Estimating soil moisture content in apple orchards using UAV remote sensing data: Application of LST/LAI two-stage feature space theory

Authors

  • Long Zhao 1. College of Water Resource and Hydropower, State Key Lab of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China; 2. College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang 471000, Henan, China
  • Xincheng Lei 2. College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang 471000, Henan, China
  • Yuehua Ding 2. College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang 471000, Henan, China
  • Ningbo Cui 1. College of Water Resource and Hydropower, State Key Lab of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China;
  • Dan Meng 3. Chinese Society of Agricultural Engineering, Beijing 100125, China
  • Yi Shi 4. College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, Henan, China;
  • Minglei Zhang 4. College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, Henan, China;
  • Xinbo Zhao 4. College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, Henan, China;
  • Xiaoxian Zhang 5. Department of Sustainable Agriculture Sciences, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK

DOI:

https://doi.org/10.25165/ijabe.v18i4.9730

Keywords:

soil moisture, remote sensing, UAV, LST/LAI, two-stage feature space

Abstract

Soil moisture is a critical component of the soil-plant-atmosphere continuum (SPAC) in fruit trees. However, highprecision monitoring of orchard soil moisture at the regional scale still remains a challenge. This study presents a two-stage feature space model to estimate root zone soil moisture using UAV remote sensing data. The results indicate that the temperature-leaf area index (TLDI) is negatively correlated with soil water content. The upper triangular space performs highly effectively for deep soil moisture inversion, with R2 values ranging from 0.56 to 0.66, RMSE between 0.20 and 0.27, and RPD from 1.25 to 1.50. Conversely, the lower triangular space yields superior results for shallow soil moisture inversion, with R2 values between 0.67 and 0.82, RMSE from 0.15 to 0.19, and RPD between 1.67 and 2.09. The results suggest that the lower triangular space is optimal for shallow soil moisture inversion, while the upper triangular space is more suited for deep soil moisture inversion. This study presents a novel approach for estimating deep soil moisture in orchards, providing a theoretical basis for improving soil moisture management. Keywords: soil moisture, remote sensing, UAV, LST/LAI, two-stage feature space DOI: 10.25165/j.ijabe.20251804.9730 Citation: Zhao L, Lei X C, Ding Y H, Cui N B, Meng D, Shi Y, et al. Estimating soil moisture content in apple orchards using UAV remote sensing data: Application of LST/LAI two-stage feature space theory. Int J Agric & Biol Eng, 2025; 18(4): 239–247.

References

Reich P B, Sendall K M, Stefanski A, Rich R L, Hobbie S E, Montgomery R A. Effects of climate warming on photosynthesis in boreal tree species depend on soil moisture. Nature, 2018; 562: 263–267.

Seneviratne S I, Conti T, Davin E L, Hirschi M, Jaeger E B, Lehner I, et al. Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Science Reviews, 2010; 99(3-4): 125–161.

Araya A, Prasad P V V, Gowda P H, Afewerk A, Abadi B, Foster A J. Modeling irrigation and nitrogen management of wheat in northern Ethiopia. Agricultural Water Management, 2019; 216: 264–272.

Gillies R R, Kustas W P, Humes K S. A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the normalized difference vegetation index (NDVI) and surface radiant temperature. International Journal of Remote Sensing, 1997; 18(15): 3145–3166.

Gómez-del-Campo M, Pérez-Expósito M Á, Hammami S B M, Centeno A, Rapoport H F. Effect of varied summer deficit irrigation on components of olive fruit growth and development. Agricultural Water Managemen, 2014; 137: 84–91.

Njoku E G, Entekhabi D. Passive microwave remote sensing of soil moisture. Journal of Hydrology, 1996; 184(1-2): 101–129.

Sandholt I, Rasmussen K, Andersen J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 2002; 79(2-3): 213–224.

Chen S L, Wen Z M, Jiang H, Zhao Q J, Zhang X Y, Chen Y. Temperature vegetation dryness index estimation of soil moisture under different tree species. Sustainability, 2015; 7(9): 11401–11417.

Sun H, Wang Y M, Liu W H, Yuan S Y, Nie R W. Comparison of three theoretical methods for determining dry and wet edges of the LST/FVC space: Revisit of method physics. Remote Sensing, 2017; 9(6): 528.

Li X Y, Jin H G, Ekiundh L, Bouras E H, Olsson P O, Cai Z Z, et al. Estimation of district-level spring barley yield in southern Sweden using multi-source satellite data and random forest approach. International Journal of Applied Earth Observation and Geoinformation, 2024; 134: 104183.

Zhu Z, Li T S, Cui J, Shi X Y, Chen J H, Wang H J. Non-destructive estimation of winter wheat leaf moisture content using near-ground hyperspectral imaging technology. Acta Agriculturae Scandinavica Section B - Soil & Plant Science, 2020; 70(4): 294–306.

Wong T-T. Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 2015; 48(9): 2839–2846.

Arias M, Notarnicola C, Campo-Bescós M Á, Arregui L M, ÁlvarezMozos J. Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields. Agricultural Water Management, 2023; 287: 108422.

