Advanced analysis and integration of remote sensing and in situ data for flood monitoring

SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP* PEDRO HENRIQUE JANDREICE MAGNONI1; CÉSAR DE OLIVEIRA FERREIRA SILVA1 E RODRIGO LILLA MANZIONE2 1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista", Avenida Universitária, n° 3780, Altos do Paraíso, 18610-034, Botucatu, São Paulo, Brasil, [email protected]; [email protected]. 2 Departamento de Engenharia de Biossistemas, Faculdade de Ciências e Engenharia, Universidade Estadual Paulista “Júlio de Mesquita Filho”, Rua Domingos da Costa Lopes, 780, CEP 17602496, Tupã – SP, Brasil. E-mail: [email protected]. *Este artigo é proveniente das dissertações de mestrado dos dois primeiros autores. 1 RESUMO Ferramentas baseadas em sensoriamento remoto possibilitam o monitoramento do balanço hídrico da água em diferentes resoluções espaciais e temporais. Ainda assim, modelos que exigem dados in-situ impossibilitam sua aplicação em áreas com escassez de dados. No sentido de lidar com esse desafio, o presente trabalho apresenta uma abordagem de escolha do momento de irrigar, pelo balanço hídrico da água no solo, baseada em estimativa da evapotranspiração real (ETA) obtida com o uso conjunto de imagens multiespectrais do sensor MSI/SENTINEL-2 e dados de uma estação meteorológica pública. A área de estudo foi um pivô central localizado no munícipio de Itatinga-SP. Para a tomada de decisão do momento de irrigar, com base em um manejo por lâmina de irrigação fixa, foi feita a interpolação da fração evapotranspirativa entre os dias com imagens disponíveis para obter a ETA nos dias sem imagens por meio do seu produto com a evapotranspiração de referência. Essa abordagem captou variações climáticas essenciais para a estimativa do balanço hídrico em dias sem imagem. Destaca-se nessa aplicação conjunta sua capacidade de ser realizada sem necessitar de parâmetros específicos da cultura, do microclima ou do relevo, tornando-se interessante para regiões com escassez de dados. Palavras-chave: evapotranspiração, momento de irrigar, agriwater. MAGNONI, P. H. J.; SILVA, C. O. F.; MANZIONE, R. L. REMOTE SENSING APPLIED TO IRRIGATION MANAGEMENT IN AREAS WITH LACK OF DATA: A CASE STUDY IN A CENTRAL PIVOT IN ITATINGA-SP 2 ABSTRACT Remote sensing-based tools allow the monitoring of water budgets over different spatial and temporal resolutions. Nevertheless, some models require in situ data, preventing their application in areas with a lack of data. To address this challenge, this work presents an approach for irrigation scheduling, based on soil water budget estimation using actual evapotranspiration (ETA) obtained using MSI/SENTINEL-2 multispectral images and data from a public meteorological station. The study area consisted of a central pivot located in the municipality of Itatinga-SP, Brazil. For decision-making of irrigation scheduling, considering a fixed irrigation rate, the evapotranspiration fraction was interpolated between the days with available images to obtain the ETA on the days without images using its product with the reference evapotranspiration. This approach captured essential climate variations for estimating the water budget on non-image days. Noteworthy in this joint application is its suitability to be performed not requiring crop-, microclimate- or relief-specific parameters, making it useful for regions with a lack of data. Keywords: evapotranspiration, irrigation scheduling, agriwater.

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Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea

Sensors ◽

10.3390/s19163609 ◽

2019 ◽

Vol 19 (16) ◽

pp. 3609 ◽

Cited By ~ 11

Author(s):

Kyryliuk ◽

Kratzer

Keyword(s):

