scholarly journals Upscaling Evapotranspiration from a Single-Site to Satellite Pixel Scale

2021 ◽  
Vol 13 (20) ◽  
pp. 4072
Author(s):  
Xiang Li ◽  
Shaomin Liu ◽  
Xiaofan Yang ◽  
Yanfei Ma ◽  
Xinlei He ◽  
...  
Keyword(s):  
Land Surface ◽  
Main Idea ◽  
Gaussian Process Regression ◽  
Ground Truth ◽  
Remotely Sensed ◽  
Heihe River ◽  
Optimal Method ◽  
Integrated Method ◽  
Comprehensive Framework ◽  
Pixel Scale

It is of great significance for the validation of remotely sensed evapotranspiration (ET) products to solve the spatial-scale mismatch between site observations and remote sensing estimations. To overcome this challenge, this paper proposes a comprehensive framework for obtaining the ground truth ET at the satellite pixel scale (1 × 1 km resolution in MODIS satellite imagery). The main idea of this framework is to first quantitatively evaluate the spatial heterogeneity of the land surface, then combine the eddy covariance (EC)-observed ET (ET_EC) to be able to compare and optimize the upscaling methods (among five data-driven and three mechanism-driven methods) through direct validation and cross-validation, and finally use the optimal method to obtain the ground truth ET at the satellite pixel scale. The results showed that the ET_EC was superior over homogeneous underlying surfaces with a root mean square error (RMSE) of 0.34 mm/d. Over moderately and highly heterogeneous underlying surfaces, the Gaussian process regression (GPR) method performed better (the RMSEs were 0.51 mm/d and 0.60 mm/d, respectively). Finally, an integrated method (namely, using the ET_EC for homogeneous surfaces and the GPR method for moderately and highly heterogeneous underlying surfaces) was proposed to obtain the ground truth ET over fifteen typical underlying surfaces in the Heihe River Basin. Furthermore, the uncertainty of ground truth ET was quantitatively evaluated. The results showed that the ground truth ET at the satellite pixel scale is relatively reliable with an uncertainty of 0.02–0.41 mm/d. The upscaling framework proposed in this paper can be used to obtain the ground truth ET at the satellite pixel scale and its uncertainty, and it has great potential to be applied in more regions around the globe for remotely sensed ET products’ validation.

scholarly journals A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

2021 ◽  
Vol 13 (14) ◽  
pp. 2838
Author(s):  
Yaping Mo ◽  
Yongming Xu ◽  
Huijuan Chen ◽  
Shanyou Zhu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Urban Heat Islands ◽  
Reconstruction Algorithms ◽  
Gap Filling ◽  
Practical Applications ◽  
Data Gaps

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

scholarly journals High-Resolution Monitoring and Assessment of Evapotranspiration and Gross Primary Production Using Remote Sensing in a Typical Arid Region

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 396
Author(s):  
Junxia Yan ◽  
Yanfei Ma ◽  
Dongyun Zhang ◽  
Zechen Li ◽  
Weike Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Surface ◽  
Arid Region ◽  
Management Practices ◽  
Gross Primary Production ◽  
Irrigation District ◽  
Heihe River ◽  
Freshwater Resource ◽  
Oasis Area

