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 Multispectral Image-Based Estimation of Drought Patterns and Intensity around Lake Chad, Africa

2019 ◽  
Vol 11 (21) ◽  
pp. 2534 ◽  
Author(s):  
Willibroad Gabila Buma ◽  
Sang-Il Lee
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Condition Index ◽  
Drought Monitoring ◽  
World Population ◽  
Lake Chad ◽  
In Situ Data

As the world population keeps increasing and cultivating more land, the extraction of vegetation conditions using remote sensing is important for monitoring land changes in areas with limited ground observations. Water supply in wetlands directly affects plant growth and biodiversity, which makes monitoring drought an important aspect in such areas. Vegetation Temperature Condition Index (VTCI) which depends on thermal stress and vegetation state, is widely used as an indicator for drought monitoring using satellite data. In this study, using clear-sky Landsat multispectral images, VTCI was derived from Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). Derived VTCI was used to observe the drought patterns of the wetlands in Lake Chad between 1999 and 2018. The proportion of vegetation from WorldView-3 images was later introduced to evaluate the methods used. With an overall accuracy exceeding 90% and a kappa coefficient greater than 0.8, these methods accurately acquired vegetation training samples and adaptive thresholds, allowing for accurate estimations of the spatially distributed VTCI. The results obtained present a coherent spatial distribution of VTCI values estimated using LST and NDVI. Most areas during the study period experienced mild drought conditions, though severe cases were often seen around the northern part of the lake. With limited in-situ data in this area, this study presents how VTCI estimations can be developed for drought monitoring using satellite observations. This further shows the usefulness of remote sensing to improve the information about areas that are difficult to access or with poor availability of conventional meteorological data.

scholarly journals Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China

2020 ◽  
Vol 12 (15) ◽  
pp. 2414
Author(s):  
Xiao Bai ◽  
Lanhui Zhang ◽  
Chansheng He ◽  
Yi Zhu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Regional Scale ◽  
Northwest China ◽  
Heihe River ◽  
Regression Equations ◽  
Mountainous Areas ◽  
Soil Layers ◽  
Temporal And Spatial

Temporal and spatial variability of soil moisture has an important impact on hydrological processes in mountainous areas. Understanding such variability requires soil moisture datasets at multiple temporal and spatial scales. Remote sensing is a very effective method to obtain surface (~5 cm depth) soil moisture at the regional scale but cannot directly measure soil moisture at deep soil layers (>5 cm depth) currently. This study chose the upstream of the Heihe River Watershed in the Qilian Mountain Ranges in Northwest China as the study area to estimate the profile soil moisture (0–70 cm depth) at the regional scale using satellite Vegetation Index (NDVI) and Land Surface Temperature (LST) products. The study area was divided into 31 zones according to the combination of altitude, vegetation and soil type. Long-term in situ soil moisture observation stations were set up at each of the zones. Soil moisture probe, ECH2O, was used to collect soil moisture at five layers (0–10, 10–20, 20–30, 30–50 and 50–70 cm) continuously. Multiple linear regression equations of time series MODIS (Moderate-resolution Imaging Spectroradiometer) NDVI, LST and soil moisture were developed for each of the five soil layers at the 31 zones to estimate the soil moisture (0–70 cm) on a regional scale with a spatial resolution of 1 km2 and a temporal resolution of 16-d from October, 2013 to September, 2016. The correlation coefficient R of the regression equations was between 0.47 and 0.94, the RMSE was 0.03, indicating that the estimation method based on the MODIS NDVI and LST data was suitable and could be applied to alpine mountainous areas with complex topography, soil and vegetation types. The overall pattern of soil moisture spatial distribution indicated that soil moisture was higher in the eastern region than in the western region, and the soil moisture content in the whole study area was 14.5%. The algorithm and results provide novel applications of remote sensing to support soil moisture data acquisition and hydrological research in mountainous areas.

Long-term trends of surface reflectance derived from models, satellite and in-situ observations over polar areas

2020 ◽  
Author(s):  
Christian Lanconelli ◽  
Fabrizio Cappucci ◽  
Bernardo Mota ◽  
Nadine Gobron ◽  
Amelie Driemel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Kendall Test ◽  
Surface Radiation ◽  
In Situ Data ◽  
In Situ Observations

