Use of Satellite-based Leaf Area Index Data for Monitoring Green Gram Crop Growth in Ikombe-Katangi Area, Machakos County, Kenya
Manzi, K. H. *
Department of Agriculture Science and Technology, Faculty of Agriculture, Kenyatta University, Kenya.
Shadrack Ngene
Kenya Wildlife Service, Spatial Ecology and Biodiversity Monitoring, Kenya.
Joseph, P. Gweyi-Onyango
Department of Agriculture Science and Technology, Faculty of Agriculture, Kenyatta University, Kenya.
*Author to whom correspondence should be addressed.
Abstract
Monitoring the conditions under which crops grow and estimating their yield is essential to the process of economic development in any nation. The traditional approaches to crop monitoring are labor- and resource-intensive, as well as limited in their ability to cover expansive geographic regions. Since remote sensing data is integrated with ground measurements, it has been used as a potentially useful tool to extract biophysical variables such as leaf area index (LAI), biomass, and phenology. Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes such as yield. The potential of satellite-based LAI for monitoring crop growth was investigated and compared to field measurements. This was the objective of his study. High resolution leaf area index was retrieved from Landsat 8 OLI imageries, and ground leaf area index measurements were taken in Ikombe-Katangi Machakos in Machakos county. An equation based on regression was developed to estimate the leaf area index using the normalized difference vegetation index that was derived from Landsat-8 OLI (NDVI). According to the findings, the derived LAI exhibited strong linear relationships with the leaf area index that was measured on the ground for green gram crops, with RMSE values being 0.09846, and R2 values of 0.9249. The overall findings shed light on the viability of using multispectral data, in estimating leaf area index in a very fragmented agricultural landscape, such as that found in the Ikombe and Katangi areas of Machakos, Kenya. Therefore, accurate crop monitoring on a large scale can be accomplished through remote sensing data in conjunction with LAI measurements taken from the ground. The implementation of the leaf area index is subject to the environmental condition which requires to be investigated as the use of remote sensing data is advocated for. The advancement in the use of satellite data for extraction LAI data is key towards transforming crop production within Kenya ad Africa at large. Scarce resources for crop growth monitoring can now be augmented with open-source satellite datasets such as Landsat 8 in LAI estimation.
Keywords: Leaf area index, satellite data, normalized vegetation index, enhanced vegetation index, landsat 8, remote sensing
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Demarty J, Chevallier F, Friend AD, Viovy N, Piao S, Ciais P. Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model. Geophysical Research Letters. 2007;34(15).
DOI:10.1029/2007GL030014.
Asner GP, Scurlock JMO, Hicke JA. Global synthesis of leaf area index observations: Implications for ecological and remote sensing studies. Glob. Ecol. Biogeogr. 2003;12:191–205.
Reyes-González A, Kjaersgaard J, Trooien T, Sánchez DGR, Sánchez-Duarte JI, Preciado-Rangel P, Fortis-Hernandez M. Comparison of Leaf Area Index, Surface Temperature, and Actual Evapotranspiration Estimated using the METRIC Model and In Situ Measurements. Sensors. 2019;19:1857.
Fassnacht KS, Gower ST, MacKenzie MD, Nordheim EV, Lillesand TM. Estimating the leaf area index of North Central Wisconsin forests using the landsat thematic mapper. Remote. Sens. Environ. 1997;61: 229–245.
Jung M, Reichstein M, Ciais P, Seneviratne SI, Sheffield J, Goulden ML, Bonan G, Cescatti A, Chen J, De Jeu R, et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 2010;467:951–954.
Qi J, Kerr YH, Moran MS, Weltz M, Huete AR, Sorooshian S, Bryant R. Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region. Remote Sensing of Environment. 2000;73(1):18–30.
Available:https://doi.org/10.1016/S0034-4257(99)00113-3
Mourad R, Jaafar H, Anderson M, Gao F. Assessment of leaf area index models using harmonized landsat and sentinel-2 surface reflectance data over a semi-arid irrigated landscape. Remote Sensing 2020;12:3121,12(19), 3121.
