scholarly journals Utility of Satellite Remote Sensing for Land–Atmosphere Coupling and Drought Metrics

2017 ◽  
Vol 18 (3) ◽  
pp. 863-877 ◽  
Author(s):  
Joshua K. Roundy ◽  
Joseph A. Santanello
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Prediction Models ◽  
Water Cycle ◽  
Spatial Scales ◽  
Complex Nature ◽  
Sufficient Information ◽  
Tropospheric Temperature ◽  
In Situ Data

Abstract Feedbacks between the land and the atmosphere can play an important role in the water cycle, and a number of studies have quantified land–atmosphere (LA) interactions and feedbacks through observations and prediction models. Because of the complex nature of LA interactions, the observed variables are not always available at the needed temporal and spatial scales. This work derives the Coupling Drought Index (CDI) solely from satellite data and evaluates the input variables and the resultant CDI against in situ data and reanalysis products. NASA’s Aqua satellite and retrievals of soil moisture and lower-tropospheric temperature and humidity properties are used as input. Overall, the Aqua-based CDI and its inputs perform well at a point, spatially, and in time (trends) compared to in situ and reanalysis products. In addition, this work represents the first time that in situ observations were utilized for the coupling classification and CDI. The combination of in situ and satellite remote sensing CDI is unique and provides an observational tool for evaluating models at local and large scales. Overall, results indicate that there is sufficient information in the signal from simultaneous measurements of the land and atmosphere from satellite remote sensing to provide useful information for applications of drought monitoring and coupling metrics.

scholarly journals Observed and simulated hydroclimatology using distributed hydrologic model from in-situ and multi-satellite remote sensing datasets in Lake Victoria region in East Africa

2010 ◽  
Vol 7 (4) ◽  
pp. 4785-4816 ◽  
Author(s):  
S. I. Khan ◽  
P. Adhikari ◽  
Y. Hong ◽  
H. Vergara ◽  
T. Grout ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Water Cycle ◽  
Lake Victoria ◽  
Remote Sensing Data ◽  
Hydrologic Model ◽  
East African ◽  
Catchment Scale ◽  
Distributed Hydrologic Model

Abstract. Floods and droughts are common, recurring natural hazards in East African nations. Studies of hydro-climatology at daily, seasonal, and annual time scale is an important in understanding and ultimately minimizing the impacts of such hazards. Using daily in-situ data over the last two decades combined with the recently available multiple-years satellite remote sensing data, we analyzed and simulated, with a distributed hydrologic model, the hydro-climatology in Nzoia, one of the major contributing sub-basins of Lake Victoria in the East African highlands. The basin, with a semi arid climate, has no sustained base flow contribution to Lake Victoria. The short spell of high discharge showed that rain is the prime cause of floods in the basin. There is only a marginal increase in annual mean discharge over the last 21 years. The 2-, 5- and 10-year peak discharges, for the entire study period showed that more years since the mid 1990's have had high peak discharges despite having relatively less annual rain. The study also presents the hydrologic model calibration and validation results over the Nzoia Basin. The spatiotemporal variability of the water cycle components were quantified using a physically-based, distributed hydrologic model, with in-situ and multi-satellite remote sensing datasets. Moreover, the hydrologic capability of remote sensing data such as TRMM-3B42V6 was tested in terms of reconstruction of the water cycle components. The spatial distribution and time series of modeling results for precipitation (P), evapotranspiration (ET), and change in storage (dS/dt) showed considerable agreement with the monthly model runoff estimates and gauge observations. Runoff values responded to precipitation events that occurred across the catchment during the wet season from March to early June. The hydrologic model captured the spatial variability of the soil moisture storage. The spatially distributed model inputs, states, and outputs, were found to be useful for understanding the hydrologic behavior at the catchment scale. Relatively high flows were experienced near the basin outlet from previous rainfall, with a new flood peak responding to the rainfall in the upper part of the basin. The monthly peak runoff was observed in the months of April, May and November. The analysis revealed a linear relationship between rainfall and runoff for both wet and dry seasons. The model was found to be useful in poorly gauged catchments using satellite forcing data and showed the potential to be used not only for the investigation of the catchment scale water balance but also for addressing issues pertaining to sustainability of the resources within the catchment.

scholarly journals Predicting species distributions and community composition using satellite remote sensing predictors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jesús N. Pinto-Ledezma ◽  
Jeannine Cavender-Bares
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Satellite Remote Sensing ◽  
Prediction Models ◽  
Direct Detection ◽  
Spatial Scales ◽  
Management Strategies ◽  
Model Performance ◽  
Next Generation ◽  
Distribution Models

AbstractBiodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m2). However, these models do return reasonable predictions at macroecological scales, i.e., moderately to highly correct assignments of species identities at the scale of NEON sites (mean area ~ 27 km2). Our results provide insights for advancing the accuracy of prediction of assemblage diversity and composition at different spatial scales globally. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models that accurately predict and monitor ecological assemblages through time and space.

scholarly journals Evaluation of Earth System Model and Atmospheric Inversion using Total Column CO2 Observations from GOSAT and OCO-2

2021 ◽  
Author(s):  
Prabir K Patra ◽  
Tomohiro Hajima ◽  
Ryu Saito ◽  
Naveen Chandra ◽  
Yukio Yoshida ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Greenhouse Gases ◽  
Satellite Remote Sensing ◽  
Earth System Model ◽  
System Model ◽  
Co2 Fluxes ◽  
Earth System ◽  
In Situ Data ◽  
Total Column

Abstract The measurements of one of the major greenhouse gases, carbon dioxide (CO2), are being made using dedicated satellite remote sensing since the launch of the greenhouse gases observing satellite (GOSAT) by Japan Aerospace Exploration Agency (JAXA) in 2009 and National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2). In the past 10 years, estimation of CO2 fluxes from land and ocean using the earth system models (ESMs) and inverse modelling of in situ atmospheric CO2 data have also made significant progress. We attempt, for the first time, to evaluate the CO2 fluxes simulated by an earth system model (MIROC-ES2L) and the fluxes estimated by an inverse model (MIROC4-Inv) using in situ data by comparing with GOSAT and OCO-2 observations. Both MIROC-ES2L and MIROC4-Inv fluxes are used in the MIROC4-atmospheric chemistry transport model (referred to as ACTM_ES2LF and ACTM_InvF, respectively) for calculating total column CO2 mole fraction (XCO2) that are sampled at the time and location of the satellite measurements. Both the ACTM simulations agreed well with the GOSAT and OCO-2 satellite observations, within 2 ppm for the spatial maps and time evolutions of the zonal mean distributions. Our results suggest that the inverse model using in situ data are more consistent with the OCO-2 retrievals, compared to those of the GOSAT XCO2 data due to the higher accuracy of the former. This suggests that the MIROC4-Inv fluxes are of sufficient quality to evaluate MIROC-ES2L simulated fluxes. The ACTM_ES2LF simulation shows a slightly weaker seasonal cycle for the meridional profiles of CO2 fluxes, compared to that from the ACTM_InvF. This difference is revealed by greater XCO2 differences for ACTM_ES2LF vs GOSAT, compared to those of ACTM_InvF vs GOSAT. Using remote sensing based global products of leaf area index (LAI) and gross primary productivity (GPP) over land, we show a weaker sensitivity of MIROC-ES2L biospheric activities to the weather and climate in the tropical regions. Our results clearly suggest the usefulness of XCO2 measurements by satellite remote sensing for evaluation of large-scale ESMs, which so far remained untested by the sparse in situ data.

scholarly journals Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

2018 ◽  
Vol 10 (1) ◽  
pp. 525-548 ◽  
Author(s):  
Sina C. Truckenbrodt ◽  
Christiane C. Schmullius
Keyword(s):  
Remote Sensing ◽  
Reference Data ◽  
Spatial Scales ◽  
Test Site ◽  
Earth Observation ◽  
Reference Database ◽  
Observation Data ◽  
Retrieval Models ◽  
In Situ Data

Abstract. Ground reference data are a prerequisite for the calibration, update, and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations in the visible and infrared range. In situ data were collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato, and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance factors, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data were gathered at an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT 5, Landsat-7 and -8) with a cloud coverage  ≤  25 % are available for 10 and 15 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. Here, the experimental design of the field campaigns, and methods employed in the determination of all parameters, are described in detail. Insights into the database are provided and potential fields of application are discussed. The data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (https://doi.org/10.1594/PANGAEA.874251).

