scholarly journals Remote Sensing of Global Daily Evapotranspiration based on a Surface Energy Balance Method and Reanalysis data

2021 ◽  
Author(s):  
Xuelong Chen ◽  
Zhongbo Su ◽  
Yaoming Ma ◽  
Isabel Trigo ◽  
Pierre Gentine
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Reanalysis Data ◽  
Balance Method ◽  
Energy Balance Method ◽  
Daily Evapotranspiration

scholarly journals METODE NERACA ENERGI UNTUK PERHITUNGAN INDEKS LUAS DAUN MENGGUNAKAN DATA CITRA SATELIT MULTI SPEKTRAL(ENERGY BALANCE METHOD FOR DETERMINING LEAF AREA INDEX LAND USING MULTI SPECTRAL SATELLITE IMAGINARY)

Agromet ◽  
2007 ◽  
Vol 21 (2) ◽  
pp. 27 ◽  
Author(s):  
I. Risdiyanto ◽  
R. Setiawan
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Surface Energy ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Surface Energy Balance ◽  
Natural Forest ◽  
Area Index ◽  
Energy Balance Method ◽  
Remote Sensing Technique

<p>Leaf area index (LAI) is a variable showing relation between leaf area leaf and area closed over it. The conventionally technique to determine LAI value conducted by measure and accumulate wide of amount of leaf in one selected area and divided broadly area. The other technique, LAI also can be measured by using measuring instrument of solar radiation like attached tube solarimeter parallelly above and below/under plant canopy. Both of the approaches have limitation of spatial which developed new method with remote sensing technique. Determination of LAI with remote sensing technique exploits the nature of spectral of surface both from short wave (sun radiation) and long wave (surface radiation). One of the method able to be developed is surface energy balance approach with Beer-Lambert law. Result of this research indicate that value of LAI for the vegetation area by surface energy balance method and equation of Beer-Lambert law got value of mean LAI for natural forest equal to 3.05 with the range value 2.85 - 3.50 and R2 is 0.91, for the rubber agroforest equal to 3.01 with range value 2.79 - 3.40 and R2 is 0.69, while value of mean LAI for the plantation of monoculture of rubber equal to 2.96 with range value 2.74 - 3.28 and and R2 is 0.82. This method can be used for vegetation area especially for homogeneously like natural forest and monoculture.</p><p>---------------------------------------------------------------------</p><p>Indeks luas daun (ILD) merupakan suatu peubah yang menunjukkan hubungan antara luas daun dan luas bidang yang tertutupi. Secara konvensional penentuan nilai LAI dilakukan dengan mengukur dan mengakumulasikan jumlah luas daun dalam satu bidang tertentu dan dibagi dengan luas bidang tersebut. ILD juga dapat diukur menggunakan alat ukur radiasi surya seperti tube solari meter yang dipasang paralel di atas dan di bawah tajuk tumbuhan. Kedua pendekatan tersebut mempunyai keterbatasan spasial, sehingga dicoba mengembangkan metode baru dengan teknik penginderaan jauh. Pendugaan ILD dengan teknik ini memanfaatkan sifat spektral dari permukaan baik yang bersumber dari radiasi gelombang pendek dari matahari maupun radiasi gelombang panjang dari permukaan. Salah satu metode yang dapat dikembangkan adalah pendekatan neraca energi untuk menghasilkan peubah-peubah penduga ILD menggunakan hukum Beer-Lambert. Hasil penelitian ini menunjukkan bahwa nilai rata-rata ILD untuk lahan bervegetasi menggunakan metode neraca energi dan persamaan hukum Beer-Lambert untuk hutan alam sebesar 3.05 dengan nilai kisaran selang 2.85- 3.50 dan R2 validasi dengan ILD lapangan sebesar 0.91. Nilai rata-rata LAI pendugaan untuk agroforest karet sebesar 3.01 dengan selang 2.79–3.40 dan nilai R2 validasi sebesar 0.69, sedangkan nilai rata-rata ILD untuk perkebunan karet monokultur sebesar 2.96 dengan selang 2.74–3.28 dan nilai R2 validasi sebesar 0.82. Metode pendugaan ILD ini dapat digunakan untuk lahan bervegetasi terutama untuk pertanaman homogen seperti hutan alam dan monokultur.</p>

scholarly journals Using climate reanalysis data in conjunction with multi-temporal satellite thermal imagery to derive supraglacial debris thickness changes from energy-balance modelling

