scholarly journals Continuous Daily Evapotranspiration with Optical Spaceborne Observations at Sub-Kilometre Spatial Resolution

2020 ◽  
Vol 12 (14) ◽  
pp. 2218
Author(s):  
José Miguel Barrios ◽  
Alirio Arboleda ◽  
Jan De Pue ◽  
Jaroslaw Chormanski ◽  
Françoise Gellens-Meulenberghs
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Land Surface ◽  
Validation Dataset ◽  
Model Parameters ◽  
Biophysical Parameters ◽  
Daily Evapotranspiration ◽  
Analysis Programme ◽  
Key Parameter ◽  
Modelling Approach

Evapotranspiration (ET) is a key parameter in the description of the energy and water fluxes over land. Continuous and spatially detailed ET simulations are thus required for a number of scientific and management-related purposes. These conditions are determined by the modelling approach and the composition of the forcing dataset. This study aimed at simulating daily ET in a diversity of climate and land cover conditions at a spatial resolution of ∼1 km and higher. The modelling approach was based on the algorithm driving the ET product developed and set in operations in the framework of the Satellite Application Facility on Land Surface Analysis programme (LSA-SAF). The implemented algorithm allowed the ingestion of biophysical parameters derived from SPOT-V and PROBA-V observations developed by the Copernicus Global Land Programme, as well as other model parameters at a similar spatial resolution. The model was tested at an ∼1 km spatial resolution in over 40 sites located in different climate and land cover contexts. The implementation at ∼300 m was tested in the upper Biebrza basin, in Poland. The simulations correlated well with the validation dataset (r2 > 0.75 in 80% of sites) and exhibited root mean squared values lower than 1 mm/day in 80% of the cases. The results also pointed to the need for refining the accuracy of soil moisture data sources, especially in dry areas. The results showed the ability of the modelling approach and the SPOT-V/PROBA-V missions to support the generation of long ET time series. They also opened the gate to incorporate Sentinel-3 in ET continuous modelling.

scholarly journals Evaluating and improving the Community Land Model's sensitivity to land cover

2018 ◽  
Vol 15 (15) ◽  
pp. 4731-4757 ◽  
Author(s):  
Ronny Meier ◽  
Edouard L. Davin ◽  
Quentin Lejeune ◽  
Mathias Hauser ◽  
Yan Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Surface ◽  
Soil Column ◽  
Plant Functional Type ◽  
Model Parameters ◽  
Functional Type ◽  
Land Surface Models ◽  
Surface Models ◽  
Open Land

Abstract. Modeling studies have shown the importance of biogeophysical effects of deforestation on local climate conditions but have also highlighted the lack of agreement across different models. Recently, remote-sensing observations have been used to assess the contrast in albedo, evapotranspiration (ET), and land surface temperature (LST) between forest and nearby open land on a global scale. These observations provide an unprecedented opportunity to evaluate the ability of land surface models to simulate the biogeophysical effects of forests. Here, we evaluate the representation of the difference of forest minus open land (i.e., grassland and cropland) in albedo, ET, and LST in the Community Land Model version 4.5 (CLM4.5) using various remote-sensing and in situ data sources. To extract the local sensitivity to land cover, we analyze plant functional type level output from global CLM4.5 simulations, using a model configuration that attributes a separate soil column to each plant functional type. Using the separated soil column configuration, CLM4.5 is able to realistically reproduce the biogeophysical contrast between forest and open land in terms of albedo, daily mean LST, and daily maximum LST, while the effect on daily minimum LST is not well captured by the model. Furthermore, we identify that the ET contrast between forests and open land is underestimated in CLM4.5 compared to observation-based products and even reversed in sign for some regions, even when considering uncertainties in these products. We then show that these biases can be partly alleviated by modifying several model parameters, such as the root distribution, the formulation of plant water uptake, the light limitation of photosynthesis, and the maximum rate of carboxylation. Furthermore, the ET contrast between forest and open land needs to be better constrained by observations to foster convergence amongst different land surface models on the biogeophysical effects of forests. Overall, this study demonstrates the potential of comparing subgrid model output to local observations to improve current land surface models' ability to simulate land cover change effects, which is a promising approach to reduce uncertainties in future assessments of land use impacts on climate.

scholarly journals Novel methods of estimating relative pollen productivity: A key parameter for reconstruction of past land cover from pollen records

2019 ◽  
Vol 43 (6) ◽  
pp. 731-753 ◽  
Author(s):  
Yiman Fang ◽  
Chunmei Ma ◽  
M Jane Bunting
Keyword(s):  
Land Cover ◽  
Empirical Data ◽  
Alternative Methods ◽  
Model Parameters ◽  
Value Analysis ◽  
Pollen Productivity ◽  
Tropical China ◽  
The Relationship ◽  
Pollen Records ◽  
Key Parameter