Evaluation of Earth System Model and Atmospheric Inversion using Total Column CO2 Observations from GOSAT and OCO-2

2020 ◽  
Author(s):  
Prabir K Patra ◽  
Tomohiro Hajima ◽  
Ryu Saito ◽  
Naveen Chandra ◽  
Yukio Yoshida ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Greenhouse Gases ◽  
Satellite Remote Sensing ◽  
Inverse Model ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Area Index ◽  
In Situ Data

Abstract The measurements of one of the major greenhouse gases, carbon dioxide (CO2), are being made using dedicated satellite remote sensing since the launch of the greenhouse gases observing satellite (GOSAT) by Japan Aerospace Exploration Agency (JAXA) in 2009 and National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2). In the past 10 years, estimation of CO2 fluxes from land and ocean using the earth system models (ESMs) and inverse modelling of in situ atmospheric CO2 data have also made significant progress. In this article, we attempt, for the first time, to evaluate the CO2 fluxes simulated by an earth system model (MIROC-ES2L) using GOSAT observations and the fluxes estimated by an inverse model (MIROC4-Inv) for the period 2009-2014. Further, we use the OCO-2 measurements for testing the consistency of inversion results for the period 2014-2018, along with the GOSAT data. Both MIROC-ES2L and MIROC4-Inv fluxes are used in the MIROC4-atmospheric chemistry transport model (referred to as ACTM_ES2LF and ACTM_InvF, respectively) for calculating CO2 concentrations that are sampled at the time and location of the satellite measurements. Our results suggest the inverse model using in situ data are more consistent with the OCO-2 retrievals, compared to those of the GOSAT XCO2 data, suggesting possible improvements in the present GOSAT retrieval system by better accounting for the degradation correction of the Thermal And Near infrared Sensor for carbon Observations - Fourier Transform Spectrometer (TANSO-FTS). The ACTM_ES2LF simulation shows a slightly weaker seasonal cycle for the meridional profiles of CO2 fluxes, compared to that from the ACTM_InvF. This difference is revealed by greater XCO2 differences for ACTM_ES2LF vs GOSAT, compared to those of ACTM_InvF vs GOSAT. Using remote sensing based global products of leaf area index (LAI) and gross primary productivity (GPP) over land, we show a weaker sensitivity of MIROC-ES2L biospheric activities to the weather and climate in the tropical regions. Our results clearly suggest the usefulness of XCO2 measurements by satellite remote sensing for evaluation of large-scale ESMs, which so far remained untested by the sparse in situ data.

Evaluation of Earth Systems Models and Atmospheric Inversions using Total Column CO2 Observations from GOSAT and OCO-2

2020 ◽  
Author(s):  
Prabir K Patra ◽  
Tomohiro Hajima ◽  
Ryu Saito ◽  
Naveen Chandra ◽  
Yukio Yoshida ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Greenhouse Gases ◽  
Atmospheric Chemistry ◽  
Satellite Remote Sensing ◽  
Seasonal Cycle ◽  
Inverse Model ◽  
Earth System ◽  
Area Index ◽  
In Situ Data

Abstract The measurements of one of the major greenhouse gases, carbon dioxide (CO 2 ), are being made using dedicated satellite remote sensing since the launch of the greenhouse gases observing satellite (GOSAT) by JAXA in 2009 and NASA’s Orbiting Carbon Observatory-2 (OCO-2). In the past 10 years, estimation of CO 2 fluxes from land and ocean using the earth system models (ESMs) and inverse modelling of in situ atmospheric CO 2 data have also made significant progress. In this article, we attempt, for the first time, to evaluate the CO 2 fluxes simulated by an earth system model (MIROC-ES2L) using GOSAT observations and the fluxes estimated by an inverse model (MIROC4-Inv) for the period 2009-2014. Further, we use the OCO-2 measurements for testing the consistency of inversion results for the period 2014-2018, along with the GOSAT data. Both MIROC-ES2L and MIROC4-Inv fluxes are used in the MIROC4-atmospheric chemistry transport model (referred to as ACTM_ES2LF and ACTM_InvF, respectively) for calculating CO 2 concentrations that are sampled at the time and location of the satellite measurements. Our results suggest the inverse model using in situ data are more consistent with the OCO-2 retrievals, compared to those of the GOSAT XCO 2 data, suggesting possible improvements in the present GOSAT retrieval system by better accounting for the degradation correction of the TANSO-FTS. The ACTM_ES2LF simulation shows a slightly weaker seasonal cycle for the meridional profiles of CO 2 fluxes, compared to that from the ACTM_InvF. This difference is revealed by greater ACTM_ES2LF vs GOSAT differences, compared to those of ACTM_InvF vs GOSAT. We also find that the simulated seasonal cycle amplitude of XCO 2 by ACTM_ES2LF are slightly weaker compared to those observed by GOSAT or ACTM_InvF. Using remote sensing based global products of leaf area index (LAI) and gross primary productivity (GPP) over land, we show a weaker sensitivity of MIROC-ES2L biospheric activities to the weather and climate in the tropical regions. Our results clearly suggest the usefulness of XCO 2 measurements by satellite remote sensing for evaluation of large-scale ESMs, which so far remained untested by the sparse in situ data.