2021 ◽  
pp. 1-19
Author(s):  
Rebecca L. Stewart ◽  
Matthew Westoby ◽  
Francesca Pellicciotti ◽  
Ann Rowan ◽  
Darrel Swift ◽  
...  
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Current Knowledge ◽  
Meteorological Data ◽  
Reanalysis Data ◽  
Balance Model ◽  
Thermal Imagery ◽  
Thickness Estimation ◽  
Miage Glacier

Abstract Surface energy-balance models are commonly used in conjunction with satellite thermal imagery to estimate supraglacial debris thickness. Removing the need for local meteorological data in the debris thickness estimation workflow could improve the versatility and spatiotemporal application of debris thickness estimation. We evaluate the use of regional reanalysis data to derive debris thickness for two mountain glaciers using a surface energy-balance model. Results forced using ERA-5 agree with AWS-derived estimates to within 0.01 ± 0.05 m for Miage Glacier, Italy, and 0.01 ± 0.02 m for Khumbu Glacier, Nepal. ERA-5 data were then used to estimate spatiotemporal changes in debris thickness over a ~20-year period for Miage Glacier, Khumbu Glacier and Haut Glacier d'Arolla, Switzerland. We observe significant increases in debris thickness at the terminus for Haut Glacier d'Arolla and at the margins of the expanding debris cover at all glaciers. While simulated debris thickness was underestimated compared to point measurements in areas of thick debris, our approach can reconstruct glacier-scale debris thickness distribution and its temporal evolution over multiple decades. We find significant changes in debris thickness over areas of thin debris, areas susceptible to high ablation rates, where current knowledge of debris evolution is limited.

Estimation of Evapotranspiration using Remote Sensing and Surface Energy Balance Algorithm for Land (SEBAL) in Canal Command

2021 ◽  
Vol 58 (03) ◽  
pp. 274-285
Author(s):  
H. V. Parmar ◽  
N. K. Gontia
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Surface Energy ◽  
Land Surface ◽  
Surface Energy Balance ◽  
Crop Growth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Surface Temperatures ◽  
Canal Command

Remote sensing based various land surface and bio-physical variables like Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), surface albedo, transmittance and surface emissivity are useful for the estimation of spatio-temporal variations in evapotranspiration (ET) using Surface Energy Balance Algorithm for Land (SEBAL) method. These variables were estimated under the present study for Ozat-II canal command in Junagadh district, Gujarat, India, using Landsat-7 and Landsat-8 images of summer season of years 2014 and 2015. The derived parameters were used in SEBAL to estimate the Actual Evapotranspiration (AET) of groundnut and sesame crops. The lower values NDVI observed during initial (March) and end (May) stages of crop growth indicated low vegetation cover during these periods. With full canopy coverage of the crops, higher value of NDVI (0.90) was observed during the mid-crop growth stage. The remote sensing-based LST was lower for agricultural areas and the area near banks of the canal and Ozat River, while higher surface temperatures were observed for rural settlements, road and areas with exposed dry soil. The maximum surface temperatures in the cropland were observed as 311.0 K during March 25, 2014 and 315.8 K during May 31, 2015. The AET of summer groundnut increased from 3.75 to 7.38 mm.day-1, and then decreased to 3.99 mm.day-1 towards the end stage of crop growth. The daily AET of summer sesame ranged from 1.06 to 7.72 mm.day-1 over different crop growth stages. The seasonal AET of groundnut and sesame worked out to 358.19 mm and 346.31 mm, respectively. The estimated AET would be helpful to schedule irrigation in the large canal command.

Estimation of evapotranspiration in lesser Himalayas using remote sensing based surface energy balance algorithm

2020 ◽  
pp. 1-19
Author(s):  
Manish K. Nema ◽  
Hitesh P. Thakur ◽  
Hitesh Upreti ◽  
Sanjay K. Jain ◽  
P. K. Mishra ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance ◽  
Lesser Himalayas

A remote sensing surface energy balance algorithm for land (SEBAL).

1998 ◽  
Vol 212-213 ◽  
pp. 213-229 ◽  
Author(s):  
W.G.M. Bastiaanssen ◽  
H. Pelgrum ◽  
J. Wang ◽  
Y. Ma ◽  
J.F. Moreno ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Surface Energy ◽  
Surface Energy Balance
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