Guo X H, Lei T, Sun X H, Ma J J, Zheng L J, Zhang S W, et al. Modelling soil water dynamics and root water uptake for apple trees under water storage pit irrigation. Int J Agric & Biol Eng, 2019; 12(5): 126–134.

Rudiyanto, Minasny B, Shah R M, Setiawan B I, van Genuchten M T, et al. Simple functions for describing soil water retention and the unsaturated hydraulic conductivity from saturation to complete dryness. Journal of Hydrology, 2020; 588: 125041.

Santos F L M, Rodrigues G, Potes M, Couto F T, Costa M J, Dias S, et al. Moisture content vegetation seasonal variability based on a multiscale remote sensing approach. Remote Sensing, 2024; 16(23): 4434.

Sillero-Medina J A, González-Pérez J, Hueso-González P, GonzálezFernández J J, Hormaza-Urroz J I, Ruiz-Sinoga J D. Effect of different deficit irrigation regimens on soil moisture, production parameters of mango (Mangifera indica L.), and spectral vegetation indices in the mediterranean region of southern spain. Remote Sensing Applications: Society and Environment, 2025; 37: 101415.

Liu Q S, Wu Z J, Cui N B, Zheng S S, Jiang S Z, Wang Z H, et al. Estimating stomatal conductance of citrus orchard based on UAV multimodal information in Southwest China. Agricultural Water Management, 2025; 307: 109253.

Wang Y X, Yan N N, Zhu W W, Ma Z H, Wu B F. A method to estimate the water storage of on-farm reservoirs by detecting slope gradients based on multi-spectral drone data. Agricultural Water Management, 2025; 307: 109241.

Akinaga T, Saito M, Onodera S, Hyodo F. UAV visual imagery-based evaluation of blue carbon as seagrass beds on a tidal flat scale. Remote Sensing Applications: Society and Environment, 2025; 37: 101430.

Turman A M, Sowby R B, Williams G P, Hansen N C. Remote sensing of residential landscape irrigation in weber county, Utah: Implications for water conservation, image analysis, and drone applications. Sustainability, 2024; 16(21): 9356.

Guo F X, Feng Q, Yang S, Yang W X. Estimation of potato canopy leaf water content in various growth stages using UAV hyperspectral remote sensing and machine learning. Frontiers in Plant Science, 2024; 15: 1458589. doi; 10.3389/FPLS. 2024.1458589.

Fathololoumi S, Vaezi A R, Firozjaei M K, Biswas A. Quantifying the effect of surface heterogeneity on soil moisture across regions and surface characteristic. Journal of Hydrology, 2021; 596: 126132.

Mahasneh H. Drones in agriculture: Real-world applications and impactful case studies. Journal of Natural Science Reviews, 2024; 2(2024): 643–656.

Zhao Y, Wang H X, Song B, Xue P F, Zhang W C, Peth S, et al. Characterizing uncertainty in process-based hydraulic modeling, exemplified in a semiarid Inner Mongolia steppe. Geoderma, 2023; 440: 116713.

Hurlimann A C, Moosavi S, Browne G R. Climate change transformation: A definition and typology to guide decision making in urban environments. Sustainable Cities and Society, 2021; 70: 102890.

Zhang R Z, Nayak A, Houtz D, Watts A, Soltanaghai E, Alipour M. Evaluation of soil moisture retrievals from a portable L-band microwave radiometer. Remote Sensing, 2024; 16(23): 4596.

Escandón-Panchana P, Martinez-cuevas S, Jaya-montalvo M, Herrerafranco G. Review of agricultural cadastre approaches using geomatics for rural development. WIT Transactions on Ecology and the Environment, 2024; 262: 465–478.

Šimůnek J, van Genuchten M T. Modeling nonequilibrium flow and transport processes using HYDRUS. Vadose Zone Journal, 2008; 7(2): 782–797.

Zhao H, Wang J J, Guo J L, Hui X, Wang Y L, Cai D Y, et al. Detecting water stress in winter wheat based on multifeature fusion from UAV remote sensing and stacking ensemble learning method. Remote Sensing, 2024; 16(21): 4100.

Zeng Y J, Su Z B, van der Velde R, Wang L C, Xu K, Wang X, et al. Blending satellite observed, model simulated, and in situ measured soil moisture over Tibetan Plateau. Remote Sensing, 2016; 8(3): 268.

Viscarra Rossel R A, Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma, 2010; 158(1-2): 46–54.

Sun H, Zhang L, Rao Z H, Ji H Y. Determination of moisture content in barley seeds based on hyperspectral imaging technology. Spectroscopy Letters, 2020; 53(10): 751–762.

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Published

2025-08-21

How to Cite

Zhao, L., Lei, X., Ding, Y., Cui, N., Meng, D., Shi, Y., … Zhang, X. (2025). Estimating soil moisture content in apple orchards using UAV remote sensing data: Application of LST/LAI two-stage feature space theory. International Journal of Agricultural and Biological Engineering, 18(4), 239–247. https://doi.org/10.25165/ijabe.v18i4.9730

Issue

Vol. 18 No. 4 (2025): IJABE

Section

Information Technology, Sensors and Control Systems

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