Remote Sensing ◽

Baltic Sea ◽

Attenuation Coefficient ◽

Secchi Depth ◽

Local Algorithm ◽

The Baltic Sea ◽

In Situ Data ◽

The Baltic ◽

Level 2

In this study, the Level-2 products of the Ocean and Land Colour Instrument (OLCI) data on Sentinel-3A are derived using the Case-2 Regional CoastColour (C2RCC) processor for the SentiNel Application Platform (SNAP) whilst adjusting the specific scatter of Total Suspended Matter (TSM) for the Baltic Sea in order to improve TSM retrieval. The remote sensing product “kd_z90max” (i.e., the depth of the water column from which 90% of the water-leaving irradiance are derived) from C2RCC-SNAP showed a good correlation with in situ Secchi depth (SD). Additionally, a regional in-water algorithm was applied to derive SD from the attenuation coefficient Kd(489) using a local algorithm. Furthermore, a regional in-water relationship between particle scatter and bench turbidity was applied to generate turbidity from the remote sensing product “iop_bpart” (i.e., the scattering coefficient of marine particles at 443 nm). The spectral shape of the remote sensing reflectance (Rrs) data extracted from match-up stations was evaluated against reflectance data measured in situ by a tethered Attenuation Coefficient Sensor (TACCS) radiometer. The L2 products were evaluated against in situ data from several dedicated validation campaigns (2016–2018) in the NW Baltic proper. All derived L2 in-water products were statistically compared to in situ data and the results were also compared to results for MERIS validation from the literature and the current S3 Level-2 Water (L2W) standard processor from EUMETSAT. The Chl-a product showed a substantial improvement (MNB 21%, RMSE 88%, APD 96%, n = 27) compared to concentrations derived from the Medium Resolution Imaging Spectrometer (MERIS), with a strong underestimation of higher values. TSM performed within an error comparable to MERIS data with a mean normalized bias (MNB) 25%, root-mean square error (RMSE) 73%, average absolute percentage difference (APD) 63% n = 23). Coloured Dissolved Organic Matter (CDOM) absorption retrieval has also improved substantially when using the product “iop_adg” (i.e., the sum of organic detritus and Gelbstoff absorption at 443 nm) as a proxy (MNB 8%, RMSE 56%, APD 54%, n = 18). The local SD (MNB 6%, RMSE 62%, APD 60%, n = 35) and turbidity (MNB 3%, RMSE 35%, APD 34%, n = 29) algorithms showed very good agreement with in situ data. We recommend the use of the SNAP C2RCC with regionally adjusted TSM-specific scatter for water product retrieval as well as the regional turbidity algorithm for Baltic Sea monitoring. Besides documenting the evaluation of the C2RCC processor, this paper may also act as a handbook on the validation of Ocean Colour data.

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Estimation of Transmittance of Solar Radiation in the Visible Domain Based on Remote Sensing: Evaluation of Models Using In Situ Data

Journal of Geophysical Research Oceans ◽

10.1002/2017jc013209 ◽

2017 ◽

Vol 122 (11) ◽

pp. 9176-9188 ◽

Cited By ~ 4

Author(s):

M. Laura Zoffoli ◽

Zhongping Lee ◽

Michael Ondrusek ◽

Junfang Lin ◽

Charles Kovach ◽

...

Keyword(s):

Remote Sensing ◽

Solar Radiation ◽

In Situ Data

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A Sentinel-1 based European crop parcel map using 2018 in-situ LUCAS Copernicus observations

10.5194/egusphere-egu2020-18703 ◽

2020 ◽

Author(s):

Verhegghen Astrid ◽

d'Andrimont Raphaël ◽

Lemoine Guido ◽

Strobl Peter ◽

van der Velde Marijn

Keyword(s):

Remote Sensing ◽

European Union ◽

Land Cover ◽

Growing Season ◽

Earth Observation ◽

Soft Wheat ◽

High Quality ◽

In Situ Data ◽

Crop Type

Efficient near-real time and wall-to-wall land monitoring is now possible with unprecedented detail because of the fleet of Copernicus Sentinel satellites. This remote sensing paradigm is the consequence of the freely accessible, global, Copernicus data, combined with affordable cloud computing. However, to translate this capacity in accurate products, and to truly benefit from the high spatial detail (~10m) and temporal resolution (~5 days in constellation) of the Sentinels 1 and 2, high quality and timely in-situ data remains crucial. Robust operational monitoring systems are in need of both training and validation data.&#160;Here, we demonstrate the potential of Sentinel 1 observations and complementary high-quality in-situ data to generate a crop type map at continental scale. In 2018, the Land Cover and Land Use Area frame Survey (LUCAS) carried out in the European Union contained a specific Copernicus module corresponding to 93.091 polygons surveyed in-situ. In contrast to the usual LUCAS point observation, the Copernicus protocol provides data on the extent of homogeneous land cover for a maximum size of 100 x 100 m, making it meaningful for remote sensing applications. After filtering the polygons to retrieve only high quality sample, a sample was selected to explore the accuracy of crop type maps at different moments of the 2018 growing season over Europe. The time series of 10 days VV and VH were classified using Random Forest models. The crops that were mapped correspond to the 13 major crops in Europe and are those that are monitored and forecast by the JRC MARS activities (soft wheat, maize, rapeseed, barley, potatoes, ...). Overall, reasonable accuracies were obtained (~80%). Although no a priori parcel delineation was used, it was encouraging to observe the relative homogeneity of pixel classification results within the same parcel. In the context of forecasting, we specifically assessed at what time in the growing season accuracies moved beyond a set threshold for the different crops. This ranged from May for winter crops such as soft wheat, and September for summer crops such as maize.&#160;Our results contribute to the discussion regarding the usefulness, benefits, as well as weaknesses, of the newly acquired LUCAS Copernicus data. Doing so, this study demonstrates the potential of in-situ surveys such as LUCAS Copernicus module&#160; specifically targeted for Earth Observation applications. Future improvements to the LUCAS Copernicus survey methodology are suggested. Importantly, now that LUCAS has been postponed to 2022, and aligned with the Copernicus space program, we advocate for a European Union wide systematic and representative in-situ sample campaign relevant for Earth Observation applications, beyond the traditional LUCAS survey.&#160;

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Evaluation of moist processes during intense precipitation in km-scale NWP models using remote sensing and in-situ data: Impact of microphysics size distribution assumptions