Land surface evapotranspiration (ET) and gross primary productivity (GPP) are critical components in terrestrial ecosystems with water and carbon cycles. Large-scale, high-resolution, and accurately quantified ET and GPP values are important fundamental data for freshwater resource management and help in understanding terrestrial carbon and water cycles in an arid region. In this study, the revised surface energy balance system (SEBS) model and MOD17 GPP algorithm were used to estimate daily ET and GPP at 100 m resolution based on multi-source satellite remote sensing data to obtain surface biophysical parameters and meteorological forcing data as input variables for the model in the midstream oasis area of the Heihe River Basin (HRB) from 2010 to 2016. Then, we further calculated the ecosystem water-use efficiency (WUE). We validated the daily ET, GPP, and WUE from ground observations at a crop oasis station and conducted spatial intercomparisons of monthly and annual ET, GPP, and WUE at the irrigation district and cropland oasis scales. The site-level evaluation results show that ET and GPP had better performance than WUE at the daily time scale. Specifically, the deviations in the daily ET, GPP, and WUE data compared with ground observations were small, with a root mean square error (RMSE) and mean absolute percent error (MAPE) of 0.75 mm/day and 26.59%, 1.13 gC/m2 and 36.62%, and 0.50 gC/kgH2O and 39.83%, respectively. The regional annual ET, GPP, and WUE varied from 300 to 700 mm, 200 to 650 gC/m2, and 0.5 to 1.0 gC/kgH2O, respectively, over the entire irrigation oasis area. It was found that annual ET and GPP were greater than 550 mm and 500 gC/m2, and annual oasis cropland WUE had strong invariability and was maintained at approximately 0.85 gC/kgH2O. The spatial intercomparisons from 2010 to 2016 revealed that ET had similar spatial patterns to GPP due to tightly coupled carbon and water fluxes. However, the WUE spatiotemporal patterns were slightly different from both ET and GPP, particularly in the early and late growing seasons for the oasis area. Our results demonstrate that spatial full coverage and reasonably fine spatiotemporal variation and variability could significantly improve our understanding of water-saving irrigation strategies and oasis agricultural water management practices in the face of water shortage issues.

scholarly journals Improving the Evapotranspiration Estimation under Cloudy Condition by Extending the Ts-VI Triangle Model

2021 ◽  
Vol 13 (8) ◽  
pp. 1516
Author(s):  
Boyang Li ◽  
Yaokui Cui ◽  
Xiaozhuang Geng ◽  
Huan Li
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Energy Exchange ◽  
Vegetation Index ◽  
Regional Scale ◽  
Reconstruction Algorithm ◽  
Heihe River ◽  
Main Process ◽  
In Situ Data

Evapotranspiration (ET) of soil-vegetation system is the main process of the water and energy exchange between the atmosphere and the land surface. Spatio-temporal continuous ET is vitally important to agriculture and ecological applications. Surface temperature and vegetation index (Ts-VI) triangle ET model based on remote sensing land surface temperature (LST) is widely used to monitor the land surface ET. However, a large number of missing data caused by the presence of clouds always reduces the availability of the main parameter LST, thus making the remote sensing-based ET estimation unavailable. In this paper, a method to improve the availability of ET estimates from Ts-VI model is proposed. Firstly, continuous LST product of the time series is obtained using a reconstruction algorithm, and then, the reconstructed LST is applied to the estimate ET using the Ts-VI model. The validation in the Heihe River Basin from 2009 to 2011 showed that the availability of ET estimates is improved from 25 days per year (d/yr) to 141 d/yr. Compared with the in situ data, a very good performance of the estimated ET is found with RMSE 1.23 mm/day and R2 0.6257 at point scale and RMSE 0.32 mm/day and R2 0.8556 at regional scale. This will improve the understanding of the water and energy exchange between the atmosphere and the land surface, especially under cloudy conditions.

scholarly journals Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

2018 ◽  
Vol 7 (7) ◽  
pp. 275 ◽  
Author(s):  
Bipin Acharya ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Laxman Khanal ◽  
Shahid Naeem ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Dengue Fever ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Remotely Sensed ◽  
Cross Correlation Analysis

Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

scholarly journals Remotely sensed land-surface energy fluxes at sub-field scale in heterogeneous agricultural landscape and coniferous plantation

2014 ◽  
Vol 11 (18) ◽  
pp. 5021-5046 ◽  
Author(s):  
R. Guzinski ◽  
H. Nieto ◽  
R. Jensen ◽  
G. Mendiguren
Keyword(s):  
Surface Energy ◽  
Spatial Resolution ◽  
Land Surface ◽  
Agricultural Landscape ◽  
Remotely Sensed ◽  
Energy Fluxes ◽  
Surface Energy Fluxes ◽  
Coniferous Plantation ◽  
Polar Orbiting Satellite ◽  
Agricultural Site