<div> <p>Nowadays, an increasingly amount of remote sensing and in-situ data are extending over decades. They contribute to increase the relevance of long-term studies aimed to deduce the mechanisms underlying the climate change dynamics. The aim of this study is to investigate the coherence between trends of different long-term climate related variables including the surface albedo (A) and land surface temperature (LST) as obtained by remote sensing platforms, models and in-situ observations. </p> </div><div> <p>Directional-hemispherical and bi-hemispherical broadband surface reflectances as derived from MODIS-MCD43 (v006) and MISR, and the analogous products of the Copernicus Global Land (CGLS) and C3S services derived from SPOT-VEGETATION, PROBA-V and AVHRR (v0 and v1), have been harmonized and, together with the ECMWF ERA-5 model, assessed with respect ground data taken over polar areas, over a temporal window spanning the last 20 years.  </p> </div><div> <p>The benchmark was established using in-situ measurements provided from the Baseline Surface Radiation Network (BSRN) over four Arctic and four Antarctic sites. The 1-minute resolution datasets of broadband upwelling and down-welling radiation, have been reduced to directional- and bi-hemispherical reflectances, with the same time scale of satellite products (1-day, 10-days, monthly).  </p> </div><div> <p>A similar approach was used to investigate the fitness for purpose of Land Surface Temperature as derived by MODIS (MOD11), ECMWF ERA-5, with respect to the brightness temperature derived using BSRN measurements over the longwave band.  </p> </div><div> <p>The entire temporal series are decomposed into seasonal and residual components, and then the presence of monotonic significant trends are assessed using the non-parametric Kendall test. Preliminary results shown a strong correlation between negative albedo trends and positive LST trends, especially in arctic regions. </p> </div>

scholarly journals Exploring the Relationship between LST, LULC and NDVI in Saïss Plain using Geospatial Techniques

2021 ◽  
Vol 314 ◽  
pp. 04001
Author(s):  
Manal El Garouani ◽  
Mhamed Amyay ◽  
Abderrahim Lahrach ◽  
Hassane Jarar Oulidi
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Arable Land ◽  
Remote Sensing Data ◽  
Landsat Images ◽  
Gis And Remote Sensing ◽  
Sensing Data

Land use/land cover (LULC) change has been confirmed that have a significant impact on climate through various pathways that modulate land surface temperature (LST) and precipitation. However, there are no studies illustrated this link in the Saïss plain using remote sensing data. Thus, the aim of this study is to monitor the LST relationship between LULC and vegetation index change in the Saïss plain using GIS and Remote Sensing Data. We used 18 Landsat images to study the annual and interannual variation of LST with LULC (1988, 1999, 2009 and 2019). To highlight the effect of biomass on LST distribution, the Normalized Difference Vegetation Index (NDVI) was calculated, which is a very good indicator of biomass. The mapping results showed an increase in the arboriculture and urbanized areas to detriment of arable lands and rangelands. Based on statistical analyzes, the LST varies during the phases of plant growth in all seasons and that it is diversified due to the positional influence of LULC type. The variation of land surface temperature with NDVI shows a negative correlation. This explains the increase in the surface temperature in rangelands and arable land while it decreases in irrigated crops and arboriculture.

scholarly journals Characterising Rice (Oryza indica) Ecology in East and Southern Eastern Plateau of Odisha using Multi Source Remote Sensing

2021 ◽  
pp. 37-47
Author(s):  
M. Satya Swarupa Rani ◽  
Anima Biswal ◽  
B. S. Rath
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Ecological Condition ◽  
Water Index ◽  
Normalised Difference Vegetation Index ◽  
Spectral Vegetation Indices ◽  
Block Level

Rice is the most important crop of Odisha occupying 41.24% of net sown area in Kharif season and contributing 65.85 % of total food grain production of Odisha state and this is being cultivated in various types environmental and ecological condition. Assessment of rice phenology is prime for management and yield prediction. In view of characterizing rice ecology in East and South Eastern Plateau from 2008 – 2018 to know the time series analysis , remote sensing tools were used . MODIS can0 acquire data over a wide area with high spatial and temporal resolutions easily providing regional scale information .In order to study the seasonal /annual as well as spatial variability of kharif rice vigour and wetness spectral vegetation indices like NDVI(Normalised Difference Vegetation Index),LSWI(Land surface water index) derived from 15 day composite 250 m data were analysed at block level for Odisha state. For studying the start of season variability, SASI index was used. The season maximum NDVI, LSWI were computed for the year 2008-2018 for kharif rice in East and Southern eastern coastal plain zone of Odisha and graphs were generated which shows the variability of the kharif rice vigour and wetness.

scholarly journals Use of Landsat Land Surface Temperature and Vegetation Indices for Monitoring Drought in the Salt Lake Basin Area, Turkey

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Osman Orhan ◽  
Semih Ekercin ◽  
Filiz Dadaser-Celik
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Satellite Remote Sensing ◽  
Vegetation Index ◽  
Salt Lake ◽  
Remote Sensing Data ◽  
Lake Basin ◽  
Sensing Data

The main purpose of this paper is to investigate multitemporal land surface temperature (LST) changes by using satellite remote sensing data. The study included a real-time field work performed during the overpass of Landsat-5 satellite on 21/08/2011 over Salt Lake, Turkey. Normalized vegetation index (NDVI), vegetation condition index (VCI), and temperature vegetation index (TVX) were used for evaluating drought impact over the region between 1984 and 2011. In the image processing step, geometric and radiometric correction procedures were conducted to make satellite remote sensing data comparable within situmeasurements carried out using thermal infrared thermometer supported by hand-held GPS. The results showed that real-time ground and satellite remote sensing data were in good agreement with correlation coefficient (R2) values of 0.90. The remotely sensed and treated satellite images and resulting thematic indices maps showed that dramatic land surface temperature changes occurred (about2∘C) in the Salt Lake Basin area during the 28-year period (1984–2011). Analysis of air temperature data also showed increases at a rate of 1.5–2∘Cduring the same period. Intensification of irrigated agriculture particularly in the southern basin was also detected. The use of water supplies, especially groundwater, should be controlled considering particularly summer drought impacts on the basin.

