| 1. |
- Bandak, Soraya, et al.
(author)
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Satellite-Based Estimation of Soil Moisture Content in Croplands: A Case Study in Golestan Province, North of Iran
- 2023
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In: Remote Sensing. - 2072-4292. ; 15:8
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Journal article (peer-reviewed)abstract
- Soil moisture content (SMC) plays a critical role in soil science via its influences on agriculture, water resources management, and climate conditions. There is broad interest in finding relationships between groundwater recharge, soil characteristics, and plant properties for the quantification of SMC. The objective of this study was to assess the potential of optical satellite imagery for estimating the SMC over cropland areas. For this purpose, we collected 394 soil samples as targets in Gonbad-e Kavus in the Golestan province in the north of Iran, where a variety of crop types are cultivated. As input data, we first computed several spectral indices from Sentinel 2 (S2) and Landsat 8 (L8) images, such as the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Salinity Index (NDSI), and then analyzed their relationships with surveyed SMC using four machine learning regression algorithms: random forests (RFs), XGBoost, extra tree decision (EDT), and support vector machine (SVM). Results revealed a high and rather similar correlation between the spectral indices and measured SMC values for both S2 and L8 data. The EDT regression algorithm yielded the highest accuracy, with an R2 = 0.82, MAE = 3.74, and RMSE = 1.08 for S2 and R2 = 0.88, RMSE = 2.42, and MAE = 1.08 for L8 images. Results also revealed that MNDWI, NDWI, and NDSI responded most sensitively to SMC estimation.
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| 2. |
- Ganeva, Dessislava, et al.
(author)
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In-situ start and end of growing season dates of major European crop types from France and Bulgaria at a field level
- 2023
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In: Data in Brief. - 2352-3409. ; 51
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Journal article (peer-reviewed)abstract
- Crop phenology data offer crucial information for crop yield estimation, agricultural management, and assessment of agroecosystems. Such information becomes more important in the context of increasing year-to-year climatic variability. The dataset provides in-situ crop phenology data (first leaves emergence and harvest date) of major European crops (wheat, corn, sunflower, rapeseed) from seventeen field study sites in Bulgaria and two in France. Additional information such as the sowing date, area of each site, coordinates, method and equipment used for phenophase data estimation, and photos of the France sites are also provided. The georeferenced ground-truth dataset provides a solid base for a better understanding of crop growth and can be used to validate the retrieval of phenological stages from remote sensing data.
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| 3. |
- Kooistra, Lammert, et al.
(author)
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Reviews and syntheses : Remotely sensed optical time series for monitoring vegetation productivity
- 2024
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In: Biogeosciences. - 1726-4170. ; 21:2, s. 473-511
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Research review (peer-reviewed)abstract
- Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.
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