Checking actual evapotranspiration model using remotely collected surface data: Case study

2020 ◽  
Author(s):  
Khil-Ha Lee
Keyword(s):  
Land Surface ◽  
Drought Index ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Characteristics ◽  
Actual Evapotranspiration ◽  
Particulate Matters ◽  
Minimum Number ◽  
Set Up ◽  
Land Surface Characteristics

<p>Drought is a reoccurring worldwide natural hazard that affects not only food production but also economics, health, and infrastructure. It is also known that regional drought condition is sensitive to the fine particulate matters (PM) and has relationships with future changes in fine dust levels and associated health impacts under climate change. This mode is strongly correlated to evapotranspiration and land surface conditions and drought index might be good when the actual evapotranspiration and the land surface characteristics are implicitly included in the formula. The procedure for estimating actual evapotranspiration is complex and scientists often tend to select simple model that does not require intensive field data. As a preliminary study this study checks the possibility of PT-JPL which is relatively simple and requires minimum number of observations for estimating local actual evapotranspiration. The model has no calibration, tuning, or spin-up for local adjustment. The model was set up for five representative stations in East Asia. The satellite-collected normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI) were used to describe the land surface characteristics. Meteorological information such as temperature, water vapor, radiation, and actual evapotranspiration was retrieved from AsiaFlux. The results show that the PT-JPL is promising for estimating local actual evapotranspiration. This study will extend to developing a drought index and its relationship to particulate matters (PM) in the near future.</p><p> </p><p><strong>Key words</strong>: Actual evapotranspiration, Particulate matters (PM), Drought, PT-JPL</p><p> </p><p><strong>Acknowledgement</strong></p><p>This work was supported by the National Research Foundation of Korea (NRF-2017-2017001809)</p>

scholarly journals A long-term monthly assessment of land surface temperature and normalized difference vegetation index using Landsat data

2021 ◽  
Vol 13 ◽  
Author(s):  
Subhanil Guha
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Negative Correlation ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Correlation Method ◽  
Surface Characteristics ◽  
Monthly Variation ◽  
Landsat Images

Abstract The present study assesses the monthly variation of land surface temperature (LST) and the relationship between LST and normalized difference vegetation index (NDVI) in Raipur City of India using one hundred and eighteen Landsat images from 1988 to 2019. The results show that a monthly variation is observed in the mean LST. The highest mean LST is found in April (38.79oC), followed by May (36.64oC), June (34.56oC), and March (32.11oC).The lowest mean LST is observed in January (23.01oC), followed by December (23.76oC), and November (25.83oC). A moderate range of mean LST is noticed in September (27.18oC), October (27.22oC), and February (27.88oC). Pearson's linear correlation method is used to correlate LST with NDVI. The LST-NDVI correlation is strong negative in October (-0.62), September (-0.55), and April (-0.51). The moderate negative correlation is developed in March (-0.40), May (-0.44), June (-0.47), and November (-0.39). A weak negative correlation is observed in December (-0.21), January (-0.24), and February (-0.29). The change in weather elements and variation in land surface characteristics contribute to the monthly fluctuation of mean LST and LST-NDVI correlation. The study will be an effective one for the town and country planners for their future estimation of land conversion.

scholarly journals Urban Heat Island in Kathmandu, Nepal: Evaluating Relationship between NDVI and LST from 2000 to 2018

2019 ◽  
Vol 8 (1) ◽  
pp. 17-29
Author(s):  
Bijesh Mishra ◽  
Jeremy Sandifer ◽  
Buddhi Raj Gyawali
Keyword(s):  
Land Cover ◽  
Urban Heat Island ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Characteristics ◽  
Heat Island ◽  
Rate Of Increase ◽  
Urban Heat