scholarly journals Remote Sensing Based Analysis of the Role of Land Use/Land Cover on Surface Temperature and Temporal Changes in Temperature; a Case Study of Ajmer District, Rajasthan

2014 ◽  
Vol XL-8 ◽  
pp. 1447-1454 ◽  
Author(s):  
A. Hussain ◽  
P. Bhalla ◽  
S. Palria
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Temporal Variations ◽  
Landsat Tm ◽  
Spectral Radiance ◽  
Ajmer District

An attempt has been made in this research to analyze temporal variations in surface temperature in Ajmer District Rajasthan. The research is carried out to assess the relationship between the land surface temperatures (LST) and land cover (LC) changes both in quantitative and qualitative ways in Ajmer District area using Landsat TM/ETM+ data over the period 1989 to 2013.in this period we used three temporal TM/ETM data 1989, 2001 and 2013. Remote sensing of Land surface temperature (LST) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST)–vegetation relationship. Unsupervised classification methods have been taken to prepare the LC map. LST is derived from the thermal band of Landsat TM/ETM+ using the calibration of spectral radiance and emissivity correction of remote sensing. NDVI is derived from the NIR & RED Band using image enhancement technique (Indices). Arc-GIS have been utilized for data visualization. This procedure allowed analyzing whether LULC classes match LST classes. However, the results of such overlaying are hard to interpret. LST and LULC maps of these areas give the understanding on how the classes and corresponding LST have changed from one date to the other. Another option is to collect statistical data. it was impossible to calculate linear regression between LULC map and LST map. A solution to that matter is to use Normalized Vegetation Index (NDVI) instead of LULC classification result.

scholarly journals Method of drought evaluation for a territory by the land surface temperature and vegetation relationship from remote sensing data

2020 ◽  
Vol 2 (4) ◽  
pp. First
Author(s):  
Van Tran-Thi ◽  
Ha Nguyen Ngan ◽  
Viet Ha Quoc ◽  
Long Nguyen Hoang ◽  
Bao Ha Duong Xuan
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Current Trend ◽  
Remote Sensing Data ◽  
Food Sources ◽  
Sensing Data ◽  
Vegetation Characteristics

Facing the current trend of climate change, which is difficult to control, human life, as well as food sources, are increasingly seriously threatened by droughts that occur more frequently. Understanding the region's drought will help people avoid risks. The paper presents research on the method of assessing drought situations based on the integration of land surface temperature and vegetation characteristics in the drought index according to the Temperature Vegetation Dryness Index TVDI from remote sensing data. Landsat satellite images were used with image processing methods to test the drought assessment method for the test area of Di Linh district, Lam Dong province. The study period was the dry season in 2018. Reflective bands were used to determine vegetation cover as representative of soil moisture supplying water to crops. Vegetation characteristics are represented by Normalized Differential Vegetation Index NDVI. In contrast, the thermal infrared band is used to calculate the surface temperature. The results showed that the bare land and sparsely populated areas exhibited a higher level of drought than the vegetated areas. The research results demonstrate the ability of remote sensing technology to support the monitoring of drought in a space for a region, in order to help people make the right management decisions in planning.  

scholarly journals Spatio-Temporal Change of Land Surface Temperature at Tiris Geothermal Potential Area, East Java, Indonesia.

2019 ◽  
Vol 3 (1) ◽  
pp. 13-21
Author(s):  
Andre Prayogo ◽  
Sukir Maryanto ◽  
Ahmad Nadhir
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Hot Spots ◽  
Land Surface ◽  
Vegetation Index ◽  
Hot Springs ◽  
Landsat 8 ◽  
Geothermal Potential ◽  
Geological Information

AbstractOne of the areas that have geothermal potential in Indonesia is Tiris because there are found some manifestation in the form of hot springs. Several studies are needed to determine its geothermal potential before exploitation is carried out. Some previous studies have been carried out in the area, one of which uses Landsat 7 remote sensing data. There are other studies that state that knowledge of geology is needed to implement remote sensing in determining geothermal areas. This study uses 3-years data from Landsat 8 and geological information from the regional geological map of the study area. The result show changes in the value of Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) from year to year, where each year the NDVI value decrease which is interpreted as reduced vegetation in the study area. From the distribution of LST values in the study area, it was found that there were hot spots that had higher temperatures than the surrounding area. When geological information and LST distribution map overlaid with regional geological maps, it is known that the hot spots inside the research area are possible to be a geothermal reservoir.

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