Reconstructing land cover from pollen data using mathematical models of the relationship between them has the potential to translate the many thousand pollen records produced over the last 100 years (over 2300 radiocarbon-dated pollen records exist for the UK alone) into formats relevant to ecologists, archaeologists and climate scientists. However, the reliability of these reconstructions depends on model parameters. A key parameter is Relative Pollen Productivity (RPP), usually estimated from empirical data using ‘Extended R Value analysis’ (ERV analysis). Lack of RPP estimates for many regions is currently a major limitation on reconstructing global land cover. We present two alternatives to ERV analysis, the Modified Davis Method and an iteration method, which use the same underlying model of the relationship between pollen and vegetation to estimate RPP from empirical data, but with different assumptions. We test them in simulation against ERV analysis, and use a case study of a problematic empirical dataset to determine whether they have the potential to increase the speed and geographic range of RPP estimation. The two alternative methods are shown to perform at least as well as ERV analysis in simulation. We also present new RPP estimates from southeastern sub-tropical China for nine taxa estimated using the Modified Davis Method. Adding these two methods to the ‘toolkit’ for land cover reconstruction from pollen records opens up the possibility to estimate a key parameter from existing datasets with less field time than using current methods. This can both speed up the inclusion of more of the globe in past land cover mapping exercises such as the PAGES Landcover6k working group and improve our understanding of how this parameter varies within a single taxon and the factors control that variation.

scholarly journals Development and evaluation of 0.05° terrestrial water storage estimates using CABLE land surface model and GRACE data assimilation

2021 ◽  
Author(s):  
Natthachet Tangdamrongsub ◽  
Michael F. Jasinski ◽  
Peter Shellito
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

Abstract. Accurate estimation of terrestrial water storage (TWS) at a meaningful spatiotemporal resolution is important for reliable assessments of regional water resources and climate variability. Individual components of TWS include soil moisture, snow, groundwater, and canopy storage and can be estimated from the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. The spatial resolution of CABLE is currently limited to 0.5° by the resolution of soil and vegetation datasets that underlie model parameterizations, posing a challenge to using CABLE for hydrological applications at a local scale. This study aims to improve the spatial detail (from 0.5° to 0.05°) and timespan (1981–2012) of CABLE TWS estimates using rederived model parameters and high-resolution meteorological forcing. In addition, TWS observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are assimilated into CABLE to improve TWS accuracy. The success of the approach is demonstrated in Australia, where multiple ground observation networks are available for validation. The evaluation process is conducted using four different case studies that employ different model spatial resolutions and include or omit GRACE data assimilation (DA). We find that the CABLE 0.05° developed here improves TWS estimates in terms of accuracy, spatial resolution, and long-term water resource assessment reliability. The inclusion of GRACE DA increases the accuracy of groundwater storage (GWS) estimates and has little impact on surface soil moisture or evapotranspiration. The use of improved model parameters and improved state estimations (via GRACE DA) together is recommended to achieve the best GWS accuracy. The workflow elaborated in this paper relies only on publicly accessible global datasets, allowing reproduction of the 0.05° TWS estimates in any study region.

scholarly journals Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada

2019 ◽  
Vol 11 (19) ◽  
pp. 2286
Author(s):  
Libo Wang ◽  
Paul Bartlett ◽  
Darren Pouliot ◽  
Ed Chan ◽  
Céline Lamarche ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Land Surface ◽  
Forest Cover ◽  
Reference Sample ◽  
Tree Cover ◽  
Initial Surface ◽  
Forest Cover Change ◽  
Global Land Cover ◽  
Global Land

Global land cover information is required to initialize land surface and Earth system models. In recent years, new land cover (LC) datasets at finer spatial resolutions have become available while those currently implemented in most models are outdated. This study assesses the applicability of the Climate Change Initiative (CCI) LC product for use in the Canadian Land Surface Scheme (CLASS) through comparison with finer resolution datasets over Canada, assisted with reference sample data and a vegetation continuous field tree cover fraction dataset. The results show that in comparison with the finer resolution maps over Canada, the 300 m CCI product provides much improved LC distribution over that from the 1 km GLC2000 dataset currently used to provide initial surface conditions in CLASS. However, the CCI dataset appears to overestimate needleleaf forest cover especially in the taiga-tundra transition zone of northwestern Canada. This may have partly resulted from limited availability of clear sky MEdium Resolution Imaging Spectrometer (MERIS) images used to generate the CCI classification maps due to the long snow cover season in Canada. In addition, changes based on the CCI time series are not always consistent with those from the MODIS or a Landsat-based forest cover change dataset, especially prior to 2003 when only coarse spatial resolution satellite data were available for change detection in the CCI product. It will be helpful for application in global simulations to determine whether these results also apply to other regions with similar landscapes, such as Eurasia. Nevertheless, the detailed LC classes and finer spatial resolution in the CCI dataset provide an improved reference map for use in land surface models in Canada. The results also suggest that uncertainties in the current cross-walking tables are a major source of the often large differences in the plant functional types (PFT) maps, and should be an area of focus in future work.

