Estimation of Single Crop Coefficient and Crop Evapotranspiration Using Remote Sensing for Irrigation Management

- Suhardi, Bambang Marhaenanto, Joni Murti Mulyo Aji


Indonesia has rainy and dry seasons sequentially from April to October and October to March. In the dry season, water availability in the soil gradually decreases, especially from June to October, when the peak of drought occurs, causing many agricultural lands to be left uncultivated. However, a small portion of agricultural land can still be planted for crops such as maize and groundnut. However, limited water availability causes crop growth to be disrupted because the amount of water absorbed by crop roots is less than the amount of evapotranspiration water. Therefore, an accurate evapotranspiration estimation technique is needed to make the water supply efficient. This study aims to evaluate the reliability of the technique of estimating crop coefficient (Kc) of maize and groundnut and temperature at the research location. A linear relationship between the Normalized Difference Vegetation Index (NDVI) from sentinel two imagery and Kc-FAO was used to estimate Kc. Meanwhile, a linear relationship between LST from Landsat 8 imagery and the results of interpolating temperature data from 4 climatological stations were used to estimate the temperature at the research site. The results of estimation showed that Kc of maize and groundnut were very accurate with the determinant coefficients (R2) respectively 0.8791 and 0.9352. This is similar to the results of the temperature estimation of the research location, showing a very accurate R2 is 0.9073. The results of this study are expected to be used for future research to improve water crop management.


Sentinel 2; Landsat 8; crop coefficient; evapotranspiration; remote sensing.

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Q. Zhang, Z. Yang, X. Hao, and P. Yue, “Conversion features of evapotranspiration responding to climate warming in transitional climate regions in northern China,†Clim. Dyn., vol. 52, no. 7–8, pp. 3891–3903, 2019, doi: 10.1007/s00382-018-4364-3.

E. Eichelmann et al., “The effect of land cover type and structure on evapotranspiration from agricultural and wetland sites in the Sacramento–San Joaquin River Delta, California,†Agric. For. Meteorol., 2018, doi: 10.1016/j.agrformet.2018.03.007.

X. Sun, B. P. Wilcox, and C. B. Zou, “Evapotranspiration partitioning in dryland ecosystems: A global meta-analysis of in situ studies,†J. Hydrol., vol. 576, no. June, pp. 123–136, 2019, doi: 10.1016/j.jhydrol.2019.06.022.

W. Dong, C. Li, Q. Hu, F. Pan, J. Bhandari, and Z. Sun, “Potential Evapotranspiration Reduction and Its Influence on Crop Yield in the North China Plain in 1961-2014,†Hindawi Adv. Meteorol., vol. 2020, p. 10, 2020, doi: 10.1155/2020/3691421.

Dwipa, I., Kasim, M., Rozen, N., & Nurhamidah, N. (2020). Land flooding effect Before and After Planting on Rice Yield in System Of Rice Intensification. International Journal on Advance Science Engineering Information Technology, 10(3).

R. G. Allen, L. S.Pereira, D. Raes, and M. Smith, FAO Irrigation and Drainage Paper No. 56, vol. 13, no. 3. Roma: FAO - Food and Agriculture Organization of the United Nations, 1998.

K. Xiang, Y. Li, R. Horton, and H. Feng, “Similarity and difference of potential evapotranspiration and reference crop evapotranspiration – a review,†Agric. Water Manag., vol. 232, no. January, 2020, doi: 10.1016/j.agwat.2020.106043.

R. Filgueiras et al., “Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data,†Agric. Water Manag., vol. 241, no. June, p. 106346, 2020, doi: 10.1016/j.agwat.2020.106346.

A. Ribera-Fonseca, E. Jorquera-Fontena, M. Castro, P. Acevedo, J. C. Parra, and M. Reyes-Diaz, “Exploring VIS/NIR reflectance indices for the estimation of water status in highbush blueberry plants grown under full and deficit irrigation,†Sci. Hortic. (Amsterdam)., vol. 256, no. June, p. 108557, 2019, doi: 10.1016/j.scienta.2019.108557.

N. Mzid, V. Cantore, G. De Mastro, R. Albrizio, M. H. Sellami, and M. Todorovic, “The Application of Ground-Based and Satellite Remote Sensing for Estimation of Bio-Physiological Parameters of Wheat Grown Under Different Water Regimes,†Water, vol. 12, no. 8, p. 2095, 2020, doi: 10.3390/w12082095.

U. Mahajan and B. R. Bundel, “Drones for Normalized Difference Vegetation Index ( NDVI ), to Estimate Crop Health for Precision Agriculture: A Cheaper Alternative for Spatial Satellite Sensors,†in International Conference on Innovative Research in Agriculture, Food Science, Forestry, Horticulture, Aquaculture, Animal Sciences, Biodiversity, Ecological Sciences and Climate Change (AFHABEC-2016), 2016, no. January, pp. 38–41.

J. M. Chen and J. Liu, “Evolution of evapotranspiration models using thermal and shortwave remote sensing data,†Remote Sens. Environ., vol. 237, no. December 2019, p. 111594, 2020, doi: 10.1016/j.rse.2019.111594.

A. Reyes-gonzález et al., “Estimation of Crop Evapotranspiration Using Satellite Remote Sensing-Based Vegetation Index,†Adv. Meteorol., vol. 2018, no. 1, 2018.

C. J. Harmse, H. Gerber, and A. Van Niekerk, “Evaluating Several Vegetation Indices Derived from Sentinel-2 Imagery for Quantifying Localized Overgrazing in a Semi-Arid Region of South Africa,†Remote Sens., vol. 14, no. 1720, p. 18, 2022, doi: 10.3390/rs14071720.