Available:https://doi.org/10.3390/RS12193121
Baret F, Guyot G. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote. Sens. Environ. 1991;35:161–173.
Broge N, Leblanc E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote. Sens. Environ. 2001;76:156–172.
Kinane SM, Montes CR, Albaugh TJ, Mishra DR. A model to estimate leaf area index in loblolly pine plantations using landsat 5 and 7 images. Remote Sensing. 2021;13(6).
Available:https://doi.org/10.3390/RS13061140
Gichangi, E. M., Gatheru, M., Njiru, E. N., Mungube, E. O., Wambua, J. M., & Wamuongo, J. W. (2015). Assessment of climate variability and change in semi-arid eastern Kenya. Climatic Change, 130(2), 287–297. https://doi.org/10.1007/s10584-015-1341-2
Republic of Kenya. Machakos District Development Plan 2002-2008: Effective Management for Sustainable Economic Growth and Poverty Reduction. Nairobi, Kenya: Government Printer; 2002
Shisanya CA, Recha C, Anyamba A. Rainfall Variability and Its Impact on Normalized Difference Vegetation Index in ASALs of Kenya. International Journal of Geosciences. 2011;2:36-47.
Jaetzold R, Schmidt H. Farm Management Handbook of Kenya: National Conditions and Farm Management Information, Vol. II: Part A. Western Kenya. Nairobi: Ministry of Agriculture; 1983.
Republic of Kenya. Mainstreaming Sustainable Land Management in Agro-Pastoral Production Systems of Kenya. UNDP Project Document-UNDPPIMS NO.3245, GEF ID 3370; 2010.
Immitzer, Vuolo, Atzberger. First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sensing. 2016;8(3) 166.(3):166.
Available:https://doi.org/10.3390/.
Nasa U.S. Geological Survey. Landsat Data Continuity Mission. USA: USGS; 2013.
Anav A, Murray-Tortarolo G, Friedlingstein P, Sitch S, Piao S, Zhu Z. Evaluation of land surface models in reproducing satellite derived leaf area index over the high-latitude northern hemisphere. Part II: Earth System Models. Remote Sens. 2013;5:3637–3661
Kaufman YJ, Tanré D. Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing. 1992;30(2):261–270.=
Available:https://doi.org/10.1109/36.134076
Kang Y, Özdoğan M, Zipper SC, Román MO, Walker J, Hong SY, Marshall M, Magliulo V, Moreno J, Alonso L, Miyata A, Kimball B, Loheide SP. How Universal Is the Relationship between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment. Remote Sensing 2016;8:5978(7);597.
Available:https://doi.org/10.3390/RS8070597
Manivel L, Weaver RJ. Effect of growth regulators and heat on germination of Tokay grape seeds. Vitis. 1974;12(4):286-90.
Watson DJ. Comparative physiological studies on the growth of field crops: I. variation in net assimilation rate and leaf area between species and varieties, and within and between years. Ann. Bot. 1947;11:41–76.
Fernandes R, Plummer S, Nightingale J, Baret F, Camacho F, Fang H, Garrigues S, Gobron N, Lang M, Lacaze R, et al. Global leaf area index product validation good practices. Version 2.0. Best Practice for Satellite-Derived Land Product Validation; Schaepman-Strub, G., Román, M., Nickeson, J., Eds.; Land Product Validation Subgroup (WGCV/CEOS). 2014;76.
Available: http://lpvs.gsfc.nasa. gov/documents.html
Laurent VCE, Schaepman ME, Verhoef W, Weyermann J, Chávez RO. Bayesian object‐based estimation of LAI and chlorophyll from a simulated Sentinel‐2 top‐of‐atmosphere radiance image. Remote Sensing of Environment. 2014;140 (0):318–329.
Available:https://doi. org/10.1016/j.rse.2013.09.005
Launay M, Gueri. Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agric. Ecosyst. Environ. 2005;111:321–339. [CrossRef]
Food and Agriculture Organization of the United Nations (FAO). Terrestrial Essential Climate Variables for Climate Change Assessment, Mitigation and Adaptation; FAO: Rome, Italy; 2008
Gitelson AA. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology. 2004;161(2):165–173.