scholarly journals Evaluating the SSEBop and RSPMPT Models for Irrigated Fields Daily Evapotranspiration Mapping with MODIS and CMADS Data

Agriculture ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 424
Author(s):  
Qifeng Zhuang ◽  
Yintao Shi ◽  
Hua Shao ◽  
Gang Zhao ◽  
Dong Chen
Keyword(s):  
Remote Sensing ◽  
Surface Energy ◽  
Land Surface ◽  
Surface Energy Balance ◽  
Model Parameters ◽  
Agricultural Water ◽  
Energy Component ◽  
Agricultural Water Management ◽  
Daily Evapotranspiration ◽  
Better Than

It is of great convenience to map daily evapotranspiration (ET) by remote sensing for agricultural water management without computing each surface energy component. This study used the operational simplified surface energy balance (SSEBop) and the remote sensing-based Penman–Monteith and Priestly–Taylor (RSPMPT) models to compute continuous daily ET over irrigated fields with the MODIS and CMADS data. The estimations were validated with eddy covariance (EC) measurements. Overall, the performance of RSPMPT with locally calibrated parameters was slightly better than that of SSEBop, with higher NSE (0.84 vs. 0.78) and R2 (0.86 vs. 0.81), lower RMSE (0.78 mm·d−1 vs. 0.90 mm·d−1), although it had higher bias (0.03 mm·d−1 vs. 0.01 mm·d−1) and PBias (1.41% vs. 0.59%). Due to the consideration of land surface temperature, the SSEBop was more sensitive to ET’s change caused by irrigation before sowing in March and had a lower PBias (6.7% vs. 39.8%) than RSPMPT. On cloudy days, the SSEBop is more likely to overestimate ET than the RSPMPT. To conclude, driven by MODIS and CMADS data, the two simple models can be easily applied to map daily ET over cropland. The SSEBop is more practical in the absence of measured data to optimize the RSPMPT model parameters.

scholarly journals Toward High-Resolution Soil Moisture Monitoring by Combining Active-Passive Microwave and Optical Vegetation Remote Sensing Products with Land Surface Model

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3924 ◽  
Author(s):  
Toride ◽  
Sawada ◽  
Aida ◽  
Koike
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Low Cost ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Coarse Resolution ◽  
Fine Resolution

The assimilation of radiometer and synthetic aperture radar (SAR) data is a promising recent technique to downscale soil moisture products, yet it requires land surface parameters and meteorological forcing data at a high spatial resolution. In this study, we propose a new downscaling approach, named integrated passive and active downscaling (I-PAD), to achieve high spatial and temporal resolution soil moisture datasets over regions without detailed soil data. The Advanced Microwave Scanning Radiometer (AMSR-E) and Phased Array-type L-band SAR (PALSAR) data are combined through a dual-pass land data assimilation system to obtain soil moisture at 1 km resolution. In the first step, fine resolution model parameters are optimized based on fine resolution PALSAR soil moisture and moderate-resolution imaging spectroradiometer (MODIS) leaf area index data, and coarse resolution AMSR-E brightness temperature data. Then, the 25 km AMSR-E observations are assimilated into a land surface model at 1 km resolution with a simple but computationally low-cost algorithm that considers the spatial resolution difference. Precipitation data are used as the only inputs from ground measurements. The evaluations at the two lightly vegetated sites in Mongolia and the Little Washita basin show that the time series of soil moisture are improved at most of the observation by the assimilation scheme. The analyses reveal that I-PAD can capture overall spatial trends of soil moisture within the coarse resolution radiometer footprints, demonstrating the potential of the algorithm to be applied over data-sparse regions. The capability and limitation are discussed based on the simple optimization and assimilation schemes used in the algorithm.

scholarly journals Constraining model parameters on remotely sensed evaporation: justification for distribution in ungauged basins?