The term “urban heat island” (UHI) describes increased surface and atmospheric temperatures in an urban core relative to surrounding non-urbanized areas. Although the phenomenon has been studied to a great extent throughout the world, it is less understood for Kathmandu, Nepal. This study used the Moderate Resolution Imaging Spectro-radiometer (MODIS) 8-day product (MOD11A2) to evaluate land surface temperatures (LSTs), the MODIS-derived Normalized Difference Vegetation Index (NDVI) 16-day product (MOD13Q1) to quantify land surface characteristics, and the MODIS annual land cover classification product (MCD12Q1) to identify major land cover classes. We evaluated the spatial correlation between significant changes in LSTs and NDVI between 2000–2018. Overall, urban (permanently developed areas) LSTs were consistently greater than non-urban (forests and dynamic agriculture lands) LSTs; however, the rate of increase in temperature was higher outside the central Kathmandu developed urban area. Furthermore, significant changes in NDVI values over time were more widespread and not always spatially coincident with significant changes in LST values, particularly for forested land areas. These results provide insight into systematic planning of open and green areas, construction of new infrastructure in peripheral areas, and highlight the challenges in applying traditional UHI conceptual models to rapidly developing urban areas such as Kathmandu, Nepal.

scholarly journals Alternative methods to predict actual evapotranspiration illustrate the importance of accounting for phenology – Part 2: The event driven phenology model

2011 ◽  
Vol 8 (3) ◽  
pp. 5335-5378 ◽  
Author(s):  
V. Kovalskyy ◽  
G. M. Henebry
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Alternative Methods ◽  
Actual Evapotranspiration ◽  
Seasonal Development ◽  
Interactive Approach ◽  
Flux Tower ◽  
Event Driven ◽  
Phenology Model

Abstract. Evapotranspiration (ET) flux constitutes a major component of both the water and energy balances at the land surface. Among the many factors that control evapotranspiration, phenology poses a major source of uncertainty in attempts to predict ET. Contemporary approaches to ET modeling and monitoring frequently summarize the complexity of the seasonal development of vegetation cover into static phenological trajectories (or climatologies) that lack sensitivity to changing environmental conditions. The Event Driven Phenology Model (EDPM) offers an alternative, interactive approach to representing phenology. This study presents the results of an experiment designed to illustrate the differences in ET arising from various techniques used to mimic phenology in models of land surface processes. The experiment compares and contrasts two realizations of static phenologies derived from long-term satellite observations of the Normalized Difference Vegetation Index (NDVI) against canopy trajectories produced by the interactive EDPM trained on flux tower observations. The assessment was carried out through validation of predicted ET against records collected by flux tower instruments. The VegET model (Senay, 2008) was used as a framework to estimate daily actual evapotranspiration and supplied with seasonal canopy trajectories produced by the EDPM and traditional techniques. The interactive approach presented the following advantages over phenology modeled with static climatologies: (a) lower prediction bias in crops; (b) smaller root mean square error in daily ET – 0.5 mm per day on average; (c) stable level of errors throughout the season similar among different land cover types and locations; and (d) better estimation of season duration and total seasonal ET.

scholarly journals Evaluation of the Relationship between Air and Land Surface Temperature under Clear- and Cloudy-Sky Conditions

2011 ◽  
Vol 50 (3) ◽  
pp. 767-775 ◽  
Author(s):  
Kevin Gallo ◽  
Robert Hale ◽  
Dan Tarpley ◽  
Yunyue Yu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Characteristics ◽  
Reference Network ◽  
Clear Sky ◽  
Sky Conditions ◽  
The Relationship