Abstract. In this study we evaluate a methodology for disaggregating land surface energy fluxes estimated with the Two-Source Energy Balance (TSEB)-based Dual-Temperature Difference (DTD) model which uses day and night polar orbiting satellite observations of land surface temperature (LST) as a remotely sensed input. The DTD model is run with MODIS input data at a spatial resolution of around 1 km while the disaggregation uses Landsat observations to produce fluxes at a nominal spatial resolution of 30 m. The higher-resolution modelled fluxes can be directly compared against eddy covariance (EC)-based flux tower measurements to ensure more accurate model validation and also provide a better visualization of the fluxes' spatial patterns in heterogeneous areas allowing for development of, for example, more efficient irrigation practices. The disaggregation technique is evaluated in an area covered by the Danish Hydrological Observatory (HOBE), in the west of the Jutland peninsula, and the modelled fluxes are compared against measurements from two flux towers: the first one in a heterogeneous agricultural landscape and the second one in a homogeneous conifer plantation. The results indicate that the coarse-resolution DTD fluxes disaggregated at Landsat scale have greatly improved accuracy as compared to high-resolution fluxes derived directly with Landsat data without the disaggregation. At the agricultural site the disaggregated fluxes display small bias and very high correlation (r ≈ 0.95) with EC-based measurements, while at the plantation site the results are encouraging but still with significant errors. In addition, we introduce a~modification to the DTD model by replacing the "parallel" configuration of the resistances to sensible heat exchange by the "series" configuration. The latter takes into account the in-canopy air temperature and substantially improves the accuracy of the DTD model.

scholarly journals Assessing the relationship between environmental factors and malaria vector breeding sites in Swaziland using multi-scale remotely sensed data

2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Sabelo Nick Dlamini ◽  
Jonas Franke ◽  
Penelope Vounatsou
Keyword(s):  
Environmental Factors ◽  
Spatial Resolution ◽  
Malaria Vector ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Breeding Sites ◽  
Multi Scale ◽  
The Relationship

Many entomological studies have analyzed remotely sensed data to assess the relationship between malaria vector distribution and the associated environmental factors. However, the high cost of remotely sensed products with high spatial resolution has often resulted in analyses being conducted at coarse scales using open-source, archived remotely sensed data. In the present study, spatial prediction of potential breeding sites based on multi-scale remotely sensed information in conjunction with entomological data with special reference to presence or absence of larvae was realized. Selected water bodies were tested for mosquito larvae using the larva scooping method, and the results were compared with data on land cover, rainfall, land surface temperature (LST) and altitude presented with high spatial resolution. To assess which environmental factors best predict larval presence or absence, Decision Tree methodology and logistic regression techniques were applied. Both approaches showed that some environmental predictors can reliably distinguish between the two alternatives (existence and non-existence of larvae). For example, the results suggest that larvae are mainly present in very small water pools related to human activities, such as subsistence farming that were also found to be the major determinant for vector breeding. Rainfall, LST and altitude, on the other hand, were less useful as a basis for mapping the distribution of breeding sites. In conclusion, we found that models linking presence of larvae with high-resolution land use have good predictive ability of identifying potential breeding sites.

scholarly journals Impact of ASAR soil moisture data on the MM5 precipitation forecast for the Tanaro flood event of April 2009

2011 ◽  
Vol 11 (12) ◽  
pp. 3135-3149 ◽  
Author(s):  
G. Panegrossi ◽  
R. Ferretti ◽  
L. Pulvirenti ◽  
N. Pierdicca
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Ground Truth ◽  
Rain Gauge ◽  
Precipitation Forecast ◽  
Flood Event ◽  
Strong Impact ◽  
Moisture Field ◽  
The Impact