2008 ◽  
Vol 5 (4) ◽  
pp. 2293-2318 ◽  
Author(s):  
H. C. Winsemius ◽  
H. H. G. Savenije ◽  
W. G. M. Bastiaanssen
Keyword(s):  
Land Cover ◽  
Water Balance ◽  
Land Surface ◽  
Root Zone ◽  
Remotely Sensed ◽  
Dry Season ◽  
Model Parameters ◽  
Model Structure ◽  
Spatial Discretization ◽  
Parameter Ranges

Abstract. In this study, land surface related parameter distributions of a conceptual semi-distributed hydrological model are estimated by employing time series of satellite-based evaporation estimates during the dry season as explanatory information. A key application for this approach is to identify part of the parameter distribution space in ungauged river basins without the need for ground data. The information, contained in the evaporation estimates implicitly imposes compliance of the model with the largest water balance term, evaporation, and a spatially and temporally realistic depletion of soil moisture within the dry season. Furthermore, the model results can provide a better understanding of the information density of remotely sensed evaporation. The approach has been applied to the ungauged Luangwa river basin (150 000 (km)2) in Zambia. Model units were delineated on the basis of similar land cover. For each model unit, model parameters for which evaporation is sensitive, have been conditioned on the evaporation estimates by means of Monte-Carlo sampling. The results show that behavioural parameter sets for model units with similar land cover, are indeed clustered. The clustering reveals hydrologically meaningful signatures in the parameter response surface: wetland-dominated areas (also called dambos) show optimal parameter ranges that reflect a relatively small unsaturated zone (due to the shallow rooting depth of the vegetation) and moisture stressed vegetation. The forested areas and evergreen highlands show parameter ranges that indicate a much deeper root zone and drought resistance. Unrealistic parameter ranges, found for instance in the high optimal field capacity values in the highlands may indicate model structural deficiencies. We believe that in these areas, groundwater uptake into the root zone and lateral movement of groundwater should be included in the model structure. Furthermore, a less distinct parameter clustering was found for forested model units. We hypothesize that this is due to the presence of 2 dominant forest types that differ substantially in their moisture regime. Therefore, this could indicate that the spatial discretization used in this study is oversimplified. This constraining step with remotely sensed data is useful for Bayesian updating in ungauged catchments. To this end trapezoidal shaped fuzzy membership functions were constructed that can be used to constrain parameter realizations in a second calibration step if more data becomes available. Especially in semi-arid areas such as the Luangwa basin, traditional rainfall-runoff calibration should be preceded by this step because evaporation represents a much larger term in the water balance than discharge and because it imposes spatial variability in the water balance. It justifies that land surface related parameters are distributed. Furthermore, the analysis reveals where hydrological processes may be ill-defined in the model structure and how accurate our spatial discretization is.

scholarly journals Evaluating and Improving the Community Land Model's Sensitivity to Land Cover

2018 ◽  
Author(s):  
Ronny Meier ◽  
Edouard L. Davin ◽  
Quentin Lejeune ◽  
Mathias Hauser ◽  
Yan Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Surface ◽  
Soil Column ◽  
Plant Functional Type ◽  
Model Parameters ◽  
Functional Type ◽  
Land Surface Models ◽  
Surface Models ◽  
Open Land

Abstract. Modelling studies have shown the importance of biogeophysical effects of deforestation on local climate conditions, but have also highlighted the lack of agreement across different models. Recently, remote sensing observations have been used to assess the contrast in albedo, evapotranspiration (ET), and land surface temperature (LST) between forest and nearby open land on a global scale. These observations provide an unprecedented opportunity to evaluate the ability of land surface models to simulate the biogeophysical effects of forests. Here, we evaluate the representation of the difference of forest minus open land (i.e., grassland and cropland) in albedo, ET, and LST in the Community Land Model version 4.5 (CLM4.5) using various remote sensing and in-situ data sources. To extract the local sensitivity to land cover we analyze plant functional type level output from global CLM4.5 simulations, using a model configuration that attributes a separate soil column to each plant functional type. Using the separated soil column configuration, CLM4.5 is able to realistically reproduce the biogeophysical contrast between forest and open land in terms of albedo, daily mean LST, and daily maximum LST, while the effect on daily minimum LST is not well captured by the model. Furthermore, we identify that the ET contrast between forests and open land is underestimated in CLM4.5 compared to observation-based products and even reversed in sign for some regions, even when considering uncertainties in these products. We then show that these biases can be partly alleviated by modifying several model parameters, such as the root distribution, the formulation of plant water uptake, the light limitation of photosynthesis, and the maximum rate of carboxylation. Furthermore, the ET contrast between forest and open land needs to be better constrained by observations in order to foster convergence amongst different land surface models on the biogeophysical effects of forests. Overall, this study demonstrates the potential of comparing sub-grid model output to local observations to improve current land surface models’ ability to simulate land cover change effects, which is a promising approach to reduce uncertainties in future assessments of land use impacts on climate.

