Estimation of spatial evapotranspiration using Terra MODIS satellite image and SEBAL model in mixed forest and rice paddy area

CO2&#160;fluxes and concentrations are not well understood in Northeast China, where dominant land surface types are mixed forest and cropland. &#160;Here, we analyzed the CO2&#160;fluxes and concentrations using Eddy Covariance&#160;(EC)&#160;measurements, satellite observations, and the Weather Research and Forecasting model coupled with the Vegetation Photosynthesis and Respiration Model (WRF-VPRM). &#160;We also used WRF-VPRM outputs to examine CO2&#160;transport/dispersion, and to quantify the biogenic&#160;and anthropogenic contributions to atmospheric&#160;CO2&#160;concentrations. &#160;Finally, we investigated the uncertainties&#160;of simulating&#160;CO2&#160;fluxes&#160;related to&#160;four VPRM parameters (including&#160;maximum light use efficiency,&#160;photosynthetically active radiation half-saturation value, and two&#160;respiration parameters) using offline ensemble simulations with&#160;randomly selected&#160;parameter values.&#160;&#160;The results indicated that&#160;mixed forests&#160;acted as a larger CO2&#160;source&#160;and sink than rice paddies&#160;on average in 2016 due to a longer growth period and stronger ecosystem respiration, although the minimum EC-measured daily mean net ecosystem exchange (NEE) was smaller at rice paddy&#160;(-10 &#956;mol m-2&#160;s-1) than at mixed forest&#160;(-6.5 &#956;mol m-2&#160;s-1) during the growing season (May through September). &#160;The monthly&#160;fluctuation of column-averaged CO2&#160;concentrations (XCO2) exceeded 10 ppm&#160;in Northeast China during 2016. &#160;Biogenic contribution&#160;(large&#160;negative&#160;in summer&#160;and insignificant in other months)&#160;offset about 70% of anthropogenic contribution of XCO2&#160;in this region. &#160;WRF-VPRM modeling successfully captured seasonal&#160;and episodic variations of NEE and CO2&#160;concentrations, however, the NEE in mixed forest was overestimated during daytime, mainly due to the uncertainties of VPRM parameters,&#160;especially maximum light use efficiency.

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

Jurnal Inotera ◽

10.31572/inotera.vol2.iss1.2017.id17 ◽

2017 ◽

Vol 2 (1) ◽

pp. 29

Author(s):

Oktalia Triananda Lovita ◽

Mokhamad Nur Cahyadi ◽

Muhammad Taufik

Keyword(s):

Water Vapor ◽

Forest Fires ◽

Satellite Image ◽

Weather Conditions ◽

Equation Model ◽

Average Value ◽

Before And After ◽

Value Of Water ◽

Moderate Resolution Imaging Spectroradiometer ◽

Terra Modis

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; 0,865�m, 1.24 �m, 0.905 �m, 0.936 �m and 0,940 �m. 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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Geo-spatial Analysis of Farmland Affected by 2020 Flooding of River Rima, Northwestern Nigeria

Journal of Engineering Research and Reports ◽

10.9734/jerr/2021/v20i717338 ◽

2021 ◽

pp. 20-27

Author(s):

Abdullahi Muktar ◽

Sadiq A. Yelwa ◽

Muhammad Tayyib Bello ◽

Wali Elekwachi

Keyword(s):

Satellite Image ◽

Shuttle Radar Topography Mission ◽

Policy Makers ◽

Professional Advice ◽

Maximum Water Level ◽

Digital Elevation ◽

Farm Produce ◽

Terra Modis ◽

Elevation Model ◽

The Impact

The flooding of River Rima is an annual issue affecting farmland located within the floodplains. This phenomena causes loss of farm produce and mass destruction of buildings, including roads and bridges in the area. Estimating the farmland affected by the flood will help the policy makers in decision making on how to mitigate the impact of flooding in the affected areas. The Terra/MODIS satellite image with 7-2-1 bands combination was used to classify the image into four landcover types. The area covered by flood was selected to calculate the flood area using Image Calculator module on QGIS software. The class of water was imposed on Digital Elevation Model that was obtained from Environmental Monitoring Satellite called The Shuttle Radar Topography Mission (SRTM). The result shows that River Rima flood occupies about 17,517 km2, equivalent to 1.7 million hectares of farmland that is below 230 meters (ASL). It was recommended that the local authorities and decision makers may use the flood map to showing flood risk zones so as to deter construction beyond the buffer. Farmers should adhere strictly to NiMet’s advice based on flood predictions. The civil engineers should also take note of the maximum water level during flooding so as to apply professional advice when constructing roads and bridges in the area.

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