H. K. Zhang et al., “Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences,†Remote Sens. Environ., vol. 215, no. April, pp. 482–494, 2018, doi: 10.1016/j.rse.2018.04.031.

S. R. Karlsen, L. Stendardi, H. Tømmervik, L. Nilsen, I. Arntzen, and E. J. Cooper, “Time-series of cloud-free sentinel-2 ndvi data used in mapping the onset of growth of central spitsbergen, svalbard,†Remote Sens., vol. 13, no. 15, pp. 1–14, 2021, doi: 10.3390/rs13153031.

K. Yang, Y. Luo, M. Li, S. Zhong, Q. Liu, and Xiuhong Li, “Reconstruction of Sentinel-2 Image Time Series Using Google Earth Engine,†Remote Sens., vol. 14, no. 4395, p. 24, 2022.

E. Firmansyah, J. Gaol, and etyo B. Susilo, “Comparison of SVM and Decision Tree Classifier with Object Based Approach for Mangrove Mapping to Sentinel-2B Data on Gili Sulat, Lombok Timur,†J. Nat. Resour., vol. 9, no. 3, pp. 746–757, 2019, doi: 10.29244/JPSL.9.3.746-757.

J. Li and D. P. Roy, “A global analysis of Sentinel-2a, Sentinel-2b and Landsat-8 data revisit intervals and implications for terrestrial monitoring,†Remote Sens., vol. 9, no. 902, pp. 1–17, 2017, doi: 10.3390/rs9090902.

S. Li, J. Wang, D. Li, Z. Ran, and B. Yang, “Evaluation of landsat 8-like land surface temperature by fusing landsat 8 and modis land surface temperature product,†Processes, vol. 9, no. 12, pp. 1–19, 2021, doi: 10.3390/pr9122262.

Y. Zhang, W. Han, X. Niu, and G. Li, “Maize crop coefficient estimated from UAV-measured multispectral vegetation indices,†Sensors (Switzerland), vol. 19, no. 23, pp. 1–17, 2019, doi: 10.3390/s19235250.

S. K. Dingre, S. D. Gorantiwar, and S. A. Kadam, “Correlating the field water balance derived crop coefficient (Kc) and canopy reflectance-based NDVI for irrigated sugarcane,†Precis. Agric., vol. 22, no. 4, pp. 1134–1153, 2021, doi: 10.1007/s11119-020-09774-8.

H. Niu, D. Wang, and Y. Chen, “Estimating Crop Coefficients Using Linear and Deep Stochastic Configuration Networks Models and UAV-Based Normalized Difference Vegetation Index Estimating Crop Coefficients Using Linear and Deep Stochastic Configuration Networks Models and UAV-Based Norm,†2020, no. September, pp. 1485–1490, doi: 10.1109/ICUAS48674.2020.9213888.

R. Wikantiyoso, A. G. Sulaksono, and T. Suhartono, “Detection of potential green open space area using landsat 8 satellite imagery,†ARTEKS J. Tek. Arsit., vol. 6, no. 1, pp. 149–154, 2021, doi: 10.30822/arteks.v6i1.730.

L. Wang, Y. Lu, and Y. Yao, “Comparison of three algorithms for the retrieval of land surface temperature from landsat 8 images,†Sensors (Switzerland), vol. 19, no. 22, 2019, doi: 10.3390/s19225049.

U. Avdan and G. Jovanovska, “Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data,†J. Sensors, vol. 2016, 2016, doi: 10.1155/2016/1480307.

J. A. Sobrino, J. C. Jiménez-Muñoz, and L. Paolini, “Land surface temperature retrieval from LANDSAT TM 5,†Remote Sens. Environ., vol. 90, no. 4, pp. 434–440, 2004, doi: 10.1016/j.rse.2004.02.003.

F. Wang, Z. Qin, C. Song, L. Tu, A. Karnieli, and S. Zhao, “An improved mono-window algorithm for land surface temperature retrieval from landsat 8 thermal infrared sensor data,†Remote Sens., vol. 7, no. 4, pp. 4268–4289, 2015, doi: 10.3390/rs70404268.

J. Cao, W. Zhou, Z. Zheng, T. Ren, and W. Wang, “Within-city spatial and temporal heterogeneity of air temperature and its relationship with land surface temperature,†Landsc. Urban Plan., vol. 206, no. November 2020, p. 103979, 2021, doi: 10.1016/j.landurbplan.2020.103979.

M. A. El-Shirbeny, B. Abdellatif, A. E. M. Ali, and N. H. Saleh, “Evaluation of Hargreaves based on remote sensing method to estimate potential crop evapotranspiration,†Int. J. GEOMATE, vol. 11, no. 1, pp. 2143–2149, 2016, doi: 10.21660/2016.23.1122.

H. R. Fooladmand and S. H. Ahmadi, “Monthly spatial calibration of Blaney-Criddle equation for calculating monthly ET o in south of Iran ,†Irrig. Drain., vol. 58, no. 2, pp. 234–245, 2009, doi: 10.1002/ird.409.

N. Seenu, D. R. M. K. Chetty, T. Srinivas, K. M. A. Krishna, and D. A. Selokar, “Reference Evapotranspiration Assessment Techniques for Estimating Crop Water Requirement,†Int. J. Recent Technol. Eng., vol. 8, no. 4, pp. 1094–1100, 2019, doi: 10.35940/ijrte.d6738.118419.

F. O. Akinyemi, M. Ikanyeng, and J. Muro, “Land cover change effects on land surface temperature trends in an African urbanizing dryland region,†City Environ. Interact., vol. 4, no. 2019, p. 100029, 2019, doi: 10.1016/j.cacint.2020.100029.



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