Anderson, M. C., Neale, C. M. U., Li, F., Norman, J. M., Kustas, W. P., Javanthi, H and Chavez, J. (2004). Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Remote Sensing of Enviroment, 92, 447-464.
Asad, M. H. and Bais, A. (2020). Crop and weed leaf area index mapping using multi-source remote and proximal sensing
. IEEE Access, 8, 138179- 138190.
Baghzouz, M., Devitt, D. A., Fenstermaker, L. and Young, M. H. (2010). Monitoring vegetation phenological cycles in two different semi-arid environmental settings using a ground-based NDVI system: A potential approach to improve satellite data interpretation. Remote Sensing, 2, 990–1013.
Baret, F. and Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35, 161–173.
Bonan, G. B. (1993). Importance of leaf-area index and forest type when estimating photosynthesis in boreal forests. Remote Sensing of Environment, 43, 303–314.
Bréda, N. J. J. (2003). Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. Journal of Experimental Botany, 54 (392), 2403–2417.
Broge, N. and Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76, 156–172.
Carricondo, C. A., Di Mauro, F., de Beeck, M. O., Roland, M., Gielen, B., Vitale, D., Ceulemans, R. and Papale, D. (2019). A comparison of di_erent methods for assessing leaf area index in four canopy types. Central European Forestry Journal, 65, 67–80.
Chen, Z., Sun, S., Zhu, Z., Jiang, H. and Zhang, X. (2019). Assessing the effects of plant density and plastic film mulch on maize evaporation and transpiration using dual crop coefficient approach. Agricultural Water Management, 225, 105765.
Clevers, J. G. P. W. (1988). The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sensing of Environment, 25(1), 53–69.
Cowling, S. A., and Field, C. B. (2003). Environmental control of leaf area production: Implications for vegetation and land-surface modeling. Global Biogeochemical Cycles, 17(1), 1-14.
Das, B., Sahoo, R. N., Pargal, S., Krishna, G., Verma, R., Viswanathan, C., Sehgal, V. K. and Gupta, V. K. (2020). Comparative analysis of index and chemometric techniques based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands. Geocarto International, 35, 1415- 1432.
Deng, Z., Guan, H., Hutson, J., Forster, M. A., Wang, Y. and Simmons, C. T. (2017). A vegetation-focused soil-plant-atmospheric continuum model to study hydrodynamic soil-plant water relations. Water Resources Research, 53, 4965–4983.
Fern, R. R., Foxley, E.A., Bruno, A. and Morrison, M. L. (2018). Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland. Ecological Indicators, 94, 16–21.
Gao, B. C. (1996). NDWI- A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257- 266.
Gitelson, A. A., and Merzlyak, M. N. (1994). Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves: Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143, 286–292.
Gitelson, A. A., Viña, A., Arkebauer, T. J., Rundquist, D. C., Keydan, G. and Leavitt, B. (2003). Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letter, 30 (5), 1-4.
Govaerts, Y. M., Verstraete, M. M., Pinty, B. and Gobron, N. (1999). Designing optimal spectral indices: a feasibility and proof of concept study. International Journal of Remote Sensing, 20 (9), 1853–1873.
Gu, Z., Sanchez-Azofeifa, A., Feng, J. and Cao, S. (2015). Predictability of leaf area index using vegetation indices from multiangular CHRIS/PROBA data over eastern China. Journal of Applied Remote Sensing, 9(1), 096085.
Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J. and Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90, 337-352.
Hansen, P. and Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86, 542–553.
He, L., Zhang, H. Y., Zhang, Y. S., Song, X., Feng, W., Kang, G. Z., Wang, C. Y. and Guo, T. (2016). Estimating canopy leaf nitrogen concentration in winter wheat based on multi-angular hyperspectral remote sensing. European Journal of Agronomy, 73, 170–185.
Huete, A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25 (3), 295–309.
Huete, A., Justice, C. and van-Leeuwen, W. (1996). MODIS vegetation index (MOD13), EOS MODIS algorithm; theoretical basis document; NASA Goddard Space Flight Center: Greenbelt, MD, USA.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X. and Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the modis vegetation indices. Remote Sensing of Environment, 83(1-2), 195–213.
Li, S., Li., Y., Lin, H., Feng, H. and Dyck, M. (2017). Effects of different mulching technologies on evapotranspiration andsummer maize growth.