Abstract Clear and cloudy daytime comparisons of land surface temperature (LST) and air temperature (Tair) were made for 14 stations included in the U.S. Climate Reference Network (USCRN) of stations from observations made from 2003 through 2008. Generally, LST was greater than Tair for both the clear and cloudy conditions; however, the differences between LST and Tair were significantly less for the cloudy-sky conditions. In addition, the relationships between LST and Tair displayed less variability under the cloudy-sky conditions than under clear-sky conditions. Wind speed, time of the observation of Tair and LST, season, the occurrence of precipitation at the time of observation, and normalized difference vegetation index values were all considered in the evaluation of the relationship between Tair and LST. Mean differences between LST and Tair of less than 2°C were observed under cloudy conditions for the stations, as compared with a minimum difference of greater than 2°C (and as great as 7+°C) for the clear-sky conditions. Under cloudy conditions, Tair alone explained over 94%—and as great as 98%—of the variance observed in LST for the stations included in this analysis, as compared with a range of 81%–93% for clear-sky conditions. Because of the relatively homogeneous land surface characteristics encouraged in the immediate vicinity of USCRN stations, and potential regional differences in surface features that might influence the observed relationships, additional analyses of the relationships between LST and Tair for additional regions and land surface conditions are recommended.

scholarly journals Alternative methods to predict actual evapotranspiration illustrate the importance of accounting for phenology – Part 2: The event driven phenology model

2012 ◽  
Vol 9 (1) ◽  
pp. 161-177 ◽  
Author(s):  
V. Kovalskyy ◽  
G. M. Henebry
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Alternative Methods ◽  
Actual Evapotranspiration ◽  
Seasonal Development ◽  
Interactive Approach ◽  
Flux Tower ◽  
Event Driven ◽  
Phenology Model

Abstract. Evapotranspiration (ET) flux constitutes a major component of both the water and energy balances at the land surface. Among the many factors that control evapotranspiration, phenology poses a major source of uncertainty in attempts to predict ET. Contemporary approaches to ET modeling and monitoring frequently summarize the complexity of the seasonal development of vegetation cover into static phenological trajectories (or climatologies) that lack sensitivity to changing environmental conditions. The Event Driven Phenology Model (EDPM) offers an alternative, interactive approach to representing phenology. This study presents the results of an experiment designed to illustrate the differences in ET arising from various techniques used to mimic phenology in models of land surface processes. The experiment compares and contrasts two realizations of static phenologies derived from long-term satellite observations of the Normalized Difference Vegetation Index (NDVI) against canopy trajectories produced by the interactive EDPM trained on flux tower observations. The assessment was carried out through validation of predicted ET against records collected by flux tower instruments. The VegET model (Senay, 2008) was used as a framework to estimate daily actual evapotranspiration and supplied with seasonal canopy trajectories produced by the EDPM and traditional techniques. The interactive approach presented the following advantages over phenology modeled with static climatologies: (a) lower prediction bias in crops; (b) smaller root mean square error in daily ET – 0.5 mm per day on average; (c) stable level of errors throughout the season similar among different land cover types and locations; and (d) better estimation of season duration and total seasonal ET.

scholarly journals A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

Technologies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 40
Author(s):  
Guang Yang ◽  
Yuntao Ma ◽  
Jiaqi Hu
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Extraction Methods ◽  
Average Error ◽  
Geographic Coordinate System ◽  
Optimal Thresholds ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Spatial Locations

The boundary of urban built-up areas is the baseline data of a city. Rapid and accurate monitoring of urban built-up areas is the prerequisite for the boundary control and the layout of urban spaces. In recent years, the night light satellite sensors have been employed in urban built-up area extraction. However, the existing extraction methods have not fully considered the properties that directly reflect the urban built-up areas, like the land surface temperature. This research first converted multi-source data into a uniform projection, geographic coordinate system and resampling size. Then, a fused variable that integrated the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night light images, the Moderate-resolution Imaging Spectroradiometer (MODIS) surface temperature product and the normalized difference vegetation index (NDVI) product was designed to extract the built-up areas. The fusion results showed that the values of the proposed index presented a sharper gradient within a smaller spatial range, compared with the only night light images. The extraction results were tested in both the area sizes and the spatial locations. The proposed index performed better in both accuracies (average error rate 1.10%) and visual perspective. We further discussed the regularity of the optimal thresholds in the final boundary determination. The optimal thresholds of the proposed index were more stable in different cases on the premise of higher accuracies.

