methods traditionally apply vegetation indices (VIs) that incorporate surface reflectances (SRs) in two or more spectral bands into a single variable, and rarely address the incorporation of SRs into empirical regression models of crop yield. In this work, we address these issues by normalizing satellite data (both VIs and SRs) derived from NASA's Harmonized Landsat Sentinel-2 (HLS) product, through a phenological fitting. We apply a quadratic function to fit VIs or SRs against accumulated growing degree days (AGDDs), which affects the rate of crop development. The derived phenological metrics for VIs and SRs, namely peak, area under curve (AUC), and fitting coefficients from a quadratic function, were used to build empirical regression winter wheat models at a regional scale in Ukraine for three years, 2016-2018. The best results were achieved for the model with near infrared (NIR) and red spectral bands and derived AUC, constant, linear, and quadratic coefficients of the quadratic model. The best model yielded a root mean square error (RMSE) of 0.201 t/ha (5.4%) and coefficient of determination R 2 = 0.73 on cross-validation.is that methods for generating products from coarse spatial resolution sensors can be ported to moderate (Landsat 8/OLI, Sentinel-2/MSI) or high (Planet/PlanetScope) spatial resolution sensors. However, the practice shows that such a transition is not always straightforward due to larger data gaps because of clouds and uneven coverage, sensor characteristics, and increased spatial resolution (at least at the order of 100, when going from 250 to 30 m).Consider, for example, a crop yield assessment/forecasting application [4]. The hypothesis is that satellite-based features, such as vegetation indices (VIs) or biophysical parameters derived at a single date or accumulated over some time period, can be correlated to crop yields [5,6]. Since the reference data on crop yields are mainly available at the regional scale, the corresponding empirical models are built by averaging satellite-based features over those regions and correlating these derived variables with crop yields [7][8][9]. It is assumed that there is a homogeneity within the region in terms of crops grown and agricultural practices applied and, therefore, the averaging should be performed for satellite data acquired at the same (or approximately the same) stages of crop growth, meaning that the data are normalized. This is usually the case for coarse spatial resolution remote sensing sensors, which enable a higher likelihood of obtaining a high temporal frequency of cloud-free data over the Earth's surface [10,11]. This is also evidenced by multiple successful applications of coarse spatial resolution satellite data to crop yield assessment and forecasting [5,[7][8][9][12][13][14][15].However, this is not the case for moderate spatial resolution sat...

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Section: Discussionsupporting

confidence: 88%

Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models

et al. 2019

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“…EVs from the food security domain include crop area and crop type represented by crop classification map, crop condition assessment and crop phenology characterized by vegetation indices (VIs) as such as NDVI, DVI, EVI (Kogan et al 2013), and biophysical parameters such as leaf area index (LAI) (Shelestov et al 2017a). For better estimation of crop condition and crop phenology with the use of VI, weather data that relate to Water and Energy Essential Variables are valuable.…”

Section: Workflows For Calculating Sdg Indicatorsmentioning

confidence: 99%

“…A Normalized Difference Vegetation Index (NDVI) has usually served as a proxy for RS-based estimation of crop yields or fraction of Absorbed Photosynthetically Active Radiation (fAPAR) (López-Lozano et al 2015;Shelestov et al 2017a). However, when dealing with a moderate spatial resolution at 10-30 m saturation can occur for high biomass plants (Gitelson 2011).…”

Section: The Use Of Harmonized Landsat Sentinel 2 Data For Crop Produmentioning

confidence: 99%

A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data

Kussul

Lavreniuk

Kolotii

et al. 2019

International Journal of Digital Earth

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For evaluating the progresses towards achieving the Sustainable Development Goals (SDGs), a global indicator framework was developed by the UN Inter-Agency and Expert Group on Sustainable Development Goals Indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDGs indicators. The main improvements consist of using moderate and high spatial resolution satellite data and state-of-the-art deep learning methodology for land cover classification and for assessing land productivity. Within the European Network for Observing our Changing Planet (ERA-PLANET), three SDGs indicators are calculated. In this research, harmonized Landsat and Sentinel-2 data are analyzed and used for land productivity analysis and yield assessment, as well as Landsat 8, Sentinel-2 and Sentinel-1 time series are utilized for crop mapping. We calculate for the whole territory of Ukraine SDG indicators: 15.1.1-'Forest area as proportion of total land area'; 15.3.1-'Proportion of land that is degraded over total land area'; and 2.4.1-'Proportion of agricultural area under productive and sustainable agriculture'. Workflows for calculating these indicators were implemented in a Virtual Laboratory Platform. We conclude that newly available high-resolution remote sensing products can significantly improve our capacity to assess several SDGs indicators through dedicated workflows.

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“…BPs were also used in wheat yield prediction (Bao et al, 2008;Huang et al, 2015a;Kolotii et al, 2015). Compared to VIs, the BPs are usually more reliable in estimating crop production since they more adequately re ect the state of the crops and thus could be more accurate in predicting crop yield and production (Duveiller et al, 2013;Kolotii et al, 2015;Shelestov et al, 2017). The present study used GEE to estimate the average annual value of all ve parameters over the 64 rice districts in China from 2001 to 2017.…”

Section: Methodsmentioning

confidence: 98%

Securing China's Rice Harvest: Unveiling Dominant Factors in Production Using Multi- Source Data and Hybrid Machine Learning Models

Mokhtar,

He,

Nabil

et al. 2024

Preprint

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Ensuring the security of China's rice harvest is imperative for sustainable food production. This study addresses this critical need by employing a comprehensive approach that integrates multi-source data, including climate, remote sensing, soil properties and statistical information. The research evaluates various single and hybrid machine learning models to predict rice production across China, particularly focusing on the main rice cultivation areas. The investigation identifies the hybrid models have performed better than single models, the best scenario was recorded in scenarios 8 (soil variables + SA) and 11 (All variables) based RF-XGB by decreasing the RMSE by 38% and 31% respectively in comparison with the single model (RF). Moreover, the soil properties contribute as the predominant factors influencing rice production, exerting an 87% and 53% impact in east and southeast China, respectively. Additionally, the study explores the implications of temperature and precipitation changes on rice production. Notably, it observes a yearly increase of 0.16°C and 0.19°C in maximum and minimum temperatures, coupled with a 20 mm/year decrease in precipitation. These climatic shifts contribute to a 2.2% annual reduction in rice production on average in southeast China. This research provides valuable insights into the dynamic interplay of environmental factors affecting China's rice yield, informing strategic measures to enhance food security in the face of evolving climatic conditions.

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Biophysical parameters mapping within the SPOT-5 Take 5 initiative

Cited by 16 publications

References 38 publications

Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models

Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models

A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data

Securing China's Rice Harvest: Unveiling Dominant Factors in Production Using Multi- Source Data and Hybrid Machine Learning Models

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