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Research Article Open access CC BY 4.0

Use of Satellite-based Leaf Area Index Data for Monitoring Green Gram Crop Growth in Ikombe-Katangi Area, Machakos County, Kenya

Manzi, K. H., Shadrack Ngene, Joseph, P. Gweyi-Onyango

Asian Journal of Advances in Agricultural Research · pp. 53–63 · Published 20 Oct 2023

10.9734/ajaar/2023/v23i3468

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.

Leaf area index satellite data normalized vegetation index enhanced vegetation index landsat 8 remote sensing

References (32)

  1. 1 Recent decline in the global land evapotranspiration trend due to limited moisture supply [DOI]
  2. 2 Potentials and limits of vegetation indices for LAI and APAR assessment [DOI]
  3. 3 Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density [DOI]
  4. 4 Review of methods for in situ leaf area index determination [DOI]
  5. 5 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 [DOI]
  6. 6 Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation [DOI]
  7. 7 First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe [DOI]
  8. 8 Global synthesis of leaf area index observations: implications for ecological and remote sensing studies [DOI]
  9. 9 Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives [DOI]
  10. 10 Estimating the leaf area index of North Central Wisconsin forests using the landsat thematic mapper [DOI]
  11. 11 Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications [DOI]
  12. 12 Tamm review: Leaf Area Index (LAI) is both a determinant and a consequence of important processes in vegetation canopies [DOI]
  13. 13 Field-level crop yield mapping with Landsat using a hierarchical data assimilation approach [DOI]
  14. 14 Forecasting crop production using satellite-based vegetation health indices in Kansas, USA [DOI]
  15. 15 How Universal Is the Relationship between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment [DOI]
  16. 16 The Optimal Threshold and Vegetation Index Time Series for Retrieving Crop Phenology Based on a Modified Dynamic Threshold Method [DOI]
  17. 17 Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape [DOI]
  18. 18 Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region [DOI]
  19. 19 Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model [DOI]
  20. 20 Evaluation of Land Surface Models in Reproducing Satellite Derived Leaf Area Index over the High-Latitude Northern Hemisphere. Part II: Earth System Models [DOI]
  21. 21 Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image [DOI]
  22. 22 Assessment of climate variability and change in semi-arid eastern Kenya [DOI]
  23. 23 Reconstructing Seasonal Variation of Landsat Vegetation Index Related to Leaf Area Index by Fusing with MODIS Data [DOI]
  24. 24 Retrieval of leaf area index using temporal, spectral, and angular information from multiple satellite data [DOI]
  25. 25 Comparison of Leaf Area Index, Surface Temperature, and Actual Evapotranspiration Estimated Using the METRIC Model and In Situ Measurements [DOI]
  26. 26 Comparison of Direct and Indirect Determination of Leaf Area Index in Permanent Grassland [DOI]
  27. 27 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 [DOI]
  28. 28 Forest Leaf Area Index Inversion Based on Landsat OLI Data in the Shangri-La City [DOI]
  29. 29 A Model to Estimate Leaf Area Index in Loblolly Pine Plantations Using Landsat 5 and 7 Images [DOI]
  30. 30 A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed [DOI]
  31. 31 Effect of growth regulators and heat on germination of Tokay grape seeds [DOI]
  32. 32 Landsat Data Continuity Mission [DOI]

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