scholarly journals Identifying Flood Events over the Poyang Lake Basin Using Multiple Satellite Remote Sensing Observations, Hydrological Models and In Situ Data

2018 ◽  
Vol 10 (5) ◽  
pp. 713 ◽  
Author(s):  
Hao Zhou ◽  
Zhicai Luo ◽  
Natthachet Tangdamrongsub ◽  
Zebing Zhou ◽  
Lijie He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Poyang Lake ◽  
Lake Basin ◽  
Hydrological Models ◽  
Flood Events ◽  
In Situ Data ◽  
Poyang Lake Basin

scholarly journals Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study of Ecosystem Functioning and Conservation at Large Spatial Scales

2020 ◽  
pp. 449-484
Author(s):  
Franziska Schrodt ◽  
Betsabe de la Barreda Bautista ◽  
Christopher Williams ◽  
Doreen S. Boyd ◽  
Gabriela Schaepman-Strub ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Ecosystem Functioning ◽  
Spatial Scales ◽  
Auxiliary Information ◽  
Critical Discussion ◽  
In Situ Data ◽  
Ecosystem Risk ◽  
Patterns And Processes ◽  
Socioeconomic Data

AbstractAssessing patterns and processes of plant functional, taxonomic, genetic, and structural biodiversity at large scales is essential across many disciplines, including ecosystem management, agriculture, ecosystem risk and service assessment, conservation science, and forestry. In situ data housed in databases necessary to perform such assessments over large parts of the world are growing steadily. Integrating these in situ data with remote sensing (RS) products helps not only to improve data completeness and quality but also to account for limitations and uncertainties associated with each data product. Here, we outline how auxiliary environmental and socioeconomic data might be integrated with biodiversity and RS data to expand our knowledge about ecosystem functioning and inform the conservation of biodiversity. We discuss concepts, data, and methods necessary to assess plant species and ecosystem properties across scales of space and time and provide a critical discussion of outstanding issues.

scholarly journals In situ data supporting remote sensing estimation of spruce forest parameters at the ecosystem station Bílý Kříž

Beskydy ◽  
2017 ◽  
Vol 10 (1-2) ◽  
pp. 75-86 ◽  
Author(s):  
Lucie Homolová ◽  
Růžena Janoutová ◽  
Petr Lukeš ◽  
Jan Hanuš ◽  
Jan Novotný ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Scales ◽  
Carotenoid Content ◽  
Area Index ◽  
Needle Age ◽  
Quantitative Parameters ◽  
In Situ Data

Remote sensing offers an effective way of mapping vegetation parameters in a spatially continuous manner, at larger spatial scales and repeatedly in time compared to traditional in situ mapping approaches that are typically accurate, but limited to a few distributed location and few repetitions. In case of forest ecosystems, remote sensing allow to assess quantitative parameters or indicators related to forest health status such as leaf area index, leaf pigment content, chlorophyll fluorescence, etc. Development, calibration and validation of remote sensing-based methods, however, still rely on supportive in situ data. The aim of this contribution is to introduce the individual in situ components in the framework for the retrieval of forest quantitative parameters from airborne imaging spectroscopy data. All measurements were acquired during an extensive in situ/flight campaign that took place at the Norway spruce dominated study site Bílý Kříž (Moravian-Silesian Beskydy Mts., Czech Republic) during August 2016. In addition to airborne remote sensing data acquisition, the in situ activities included terrestrial laser scanning for tree 3D modelling, measurements of needle biochemical and optical properties, leaf area index measurements and spectral measurements of various natural and artificial surfaces. Leaf pigments varied between 25.2 and 49.1 µg cm-2 for chlorophyll a+b content, 4.9 – 10.6 µg cm-2 for carotenoid content depending on needle age and its adaptation to sun illumination, whereas ratio between the two pigments was stable around 4.6 – 5. 3. Specific leaf area of spruce needles varied between 49.3 and 105.8 cm2 g-1, being the highest for the shade adapted needles of the current year. Leaf area index of spruce stands of various age and densities varied between 5.3 and 9. 3.

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