Surface temperature and water vapour retrieval from MODIS data

Sobrino, José A.; Kharraz, Jauad El; Li, Z.-L.

doi:10.1080/0143116031000102502

Cited by 190 publications

(199 citation statements)

References 27 publications

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“…In this study, the NDVI based approach of LSE estimation has been used [9,35]. This algorithm has been applied in the estimation of LSE for various sensors [32,[36][37][38][39] with the use of Visible and Near Infrared (VNIR) data. To calculate the NDVI, Equation (3) has been used in the study [40].…”

Section: Estimation Of Land Surface Emissivity (Lse)mentioning

confidence: 99%

Inversion of Land Surface Temperature (LST) Using Terra ASTER Data: A Comparison of Three Algorithms

Ndossi

Avdan

2016

Remote Sensing

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Land Surface Temperature (LST) is an important measurement in studies related to the Earth surface's processes. The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) instrument onboard the Terra spacecraft is the currently available Thermal Infrared (TIR) imaging sensor with the highest spatial resolution. This study involves the comparison of LSTs inverted from the sensor using the Split Window Algorithm (SWA), the Single Channel Algorithm (SCA) and the Planck function. This study has used the National Oceanic and Atmospheric Administration's (NOAA) data to model and compare the results from the three algorithms. The data from the sensor have been processed by the Python programming language in a free and open source software package (QGIS) to enable users to make use of the algorithms. The study revealed that the three algorithms are suitable for LST inversion, whereby the Planck function showed the highest level of accuracy, the SWA had moderate level of accuracy and the SCA had the least accuracy. The algorithms produced results with Root Mean Square Errors (RMSE) of 2.29 K, 3.77 K and 2.88 K for the Planck function, the SCA and SWA respectively.

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Section: Estimation Of Land Surface Emissivity (Lse)mentioning

confidence: 99%

Inversion of Land Surface Temperature (LST) Using Terra ASTER Data: A Comparison of Three Algorithms

Ndossi

Avdan

2016

Remote Sensing

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“…For MODIS data, the SMAC (Simplified Method for Atmospheric Correction) program was used for atmospheric correction of the first 7 bands, whilst the thermal bands of MODIS data (Bands 31 and 32) were corrected using a split-window algorithm [50] which was used to retrieve land surface temperature. This method can correct the thermal bands using water vapor content and surface emissivity data [51].…”

Section: Pre-processing Of Remote Sensing Datamentioning

confidence: 99%

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

2016

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Vegetation moisture and dry matter content are important indicators in predicting the behavior of fire and it is widely used in fire spread models. In this study, leaf fuel moisture content such as Live Fuel Moisture Content (LFMC), Leaf Relative Water Content (RWC), Dead Fuel Moisture Content (DFMC), and Leaf Dry Matter Content (LDMC) (hereinafter known as moisture content indices (MCI)) were calculated in the field for different forest species at 32 sites in a temperate humid forest (Zaringol forest) located in northeastern Iran. These data and several relevant vegetation-biophysical indices and atmospheric variables calculated using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data with moderate spatial resolution (30 m) were used to estimate MCI of the Zaringol forest using Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) methods. The prediction of MCI using ANN showed that ETM+ predicted MCI slightly better (Mean Absolute Percentage Error (MAPE) of 6%-12%)) than MLR (MAPE between 8% and 17%). Once satisfactory results in estimating MCI were obtained by using ANN from ETM+ data, these data were then upscaled to estimate MCI using MODIS data for daily monitoring of leaf water and leaf dry matter content at 500 m spatial resolution. For MODIS derived LFMC, LDMC, RWC, and DLMC, the ANN produced a MAPE between 11% and 29% for the indices compared to MLR which produced an MAPE of 14%-33%. In conclusion, we suggest that upscaling is necessary for solving the scale discrepancy problems between the indicators and low spatial resolution MODIS data. The scaling up of MCI could be used for pre-fire alert system and thereby can detect fire prone areas in near real time for fire-fighting operations.

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“…Rata-rata jumlah Water Vapor pada suatu kondisi atmosfer dapat diturunkan berdasarkan Pers (3) berikut [10] . …”

Section: Penurunan Nilai Water Vapor Dari Data Modisunclassified

Analisa Perubahan Cuaca Di Pulau Sumatera Akibat Kebakaran Hutan Dengan Data Water Vapor Dari Citra Satelit Terra Modis

Lovita¹,

Cahyadi

Taufik³

2017

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Forest fires in Sumatra lead to a very extreme climate changes around the earth, so there would still be a difficult job for atmosphere researchers. This research was conducted to know the weather conditions by determining the condition of Water Vapor (WV) on the island of Sumatra. Monitoring the condition of WV can be done by using remote sensing techniques, by processing the image satellite data namely Terra Modis (Moderate Resolution Imaging Spectroradiometer). Data calculation condition WV, as one of the parameters of dynamic atmosphere. The data comes from Terra Modis satellite image, the data on Canal 2, 5, 17, 18 and 19 with a wavelength range; 0,865μm, 1.24 μm, 0.905 μm, 0.936 μm and 0,940 μm. From these results obtained from the average value of Water Vapor before and after fires in 2012. Water Vapor taken from TERRA MODIS satellite imagery (y) with a correction factor of 0.9865. Although the correlation (r) between Water Vapor from MODIS data is high, it can be seen that between Water Vapor in 2012 ranged between 3-8 cm. 82%, however only about 68% of Water Vapor MODIS diversity that can be presented by the equation model to approach the actual value of Water Vapor. With these data will greatly affect the weather cycle in Indonesia..

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Surface temperature and water vapour retrieval from MODIS data

Cited by 190 publications

References 27 publications

Inversion of Land Surface Temperature (LST) Using Terra ASTER Data: A Comparison of Three Algorithms

Inversion of Land Surface Temperature (LST) Using Terra ASTER Data: A Comparison of Three Algorithms

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

Analisa Perubahan Cuaca Di Pulau Sumatera Akibat Kebakaran Hutan Dengan Data Water Vapor Dari Citra Satelit Terra Modis

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