Available:https://doi.org/10.1078/0176-1617-01176
Cao X, Zhou Z, Chen X, Shao W, Wang Z. Improving leaf area index simulation of IBIS model and its effect on water carbon and energy—A case study in Changbai Mountain broadleaved forest of China. Ecol. Model. 2015;303:97–104
Kogan F, Salazar L, Roytman L. Forecasting crop production using satellitebased vegetation health indices in Kansas, USA. Int. J. Remote Sens. 2012;33:2798–2814.
Available:https://doi.org/10.1080/01431161.2011.621464
Zhang HK, Chen JM, Huang B, Song HH, Li YR. Reconstructing seasonal variation of Landsat vegetation index related to leaf area index by fusing with MODIS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2014;7(3):950–960.
Available:https://doi.org/10.1109/jstars.2013.2284528
Huang X, Liu J, Zhu W, Atzberger C, Liu Q. The optimal threshold and vegetation index time series for retrieving crop phenology based on a modified dynamic threshold method. Remote. Sens. 2019;11(23):2725.
Available:https://doi.org/10.3390/ rs11232725.
Liu Q, Liang S, Xiao Z, Fang H. Retrieval of leaf area index using temporal, spectral, and angular information from multiple satellite data. Remote Sensing of Environment. 2014;145(0): 25–37.
Available:https://doi.org/10.1016/j.rse.2014.01.021
Huang X, Liu J, Zhu W, Atzberger C. Liu Q. The optimal threshold and vegetation index time series for retrieving crop phenology based on a modified dynamic threshold method. Remote. Sens. 2019;11(23):2725.
Available:https://doi.org/10.3390/ rs11232725.
Parker Geoffrey G. Tamm review: Leaf area index (LAI) Is both a determinant and a consequence of important processes in vegetation canopies. Forest Ecology and Management 2020;477.
Available:https://doi.org/10.1016/j.foreco.2020.118496.
Yan Guangjian, Ronghai Hu, Jinghui Luo, Marie Weiss, Hailan Jiang, Xihan Mu, Donghui Xie, and Wuming Zhang. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agricultural and Forest Meteorology. 2019;265 (March 2018):390–411.
Available:https://doi.org/10.1016/j.agrformet.2018.11.033.
Parker, Geoffrey G. Tamm review: Leaf area index (LAI) is both a determinant and a consequence of important processes in vegetation canopies. Forest Ecology and Management. 2020;477(June).
Available:https://doi.org/10.1016/j.foreco.2020.118496.
Hui, Jiang, and Liu Yao. A method to upscale the leaf area index (LAI) using gf-1 data with the assistance of MODIS Products in the Poyang Lake Watershed.” Journal of the Indian Society of Remote Sensing. 2018;46(4):551–60.
Available:https://doi.org/10.1007/s12524-017-0731-5.
Yu, Yuanhe, Jinliang Wang, Guangjie Liu, and Feng Cheng. Forest leaf area index inversion based on landsat OLI data in the Shangri-La City. Journal of the Indian Society of Remote Sensing. 2019;47 (6):967–76.
Available:https://doi.org/10.1007/s12524-019-00950-6.
Kang, Yanghui, and Mutlu Özdoğan. Field-level crop yield mapping with landsat using a hierarchical data assimilation approach. Remote Sensing of Environment. 2019;228:144–63.
Available:https://doi.org/10.1016/j.rse.2019.04.005.
Klingler A, Schaumberger A, Vuolo F, Kalmár LB, Pötsch EM. Comparison of Direct and Indirect Determination of Leaf Area Index in Permanent Grassland. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 2020;88(5):369–378.
Available:https://doi.org/10.1007/s41064-020-00119-8
Jonckheere I, Fleck S, Nackaerts K, Muys B, Coppin P, Weiss M, Baret F. Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology. 2004;121(1–2):19–35.