scholarly journals Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Yulia Ivanova ◽  
Anton Kovalev ◽  
Vlad Soukhovolsky
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Principal Component ◽  
Stem Growth ◽  
Forest Growth ◽  
Parabolic Approximation ◽  
New Approach ◽  
The Relationship ◽  
Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

scholarly journals A Simple Method for Converting 1-km Resolution Daily Clear-Sky LST into Real LST

2020 ◽  
Vol 12 (10) ◽  
pp. 1641
Author(s):  
Yunfei Zhang ◽  
Yunhao Chen ◽  
Jing Li ◽  
Xi Chen
Keyword(s):  
Land Surface ◽  
Missing Values ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Shortwave Radiation ◽  
Simple Method ◽  
The Real ◽  
Independent Variables ◽  
Clear Sky ◽  
Reconstruction Methods

Land-surface temperature (LST) plays a key role in the physical processes of surface energy and water balance from local through global scales. The widely used one kilometre resolution daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product has missing values due to the influence of clouds. Therefore, a large number of clear-sky LST reconstruction methods have been developed to obtain spatially continuous LST datasets. However, the clear-sky LST is a theoretical value that is often an overestimate of the real value. In fact, the real LST (also known as cloudy-sky LST) is more necessary and more widely used. The existing cloudy-sky LST algorithms are usually somewhat complicated, and the accuracy needs to be improved. It is necessary to convert the clear-sky LST obtained by the currently better-developed methods into cloudy-sky LST. We took the clear-sky LST, cloud-cover duration, downward shortwave radiation, albedo and normalized difference vegetation index (NDVI) as five independent variables and the real LST at the ground stations as the dependent variable to perform multiple linear regression. The mean absolute error (MAE) of the cloudy-sky LST retrieved by this method ranged from 3.5–3.9 K. We further analyzed different cases of the method, and the results suggested that this method has good flexibility. When we chose fewer independent variables, different clear-sky algorithms, or different regression tools, we also achieved good results. In addition, the method calculation process was relatively simple and can be applied to other research areas. This study preliminarily explored the influencing factors of the real LST and can provide a possible option for researchers who want to obtain cloudy-sky LST through clear-sky LST, that is, a convenient conversion method. This article lays the foundation for subsequent research in various fields that require real LST.

scholarly journals An LSWI-Based Method for Mapping Irrigated Areas in China Using Moderate-Resolution Satellite Data

2020 ◽  
Vol 12 (24) ◽  
pp. 4181
Author(s):  
Kunlun Xiang ◽  
Wenping Yuan ◽  
Liwen Wang ◽  
Yujiao Deng
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Statistical Data ◽  
Spatial Information ◽  
Hydrological Cycle ◽  
Mainland China ◽  
Mean Annual Precipitation ◽  
Irrigated Area ◽  
Moderate Resolution

Accurate spatial information about irrigation is crucial to a variety of applications, such as water resources management, water exchange between the land surface and atmosphere, climate change, hydrological cycle, food security, and agricultural planning. Our study proposes a new method for extracting cropland irrigation information using statistical data, mean annual precipitation and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type data and surface reflectance data. The approach is based on comparing the land surface water index (LSWI) of cropland pixels to that of adjacent forest pixels with similar normalized difference vegetation index (NDVI). In our study, we validated the approach over mainland China with 612 reference samples (231 irrigated and 381 non-irrigated) and found the accuracy of 62.09%. Validation with statistical data also showed that our method explained 86.67 and 58.87% of the spatial variation in irrigated area at the provincial and prefecture levels, respectively. We further compared our new map to existing datasets of FAO/UF, IWMI, Zhu and statistical data, and found a good agreement with the irrigated area distribution from Zhu’s dataset. Results show that our method is an effective method apply to mapping irrigated regions and monitoring their yearly changes. Because the method does not depend on training samples, it can be easily repeated to other regions.

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