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All Journal International Journal of Remote Sensing and Earth Sciences (IJReSES)
M.Rokhis Khomarudin
LAPAN
Earth & Planetary Sciences

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ESTIMATION OF AIR TEMPERATURE USING REMOTE SENSING BASED ON THERMAL DIFFUSIVITY APPROACH M.Rokhis Khomarudin; Ahmad Bey; Idung Risdiyanto
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 3,(2006)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (266.877 KB) | DOI: 10.30536/j.ijreses.2006.v3.a1203

Abstract

The measurement of air temperature usually used thermometer in the meteorology or climate station under Bureau of Meteorology and Geophysics. In Indonesia, there are some limitations in air temperature measurement and then they could not provide the spatial high resolution information. The measurement of air temperature is very important for analyzing the human comfort, photosynthesis, and vegetation growth which we need saome details spatial information. However, when data were sparse, the underlying assumptions about the variation among sampled points often differed and the choice of interpolation method and parameters then became critical. Often though data may be too sparse to use any of the interpolation methods, alternate ways to derive spatially representative values of air temperature need to researched. The data that could provide spatial information are remote sensing. The objective of this research is to estimate air temperature using remote sensing data (NOAA/AVHRR and LANDSAT/TM), based on thermal diffusivity approach. The steps of this research include the calibration of surface temperature, the determination of amplitude, and the estimation of air temperature. Based on this research, the best equation to calculate surface temperature from NOAA AVHRR is Ulivieri et al equation. This equation shows the higher correlation between surface temperatures from NOAA/AVHRR and the observation in the field than the other equation. Physically, this research could estimate air temperature from satellites data, but statistically, this research has not enough significancy to describe the field observation. Keywords: physical model, temperature, remote sensing.
CROP WATER STRESS INDEX (CWSI) ESTIMATION USING MODIS DATA M.Rokhis Khomarudin; Parwati Sofan
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 3,(2006)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (190.18 KB) | DOI: 10.30536/j.ijreses.2006.v3.a1208

Abstract

Crop Water Stress Index (CWSI) is an index which is used to explain the amount of crop water defisiency based on canopy surface temperature. Many researches of CWSI have been done for arranging irigation water system in several crops at different areas. Beside its application in irigation system, CWSI is also known as one of parameters that can influence crop productivity. Regarding the above explanation, it is implied that CWSI is important for monitoring crop drought, arranging irigation water, and estimating crop productivity. This research is proposed to estimate CWSI using MODIS (Moderate Resolution Imaging Spectroradiometer) data which is related to Normalized Difference Vegetation Index (NDVI) and Soil Moisture Storage (ST) in paddy field. The interest area is in East Java wich is the driest area in Java Island. MODIS land surface temperature is used to estimate CWSI, while MODIS reflectance 500 m is used to estimate NDVI. They were downloaded from NASA website. Data period was from June 15th to June 30 th, 2004. Based on the correlation between NDVI and CWSI, we can estimate NDVI value when paddy water stress occured. The result showed that the largest paddy area in East Java which has high water stress is located in Bojonegoro District. The water stress areain Bojonegoro Distric increase from June 15th to June 30th, 2004. The high to medium water stress level in East Java were predicted as bare land. The CWSI has negative correlation with NDVI and ST. The CWSI 0.6 are obtained in NDVI 0.5 with ST less than 50 percent. This showed that the paddy water stress began at NDVI 0.5 and ST 50 percent. Coefficient of correlation between CWSI and NDVI is 0.58, while CWSI and ST is 0.71. The correlation model between CWSI, NDVI and ST is statistically significant. Keywords: CWSI,NDVI, ST, MODIS Land Surface Temperature, Water Stress.
Co-Authors Ahmad Bey Idung Risdiyanto Parwati Sofan