Agricultural Water Management, 201, 309-318.
Liu, J., Pattey, E. and Jego, G. (2012). Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons. Remote Sensing of Environment, 123, 347–358.
Liu, Y., Xiao, J., Ju, W., Zhu, G., Wu, X., Fan, W., Li, D. and Zhou, Y. (2018). Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes. Remote Sensing of Environment, 206, 174–188.
Mannschatz, T., Pflug, B., Borg, E., Feger, K. H. and Dietrich, P. (2014). Uncertainties of LAI estimation from satellite imaging due to atmospheric correction. Remote Sensing of Environment, 153, 24- 39.
Martens, S. N., Ustin, S. L. and Rousseau, R.A. (1993). Estimation of tree canopy leaf area index by gap fraction analysis. Forest Ecology and Management. 61, 91–108.
Mokhtari, A., Noory, H. and Vazifedoust, M. (2018). Improving crop yield estimation by assimilating LAI and inputting satellitebased surface incoming solar radiation into SWAP model. Agricultural and Forest Meteorology, 250–251, 159–170.
Moulin, S. and Guerif, M. (1999). Impacts of model parameter uncertainties on crop reflectance estimates: A regional case study on wheat. International Journal of Remote Sensing, 20, 213–218.
Mourad, R., Jaafar, H., Anderson, M. and Gao, F. (2020). Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape. Remote Sensing, 12(19), 3121.
Nguy-Robertson, A. L., Gitelson, A., Peng, Y., Vina, A., Arkebauer, T. and Rundquist, D. (2012). Green leaf area index estimation in maize and soybean: combining vegetation indices to achieve maximal sensitivity. Agronomy Journal, 104(5), 1336- 1347.
Nguy-Robertson, A. L. and Gitelson, A. A. (2015). Algorithms for estimating green leaf area index in C3 and C4 crops for MODIS, Landsat TM/ETM+, MERIS, Sentinel MSI/OLCI, and Venμs sensors. Remote Sensing Letters, 6(5), 360- 369.
Rondeaux, G., Steven, M. and Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95-107.
Rouse, R. J., Haas, R. H., Schell, J. A. and Deering, D. W. (1974). Monitoring vegetation systems in the great plains with ERTS, NASA special publication, 351, 0- 309.
Schmidt, M. and Lipson, H. (2009). Distilling free-form natural laws from experimental data. Science, 324 (5923), 81–85.
Sripada, R., Heiniger, R. W., White, J. G. and Meijer, A. D. (2006). Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy Journal, 98, 968–977.
Thorp, K. R. and Tian, L. F. (2004). A review on remote sensing of weeds in agriculture. Precision Agriculture, 5, 477–508.
Tucker, C. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8; 127–150.
Vina, A., Gitelson, A. A., Nguy-Robertson, A. L. and Peng, Y. (2011). Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment, 115, 3468–3478.
Wang, S. X., Wang, Z. J., Zuo, Z. and Guo, Y. Z. (2004). Effects of difference mulching on the soil environment and maize yield in rain fed land. Journal of Arid Land Resources & Environment, 18(9), 134–137.
Wang, R., He, N., Li, S., Xu, L. and Li, M. (2021). Spatial variation and mechanisms of leaf water content in grassland plants at the biome scale: Evidence from three comparative transects. Scientific Reports, 11, 9281.
Xiao, Z. Q., Liang, S. L., Wang, J. D., Chen, P., Yin, X. J., Zhang, L. Q. and Song, J. L. (2014). Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209–223.
Zarate-Valdez, J. L., Whiting, M. L., Lampinen, B. D., Metcalf, S., Ustin, S. L. and Brown, P. H. (2012). Prediction of leaf area index in almonds by vegetation indexes. Computers and Electronics in Agriculture, 85, 24–32.
Zhang, Y., Yang, J., Liu, X., Du, L., Shi, S., Sun, J. and Chen, B. (2020). Estimation of multi-species leaf area index based on chinese GF-1 satellite data using look-up table and gaussian process regression methods. Sensors, 20(9), 2460.
Zheng, J., Fan, J., Zhang, F., Yan, S., Guo, J., Chen, D. and Li, Z. (2018). Mulching mode and planting density affect canopy interception loss of rainfall and water use efficiency of dryland maize on the Loess Plateau of China. Journal of Arid Land, 10(5), 794- 808.
)