Abstract. The representation of land-atmosphere interactions in weather forecast models has a strong impact on the Planetary Boundary Layer (PBL) and, in turn, on the forecast. Soil moisture is one of the key variables in land surface modelling, and an inadequate initial soil moisture field can introduce major biases in the surface heat and moisture fluxes and have a long-lasting effect on the model behaviour. Detecting the variability of soil characteristics at small scales is particularly important in mesoscale models because of the continued increase of their spatial resolution. In this paper, the high resolution soil moisture field derived from ENVISAT/ASAR observations is used to derive the soil moisture initial condition for the MM5 simulation of the Tanaro flood event of April 2009. The ASAR-derived soil moisture field shows significantly drier conditions compared to the ECMWF analysis. The impact of soil moisture on the forecast has been evaluated in terms of predicted precipitation and rain gauge data available for this event have been used as ground truth. The use of the drier, highly resolved soil moisture content (SMC) shows a significant impact on the precipitation forecast, particularly evident during the early phase of the event. The timing of the onset of the precipitation, as well as the intensity of rainfall and the location of rain/no rain areas, are better predicted. The overall accuracy of the forecast using ASAR SMC data is significantly increased during the first 30 h of simulation. The impact of initial SMC on the precipitation has been related to the change in the water vapour field in the PBL prior to the onset of the precipitation, due to surface evaporation. This study represents a first attempt to establish whether high resolution SAR-based SMC data might be useful for operational use, in anticipation of the launch of the Sentinel-1 satellite.

scholarly journals Combining remotely sensed data using aggregation algorithms

1998 ◽  
Vol 2 (2/3) ◽  
pp. 149-158 ◽  
Author(s):  
W. J. Shuttleworth
Keyword(s):  
Surface Energy ◽  
Land Surface ◽  
Remotely Sensed ◽  
Surface Radiation ◽  
Control Surface ◽  
Future Research ◽  
Practical Implementation ◽  
Remotely Sensed Data ◽  
Theoretical Concepts ◽  
Average Value

Abstract. This paper describes a strategic approach for providing documentation of the surface energy exchange for heterogeneous land surfaces via the simultaneous, four-dimensional assimilation of several streams of remotely sensed data into a coupled land surface-atmosphere model. The basic concepts and underlying theory behind this proposed approach are presented with the intent that this will guide, facilitate, and stimulate future research focused on its practical implementation when appropriate data from the Earth Observing System (EOS) become available. The theoretical concepts that underlie the approach are derived from relationships between the values of parameters which control surface exchanges at pixel (or patch) scale and the area-average value of equivalent parameters applicable at larger, grid scale. A three-step implementation method is proposed which involves (a) estimating grid-average surface radiation fluxes from appropriate remotely sensed data; (b) absorbing these radiation flux estimates into a four-dimensional data assimilation model in which grid-average values of vegetation-related parameters are calculated from pertinent remotely sensed data using the equations that link pixel and grid scales; and (c) improving the resulting estimate of the surface energy balance-again using scale-linking equations by estimating the effect of soil-moisture availability, perhaps assuming that cloud-free pixels are an unbiased subsample of all the pixels in the grid square.

scholarly journals GIS and Remote Sensing Application in Water Management in Agriculture Field

2020 ◽  
Author(s):  
Sassi Mohamed Taher
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Water Resources ◽  
Land Surface ◽  
Remotely Sensed ◽  
Irrigated Agriculture ◽  
Accurate Information ◽  
Remotely Sensed Data ◽  
Gis And Remote Sensing ◽  
Practical Applications

This document is meant to demonstrate the potential uses of remote sensing in managing water resources for irrigated agriculture and to create awareness among potential users. Researchers in various international programs have studied the potential use of remotely sensed data to obtain accurate information on land surface processes and conditions. These studies have demonstrated that quantitative assessment of the soil-vegetation-atmosphere transfer processes can lead to a better understanding of the relationships between crop growth and water management. Remote sensing and GIS was used to map the agriculture area and for detect the change. This was very useful for mapping availability and need of water resources but the problem was concentrating in data collection and analysis because this kind of information and expertise are not available in all country in the world mainly in the developing and under developed country or third world country. However, even though considerable progress has been made over the past 20 years in research applications, remotely sensed data remain underutilized by practicing water resource managers. This paper seeks to bridge the gap between researchers and practitioners first, by illustrating where research tools and techniques have practical applications and, second, by identifying real problems that remote sensing could solve. An important challenge in the field of water resources is to utilize the timely, objective and accurate information provided by remote sensing.

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