Estimation of Site-specific Crop Coefficients for Major Crops of Lalgudi Block in Tamil Nadu using Remote Sensing based Algorithms

J. Ramachandran; R. Lalitha; S. Vallal Kannan

doi:10.52151/jae2021581.1735

Authors

J. Ramachandran Department of Soil and Water Conservation Engineering College and Research Institute, Tamil Nadu Agricultural University, Kumulur Author
R. Lalitha Department of Soil and Water Conservation Engineering, College and Research Institute, Tamil Nadu Agricultural University, Kumulur-621712, Trichy, India Author
S. Vallal Kannan (Agronomy), Department of Irrigation and Drainage Engineering, Agricultural Engineering College and Research Institute, Tamil Nadu Agricultural University, Kumulur-621712, Trichy, India Author

DOI:

https://doi.org/10.52151/jae2021581.1735

Keywords:

Banana, crop coefficient, NDVI, paddy, remote sensing, SEBAL, sugarcane

Abstract

Crop coefficient (Kc) is an important parameter in estimating the crop water requirements during different crop growth stages. The Kc values for a particular crop are highly site- and region-specific, and needs to be precisely determined for each agro-climatic region for better irrigation scheduling and improved water and crop productivity. The site-specific crop coefficients for paddy, sugarcane, and banana cultivated in Lalgudi block, Tiruchirapalli district, Tamil Nadu, India, were estimated using two remote sensing-based methods viz. NDVI-Kc linear regression technique and SEBAL actual evapotranspiration to reference evapotranspiration ratio approach (SEBAL-Kc). The Kc values obtained by remote sensing methods were compared with FAO-56 Adjusted Kc (ClimAdj-Kc) for local climatic conditions and FAO-56 tabulated reference Kc values (FAOTab-Kc). Regression analysis revealed a good agreement between NDVI-Kc and ClimAdj-Kc for paddy (R2=0.95), banana (R2=0.93), and sugarcane (R2=0.79). Compared to FAO56-Kc, the derived Kc values using NDVI-Kc were higher, while the SEBAL-Kc values were lower for all growth stages of paddy. For sugarcane crop, the FAO-56 Kc, NDVI-Kc and ClimAdj-Kc for local climate were almost similar in all stages. In case of banana, NDVI-Kc and SEBAL-Kc were higher as compared to the FAO-56-Kc and ClimAdj-Kc. SEBAL approach performs well as it accounted for local climatic conditions and crop canopy changes, whereas NDVI considered only crop canopy. However, SEBAL method is computationally intensive as compared to NDVI-Kc method. The Kc values estimated in this study can be important in quantifying the crop evapotranspiration at regional and field scale, leading to better decision making in irrigation scheduling.

Author Biographies

J. Ramachandran, Department of Soil and Water Conservation Engineering College and Research Institute, Tamil Nadu Agricultural University, Kumulur

Research Scholar
R. Lalitha, Department of Soil and Water Conservation Engineering, College and Research Institute, Tamil Nadu Agricultural University, Kumulur-621712, Trichy, India

Professor and Head
S. Vallal Kannan, (Agronomy), Department of Irrigation and Drainage Engineering, Agricultural Engineering College and Research Institute, Tamil Nadu Agricultural University, Kumulur-621712, Trichy, India

Assistant Professor

References

Allen R G; Pereira L S; Raes D; Smith M. 1998. Crop Evapo-transpiration: Guidelines for Computing Crop Water Requirements. Food and Agricultrual Organization, Rome, 56(97), 90-129.

Anju B; Kishan S R; Anil K M; Amit S. 2016. Assessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm, India. Geocarto Int., 31(7), 739-764.

Ayad A F B; Ahmed H A S; Adrian O; Anna J; Adriana M. 2016. Estimation of evapotranspiration using SEBAL algorithm and Landsat-8 data—A case study: Tatra mountains region. J. Geological Resour. Eng., 4(6), 257-270.