Atmospheric Research ◽

10.1016/j.atmosres.2010.08.017 ◽

2011 ◽

Vol 99 (1) ◽

pp. 15-38 ◽

Cited By ~ 12

Author(s):

Kwinten Van Weverberg ◽

Nicole P.M. van Lipzig ◽

Laurent Delobbe

Keyword(s):

Remote Sensing ◽

Size Distribution ◽

In Situ Data ◽

Intense Precipitation ◽

Moist Processes

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Trophic status assessment of small turbid lakes comparing remote sensing and in situ data: case study at lower Danube floodplain

Aerospace Research in Bulgaria ◽

10.3897/arb.v32.e05 ◽

2020 ◽

Vol 32 ◽

pp. 53-63

Author(s):

Stefan Kazakov ◽

Valko Biserkov ◽

Luchezar Pehlivanov ◽

Stoyan Nedkov

Keyword(s):

Remote Sensing ◽

Chlorophyll A ◽

Satellite Data ◽

Suspended Matter ◽

Trophic Status ◽

Remote Sensing Data ◽

Water Quality Monitoring ◽

Floodplain Lakes ◽

In Situ Data

The aim of the study was to compare in situ and remote sensing data, in order to assess the applicability of satellite images in water quality monitoring of floodplain lakes. Two indicators of trophic status were compared: chlorophyll a and total suspended matter. Two lakes on Lower Danube floodplain were selected: Srebarna and Malak Preslavets. Data were obtained in July and August 2018. Sentinel 2 MSI L1c images were analyzed in SeNtinel Application Platform (SNAP), (v. 6.0). According to in situ data, Srebarna Lake indicated status of eutrophication, while Malak Preslavets experienced hypertrophic conditions. Satellite data indicated eutrophic conditions for both lakes. Comparing the results from in situ and satellite data, chlorophyll a showed higher correlation (r = 0.66) and comparable results. On the other hand, significantly overestimation of suspended matter according to satellite data were found, as well weaker correlation (r = 0.57) between both methods. Remote sensing i.e. Sentinel products are emerging as a powerful tool in environmental observation. Although weather conditions could have significant impact on environmental dynamic especially in floodplain lakes, combining and comparing of different methods could improve the preciseness of the methodology as well as assessment reliability.

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Use of absorption optical indices to assess seasonal variability of dissolved organic matter in amazon floodplain lakes

10.5194/bg-2019-324 ◽

2019 ◽

Author(s):

Maria Paula da Silva ◽

Lino A. Sander de Carvalho ◽

Evlyn Novo ◽

Daniel S. F. Jorge ◽

Claudio C. F. Barbosa

Keyword(s):

Remote Sensing ◽

Organic Matter ◽

Dissolved Organic Matter ◽

Seasonal Variability ◽

Large Scale ◽

River Bank ◽

In Situ Data ◽

Amazon Floodplain ◽

Significant Difference

Abstract. Given the importance of DOM in the carbon cycling of aquatic ecosystems, information on its seasonal variability is crucial. This study assesses the use of available absorption optical indices based on in situ data to both characterize the seasonal variability of the DOM dynamics in a highly complex environment and their viability of being used for satellite remote sensing on large scale studies. The study area comprises four lakes located at the Mamirauá Sustainable Development Reserve (MSDR). Samples for the determination of coloured dissolved organic matter (CDOM) and remote sensing reflectance (Rrs) were acquired in situ. The Rrs was applied to simulate MSI visible bands and used in the proposed models. Differences between lakes were tested regarding CDOM indices. Significant difference in the average of aCDOM (440), aCDOM spectra and S275–295 were found between lakes located inside the flood forest and those near the river bank. The proposed model showed that aCDOM can be used as proxy of S275–295 during rising water with good validation results, demonstrating the potential of Sentinel/MSI imagery data in large scale studies on the dynamics of DOM.

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A dynamic approach for evaluating coarse scale satellite soil moisture products

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-7263-2010 ◽

2010 ◽

Vol 7 (5) ◽

pp. 7263-7303 ◽

Cited By ~ 1

Author(s):

A. Loew ◽

F. Schlenz

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Sampling Error ◽

Coarse Scale ◽

Dynamic Approach ◽

In Situ Data ◽

Novel Approach ◽

Area Sampling ◽

Satellite Product

Abstract. Validating coarse scale remote sensing soil moisture products requires a comparison of gridded data to point-like ground measurements. The necessary aggregation of in situ measurements to the footprint scale of a satellite sensor (>100 km2) introduces uncertainties in the validation of the satellite soil moisture product. Observed differences between the satellite product and in situ data are therefore partly attributable to these aggregation uncertainties. The present paper investigates different approaches to disentangle the error of the satellite product from the uncertainties associated to the up-scaling of the reference data. A novel approach is proposed, which allows for the quantification of the remote sensing soil moisture error using a temporally adaptive technique. It is shown that the point-to-area sampling error can be estimated within 0.0084 [m3/m3].

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