scholarly journals Assessing the Impact of Land Cover Changes on Surface Urban Heat Islands with High-Spatial-Resolution Imagery on a Local Scale: Workflow and Case Study

2019 ◽  
Vol 11 (19) ◽  
pp. 5188 ◽  
Author(s):  
Peng Ren ◽  
Xinxin Zhang ◽  
Haoyan Liang ◽  
Qinglin Meng
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Spatial Configuration ◽  
Central Area ◽  
Local Scale ◽  
Systematic Analysis ◽  
Urban Heat ◽  
The Impact

Low-altitude remote sensing platform has been increasingly applied to observing local thermal environments due to its obvious advantage in spatial resolution and apparent flexibility in data acquisition. However, there is a general lack of systematic analysis for land cover (LC) classification, surface urban heat island (SUHI), and their spatial and temporal change patterns. In this study, a workflow is presented to assess the LC’s impact on SUHI, based on the visible and thermal infrared images with high spatial resolution captured by an unmanned airship in the central area of the Sino-Singapore Guangzhou Knowledge City in 2012 and 2015. Then, the accuracy assessment of LC classification and land surface temperature (LST) retrieval are performed. Finally, the commonly-used indexes in the field of satellites are applied to analyzing the spatial and temporal changes in the SUHI pattern on a local scale. The results show that the supervised maximum likelihood algorithm can deliver satisfactory overall accuracy and Kappa coefficient for LC classification; the root mean square error of the retrieved LST can reach 1.87 °C. Moreover, the LST demonstrates greater consistency with land cover type (LCT) and more fluctuation within an LCT on a local scale than on an urban scale. The normalized LST classified by the mean and standard deviation (STD) is suitable for the high-spatial situation; however, the thermal field level and the corresponded STD multiple need to be judiciously selected. This study exhibits an effective pathway to assess SUHI pattern and its changes using high-spatial-resolution images on a local scale. It is also indicated that proper landscape composition, spatial configuration and materials on a local scale exert greater impacts on SUHI.

scholarly journals Downscaling Land Surface Temperature from MODIS Dataset with Random Forest Approach over Alpine Vegetated Areas

2019 ◽  
Vol 11 (11) ◽  
pp. 1319 ◽  
Author(s):  
Paulina Bartkowiak ◽  
Mariapina Castelli ◽  
Claudia Notarnicola
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
High Temporal Resolution ◽  
Vegetation Fraction ◽  
Coarse Resolution ◽  
Area Of Interest

In this study, we evaluated three different downscaling approaches to enhance spatial resolution of thermal imagery over Alpine vegetated areas. Due to the topographical and land-cover complexity and to the sparse distribution of meteorological stations in the region, the remotely-sensed land surface temperature (LST) at regional scale is of major area of interest for environmental applications. Even though the Moderate Resolution Imaging Spectroradiometer (MODIS) LST fills the gap regarding high temporal resolution and length of the time-series, its spatial resolution is not adequate for mountainous areas. Given this limitation, random forest algorithm for downscaling LST to 250 m spatial resolution was evaluated. This study exploits daily MODIS LST with a spatial resolution of 1 km to obtain sub-pixel information at 250 m spatial resolution. The nonlinear relationship between coarse resolution MODIS LST (CR) and fine resolution (FR) explanatory variables was performed by building three different models including: (i) all pixels (BM), (ii) only pixels with more than 90% of vegetation content (EM1) and (iii) only pixels with 75% threshold of homogeneity for vegetated land-cover classes (EM2). We considered normalized difference vegetation index (NDVI) and digital elevation model (DEM) as predictors. The performances of the thermal downscaling methods were evaluated by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) between the downscaled dataset and Landsat LST. Validation indicated that the error values for vegetation fraction (EM1, EM2) were smaller than for basic modelling (BM). BM model determined averaged RMSE of 2.3 K and MAE of 1.8 K. Enhanced methods (EM1 and EM2) gave slightly better results yielding 2.2 K and 1.7 K for RMSE and MAE, respectively. In contrast to the EMs, BM showed a reduction of 22% and 18% of RMSE and MAE respectively with regard to Landsat and the original MODIS LST. Despite some limitations, mainly due to cloud contamination effect and coarse resolution pixel heterogeneity, random forest downscaling exhibits a large potential for producing improved LST maps.

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