Bala A; Pawar P S; Misra A K; Rawat K S. 2017. Estimation and validation of actual evapotranspiration for wheat crop using SEBAL model over Hisar district, Haryana, India. Current Sci., 113(1), 134-141.

Bashir M A; Hata T; Tanakamaru H; Abdelhadi A W; Tada A. 2007.Remote sensing derived crop coefficient for estimating crop water requirements for irrigated sorghum in the Gezira Scheme, Sudan. J. Enviorn. Info., 10(1), 47-54.

Bastiaanssen W G M; Menenti M; Feddes R A; Holtstlag A A M. 1998. A remote sensing surface energy balance algorithm for land (SEBAL) 1. Formulation. J. Hydrol., 212(1), 198-212.

Bastos E A; Silva C R; Rodrigues B H N; Andrade Jr; Aderson S; Ibiapina L M M. 2012. Evapotranspiration and crop coefficient of drip irrigated watermelon in Piaui coastline, Brazil. Engenharia Agríc., 32(3), 582- 590.

Casa R; Rossi M; Sappa G; Trotta A. 2008. Assessing crop water demand by remote sensing and GIS for the Pontina Plain, Central Italy. Water Resour. Manage., 23(9), 1685-1712.

Gontia N K; Tiwari K N. 2009. Estimation of crop coefficient and evapotranspiration of wheat (Triticum aestivum) in an irrigation command using remote sensing and GIS. Water Resour. Manage., 24(7), 1399- 1414.

Han M; Zhang H; DeJonge K C; Comas L H;Gleason S. 2018. Comparison of three crop water stress index models with sap flow measurements in maize. Agric. Water Manage., 203, 366-375.

Jana C; Rowat M; Sena D R; Alam N M; Mandal U; Kaushal R; Mishra P K. 2016. Applicaiton of SEBAL model to estimate evapotranspiration in Doon Valley, India. Indian J. Soil Conserv., 44(2), 191-197.

Kamble B; Kilic A; Hubbard K. 2013. Estimating crop coefficients using remote sensing-based vegetation index. Remote Sens., 5(4), 1588-1602.

Kingra P K; Majumder D; Singh S P. 2016. Application of remote sensing and GIS in agriculture and natural resource management under changing climatic conditions. Agric. Res. J., 53(3), 295-302.

Mokhtari A; Noory H; Vazifedoust M; Bahrami M. 2018. Estimating net irrigation requirement of winter wheat using model- and satellite-based single and basal crop coefficients. Agric. Water Manage., 208, 95-106.

Montazar A; Rejmanek H; Tindula G; Little C; Shapland T; Anderson F; Inglese G; Mutters R; Linquist B; Greer C A; Hill J; Snyder R L. 2017. Crop coefficient curve for paddy rice from residual energy balance calculations. J. Irrig. Drain. Eng., 143(2), 1-14.

Moore D S; Notz W I; Flinger M A. 2013. The Basic Practice of Statistics.XIEd. , W. H. Freeman and Company, New York, NY, pp: 138.

Salifu T; Agyare W A. 2012. Distinguishing land-use types using Surface Albedo and Normalized Difference Vegetation Index derived from the SEBAL model for the Atankwidi and Afram Sub-Catchments in Ghana. ARPN J. Eng. Appl. Sci., 7(1), 69-80.

Singh R K; Irmak A. 2009. Estimation of crop coefficients using satellite remote sensing. J. Irrig. Drain. Eng., 135(5), 597-608.

Valiantzas J D. 2018. Temperature-and humiditybased simplified Penman’s ET0 formulae. Comparisons with temperature-based Hargreaves-Samani and other methodologies. Agric. Water Manage., 208, 326-334.

Win S K; Zamora O; Thein S. 2014. Determination of the water requirement and Kc values of sugarcane at different crop growth stages by Lysimetric Method. Sugar Technol., 16, 286-294.