Latent heat flux estimation in clear sky days over Indian agroecosystems using noontime satellite remote sensing data

Mallick, Kaniska; Bhattacharya, Bimal K.; RAO, V.U.M.; Reddy, D. Sreekantha; Banerjee, Saon; Venkatesh, H.; Pandey, Vivek; Kar, Gouranga; Mukherjee, Joydeep; Vyas, S. P.; Gadgil, A. S.; Patel, N. K.

doi:10.1016/j.agrformet.2009.05.006

Cited by 49 publications

(36 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scatter above the minimum Rg line is due to soil moisture variation and the effect of soil moisture is higher (indicated by larger scatter) under lower canopy cover and it decreases with the increase in NDVI (indicating an increase in canopy cover). This kind of inverse relationship between EF and NDVI was observed by [11] and was utilized in the development of the DEFrac model for the disaggregation of EF [9]. The DiSoRa model is a direct extension of the EF-NDVI relationship to the Rg-NDVI relationship.…”

Section: Disaggregation Of Rg and Efmentioning

confidence: 99%

“…α was estimated from the MODIS seven-band surface reflectance data following [26] and the εs was estimated from the emissivity of bands 31 and 32 in MODIS following [27]. In Equations (2) and (3), (Rsd)inst [11] and εa [28] were estimated as:…”

Section: Disaggregation Of R G and Efmentioning

confidence: 99%

“…(Rn − G)inst was estimated as [11]: Figure 2. Flowchart indicating the steps involved in estimating λE day at 250 m using the three approaches.…”

Section: Estimation Of Ef and Rg From Satellite Datamentioning

confidence: 99%

“…A single MODIS pixel at 1000 m resolution may include several tens of plots and, hence, it becomes highly difficult to monitor λE at the field scale. However, multiple studies have been carried out for the estimation of λE over India [11][12][13], and they all analyzed data at a coarse spatial resolution (≥ 1000 m). A previous study [8] tested the utility of disaggregated T Rad for the estimation of fine resolution λE over an area in south India, however, the disaggregated λE was not validated due to the lack of ground measurements.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Spatial Disaggregation of Latent Heat Flux Using Contextual Models over India

et al. 2017

View full text Add to dashboard Cite

Estimation of latent heat flux at the agricultural field scale is required for proper water management. The current generation thermal sensors except Landsat-8 provide data on the order of 1000 m. The aim of this study is to test three approaches based on contextual models using only remote sensing datasets for the disaggregation of latent heat flux over India. The first two approaches are, respectively, based on the estimation of the evaporative fraction (EF) and solar radiation ratio at coarser resolution and disaggregating them to yield the latent heat flux at a finer resolution. The third approach is based on disaggregation of the thermal data and estimating a finer resolution latent heat flux. The three approaches were tested using MODIS datasets and the validation was done using the Bowen Ratio energy balance observations at five sites across India. From the validation, it was observed that the first two approaches performed similarly and better than the third approach at all five sites. The third approach, based on the disaggregation of the thermal data, yielded larger errors. In addition to better performance, the second approach based on the disaggregation of solar radiation ratio was simpler and required lesser data processing than the other approaches. In addition, the first two approaches captured the spatial pattern of latent heat flux without introducing any artefacts in the final output.

show abstract

Section: Disaggregation Of Rg and Efmentioning

confidence: 99%

Section: Disaggregation Of R G and Efmentioning

confidence: 99%

“…(Rn − G)inst was estimated as [11]: Figure 2. Flowchart indicating the steps involved in estimating λE day at 250 m using the three approaches.…”

Section: Estimation Of Ef and Rg From Satellite Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatial Disaggregation of Latent Heat Flux Using Contextual Models over India

et al. 2017

View full text Add to dashboard Cite

show abstract

“…[a= 0.75 and b= 1.28 (a and b are the co-efficient (Mallick et al 2009); l0= Solar constant (1367 Wm -2 ); f= sun earth distance correction factor (Astronomical Unit); ω=sun elevation angle (radian) and ω=π/2-θs;θs=Solar zenith angle (radian); insolation (Rs) was computed using Cano et al (1986) Bastiaanssen et al, (1998) APPENDIX I Computation of energy flux parameters from MODIS products (LST, NDVI, albedo).…”

mentioning

confidence: 99%

Mapping of coupling hot spots of satellite derived latent heat flux in indian agro-climatic regions

Choudhury

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:This study focuses on the understanding and mapping of coupling hotspots of LE versus terrestrial and meteorological parameters. Single source surface energy balance model was used to derive surface energy balance parameters. Agro climatic region wise monthly information of terrestrial, energy balance and meteorological parameters were derived during JuneSeptember from decadal analysis of MODIS data (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) The pixel based knowledge of the parameters was incorporated into hierarchical decision rule algorithm and pixel-by-pixel segmentation of monthly coupling of LE versus parameters (ET, EF, SWI, LST, T a ) was generated. The rainfall zonations in a spatiotemporal domain were done based on the LE couplings that clearly demarcated the highest (West Coast Plains and Hills Region, Himalayan region), moderate (Gangetic Plains and Hills Regions, and the Plateau and Hills Regions) and lowest rainfall (Western dry region) areas. The transition of zone-wise availability of rainfall (both surplus and deficient) can be very well understood from the seasonal dynamics of the LE couplings.

show abstract

A simple model for spatial disaggregation of evaporative fraction: Comparative study with thermal sharpened land surface temperature data over India

2013

View full text Add to dashboard Cite

[1] Evaporative fraction (EF) is a measure of the amount of available energy at the earth surface that is partitioned into latent heat flux. The currently operational thermal sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) on satellite platforms provide data only at 1000 m, which constraints the spatial resolution of EF estimates. A simple model (disaggregation of evaporative fraction (DEFrac)) based on the observed relationship between EF and the normalized difference vegetation index is proposed to spatially disaggregate EF. The DEFrac model was tested with EF estimated from the triangle method using 113 clear sky data sets from the MODIS sensor aboard Terra and Aqua satellites. Validation was done using the data at four micrometeorological tower sites across varied agro-climatic zones possessing different land cover conditions in India using Bowen ratio energy balance method. The root-mean-square error (RMSE) of EF estimated at 1000 m resolution using the triangle method was 0.09 for all the four sites put together. The RMSE of DEFrac disaggregated EF was 0.09 for 250 m resolution. Two models of input disaggregation were also tried with thermal data sharpened using two thermal sharpening models DisTrad and TsHARP. The RMSE of disaggregated EF was 0.14 for both the input disaggregation models for 250 m resolution. Moreover, spatial analysis of disaggregation was performed using Landsat-7 (Enhanced Thematic Mapper) ETM + data over four grids in India for contrasted seasons. It was observed that the DEFrac model performed better than the input disaggregation models under cropped conditions while they were marginally similar under non-cropped conditions. Citation: Eswar, R., M. Sekhar, and B. K. Bhattacharya (2013), A simple model for spatial disaggregation of evaporative fraction: Comparative study with thermal sharpened land surface temperature data over India,

show abstract

Latent heat flux estimation in clear sky days over Indian agroecosystems using noontime satellite remote sensing data

Cited by 49 publications

References 76 publications

Spatial Disaggregation of Latent Heat Flux Using Contextual Models over India

Spatial Disaggregation of Latent Heat Flux Using Contextual Models over India

Mapping of coupling hot spots of satellite derived latent heat flux in indian agro-climatic regions

A simple model for spatial disaggregation of evaporative fraction: Comparative study with thermal sharpened land surface temperature data over India

Contact Info

Product

Resources

About