[1] W. Zhang et al., "WTS: A Weakly towards strongly supervised learning framework for remote sensing land cover classification using segmentation models". Remote Sensing, 2021. 13(3): p. 394.
[2] A. Orynbaikyzy, U. Gessner & C. Conrad, "Crop type classification using a combination of optical and radar remote sensing data: a review". international journal of remote sensing, 2019. 40(17): p. 6553-6595.
[3] J. Jung et al., "The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems". Current Opinion in Biotechnology, 2021. 70: p. 15-22.
[4] Q. Xie et al., "Crop height estimation of corn from multi-year RADARSAT-2 polarimetric observables using machine learning". Remote Sensing, 2021. 13(3): p. 392.
[5] G.A. Carter, "Responses of leaf spectral reflectance to plant stress". American Journal of Botany, 1993. 80(3): p. 239-243.
[6] J. Peñuelas et al., "Reflectance indices associated with physiological changes in nitrogen-and water-limited sunflower leaves". Remote sensing of Environment, 1994. 48(2): p. 135-146.
[7] H.C. Stimson et al., "Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma". Remote Sensing of Environment, 2005. 96(1): p. 108-118.
[8] C. Xu et al., "Monitoring crop water content for corn and soybean fields through data fusion of MODIS and Landsat measurements in Iowa". Agricultural Water Management, 2020. 227: p. 105844.
[9] J. Penuelas, I. Filella, C. Biel, L. Serrano & R. Save, "The reflectance at the 950-970 nm region as an indicator of plant water status". International Journal of Remote Sensing, 1993. 14: p. 1887-1905.
[10] S.L. Ustin et al., "Estimating canopy water content of chaparral shrubs using optical methods". Remote Sensing of Environment, 1998. 65(3): p. 280-291.
[11] J. Carlson, and R. Burgan, "Review of users' needs in operational fire danger estimation: the Oklahoma example". International Journal of remote sensing, 2003. 24(8): p. 1601-1620.
[12] E. Chuvieco et al., Improving burning efficiency estimates through satellite assessment of fuel moisture content. Journal of Geophysical Research: Atmospheres, 2004. 109(D14).
[13] W. Kong et al., "Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence". Remote Sensing, 2021. 13(20): p. 4125.
[14] A.F. Goetz et al., "Imaging spectrometry for earth remote sensing". science, 1985. 228(4704): p. 1147-1153.
[15] P.J. Curran, J.A. Kupiec and G.M. Smith, "Remote sensing the biochemical composition of a slash pine canopy. Geoscience and Remote Sensing", IEEE Transactions on, 1997. 35(2): p. 415-420.
[16] R. Colombo et al., "Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling". Remote Sensing of Environment, 2008. 112(4): p. 1820-1834.
[17] M. Hardisky, V. Klemas and M. Smart, "The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of". Spartina alterniflora, 1983. 49: p. 77-83.
[18] B. C. Gao, "Normalized difference water index for remote sensing of vegetation liquid water from space". in Imaging Spectrometry. 1995. International Society for Optics and Photonics.
[19] G. Krishna et al., "Application of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring". Geocarto International, 2021. 36(5): p. 481-498.
[20] R. Filgueiras et al., "Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data". Agricultural Water Management, 2020. 241: p. 106346.
[21] L. Zhang et al., "Monitoring cotton (Gossypium hirsutum L.) leaf ion content and leaf water content in saline soil with hyperspectral reflectance". European Journal of Remote Sensing, 2014. 47: p. 593-610.
[22] E.B. Knipling, "Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation". Remote sensing of environment, 1970. 1(3): p. 155-159.
[23] C.J. Tucker, "Remote sensing of leaf water content in the near infrared". Remote sensing of Environment, 1980. 10(1): p. 23-32.
[24] P. Ceccato et al., "Detecting vegetation leaf water content using reflectance in the optical domain". Remote sensing of environment, 2001. 77(1): p. 22-33.
[25] P.J. Zarco-Tejada and S. Ustin. "Modeling canopy water content for carbon estimates from MODIS data at land EOS validation sites". in IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No. 01CH37217). 2001. IEEE.
[26] M.R. Mobasheri, and S.B. Fatemi, "Leaf Equivalent Water Thickness assessment using reflectance at optimum wavelengths". Theoretical and Experimental Plant Physiology, 2013. 25(3): p. 196-202.
[27] R. Pu et al., "Spectral absorption features as indicators of water status in coast live oak (Quercus agrifolia) leaves". International Journal of Remote Sensing, 2003. 24(9): p. 1799-1810.
[28] P. Bowyer and F. Danson, "Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy level". Remote Sensing of Environment, 2004. 92(3): p. 297-308.
[29] E.R. Hunt et al., "Remote sensing leaf chlorophyll content using a visible band index". 2011.
[30] D.M. Kim et al., "Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis". Scientific reports, 2015. 5(1): p. 1-11.
[31] F. Rasheed, S. Delagrange, and F. Lorenzetti, "Detection of plant water stress using leaf spectral responses in three poplar hybrids prior to the onset of physiological effects". International Journal of Remote Sensing, 2020. 41(14): p. 5127-5146.
)