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Enabling better global research outcomes in soil, plant & environmental monitoring.

SFM1 Sap Flow Meter

Measuring sap flow allows tree and plant water use to be calculated on a daily or seasonal basis, and subsequently informing irrigation choices or species selection. Realtime monitoring of these measurements has not been possible until ICT International developed the SFM1x range of Sap Flow Meters using Internet of Things (IoT).

SFM1x Sap Flow Meter Measures Tree and Plant Water Use with IoT Connectivity

With IoT connectivity, the SFM1x is ideal for scenarios where trees are being monitored off site. The continuous connectivity via IoT allows for simplified data retrieval for analysis.

For decision makers, enabling the regular collection of data on the water use of the tree is crucial to ensure that an appropriate watering and water management strategy can be developed. Furthermore, this will ensure an efficient allocation of water and other resources in the management of the trees.

Addressing the needs of the researcher, the SFM1x is a scientifically robust instrument. The underpinning method, the Heat Ratio Method, has a proven track record in research publications. This acceptance is due to the designed functionality such as the internal SD card that records all the measurements taken, and the ability to store raw temperature data for off-line analysis.

Since 2013, ICT International has worked with wireless communication networks for the transmission of data from their instruments. In 2021 the SFM1x was launched with CAT-M1 or LoRaWAN capabilities for IoT connectivity.

How it does it

The SFM1x Sap Flow Meter uses the Heat Ratio Method, developed by Burgess, Adams, Turner, Beverly, Ong, Khan and Bleby (2001). A pulse of heat is provided by a heater needle located in the sapwood area, with a downstream and upstream needle that are accurately spaced to measure the heat difference over time. This measurement, combined with data on the sapwood area allows for the calculation of the sap flow. Uniquely the SFM1x can measure reverse sap flow as well, through the upstream needle.

This data is available to users in both a calculated water use form, as well as raw temperature data for scientific researchers. With the raw temperature data stored in a JSON format, the data can be imported into analysis packages such as R or Python.

Data Output

With the ability to provide data in common file types, the SFM1x can support researchers or web developers to work with data in a form that is suitable for many varied applications:

  • Comma Seperated Values (CSV) for Excel, R and Python
  • Raw temperature data as JSON

For those using R and Python, scripts are available upon request for the processing of data from the SFM1x. R Scripts are available for basic import here

Communication Technology Options

The SFM1x is an IoT enabled Sap Flow Meter is available in the following versions:

  • SFM1C uses CATM1
  • SFM1L uses LoRaWAN
  • SFM1B uses a dedicated, long range Bluetooth with external antenna

The use of these communication technologies enables the SFM1x to be deployed wherever there is cellular network coverage, or if there is no cellular coverage, a network using LoRaWAN and subsequent internet connection via 4G or satellite backhaul.

All SFM1x versions contain a Bluetooth module with an internal antenna for wireless data access and configuration of the unit. The SFM1B has a dedicated external Bluetooth antenna allowing long range connection.


The Sap Flow Meter (HRM30 and SFM1) has over 400 listings in Google Scholar, highlighting the wide acceptance by the research community. ICT International has an active library of over 150 leading publications that can be accessed here.

The principles behind the SFM1x – the Heat Ratio Method – have been used to measure plant and tree water use in many applications. Example applications of the SFM1 and HRM30 can be found in:

Frequently Asked Questions

  • What is the advantage of the Heat Ratio Method for sap flow measurement?
  • The heat ratio method can measure reverse flow, low flow and high flow. Supported by widespread use in the research community, the calculations that enable these calculations are well established and verified.

  • What does the measurement of sap flow tell us?
  • Sap flow measurement provides a measurement of the transpiration of the plant. This indicates the plant water status, and subsequently the daily water requirements can be calculated.

  • How can sap flow measurements from the SFM1x be integrated into analysis?
  • Data from the SFM1x is stored as CSV or JSON for easy access and analysis. The data from the SFM1x can be viewed online or downloaded for analysis using a number of common tools, as well as direct integration into customer dashboards.

    Data Analysis and Visualisation

    Data from the SFM1x can be downloaded to be analysed in many statistical packages or sent via an API to a data visualisation (dashboard) of choice. Data can be downloaded directly from the SFM1x, or from the cloud when using the SFM1C or SFM1L.


    When data is downloaded from the cloud, subsequent analysis can be performed using statistical packages. Advanced cloud integration solutions, which provide programming and modelling environments for further scientific analysis, machine learning and management decision making applications are available. Please consult ICT International for more details.

    ICT International have integration solutions for the analysis of SFM1x data (CSV or JSON) using:

    • R and R Studio
    • Python
    • ArcGIS/QGIS
    • Various Dashboards (customer supplied)

    Advanced cloud integration programming and modelling environments include:

    • SENAPS
    • Hitachi Vantara

    These provide the ability to undertake all analysis in a cloud environment, removing the need for static processing with downloaded data.

    For the static processing of data, data from the SFM1x can be downloaded and analysed using the proven ICT International Dataview and Sap Flow Tool software. This is ideal for those who are upgrading their SFM1 to an SFM1x.


    Providing real time information of the tree condition and water use, SFM1x data can be integrated into specific tools. These tools can be web based or dedicated apps using the API and JSON feeds. The results are suitable for arborists and the public when visiting an arboretum or tree canopy walk.

    Visualisation solutions include:

    • Geospatial Integration (ArcGIS/QGIS or similar)
    • Various Dashboards (customer supplied)

    Data from the SFM1x can also be downloaded and processed using ICT International Sap Flow Tool software. The software also allows for instant visualising of sap flow in 2- and 3- dimensions.

    The Measurement Principle

    Using the Heat Ratio Method, the SFM1x can measure plant water use when the stem or root diameter is greater than 10mm, including:

    • Low & zero sap flow rates
    • Reverse sap flow rates
    • Night-time water use
    • High flow rates

    Advanced users may choose to use JSON files containing the raw temperature data for subsequent processing and analysis.

    The dynamic reference list maintained by ICT International can be found here.

    Specifications common to all Variants of the SFM1x:


    Output Options Raw Temperatures: °C
    Heat Pulse Velocity: cm hr-1
    Sap Velocity: cm hr-1
    Sap Flow: cm3 hr-1 (Litres hr-1)
    Range -100 to +100 cm hr-1
    Resolution 0.01 cm hr-1
    Accuracy 0.5 cm hr-1
    Measurement Duration 120 seconds
    User Adjustable Heat Pulse 20 Joules (default) approx. Maximum 40 Joules. 20 Joule pulse is equivalent to a 2.5 second heat pulse duration, auto scaling.
    User Adjustable Logging Interval Minimum interval 3 minutes, recommended minimum 10 minutes.


    Needle Diameter 1.3 mm
    Needle Length 35 mm
    Measurement Positions 2 per measurement needle
    Measurement Spacings 7.5 mm and 22.5 mm from the needle tip


    Dimensions L x W X D 170 x 80 x 35 mm
    Weight 400 g


    Temperature Range -10 to 50°C
    R/H Range 0-99%


    Battery Specifications 3300mAh Lithium Ion, 4.20 Volts fully charged
    Fully Charged Battery 4.20 Volts
    Low Power Mode 3.60 Volts – Instrument ceases to take measurements. Resumes measurement at 3.70 Volts
    Discharged Battery 2.90 Volts – Instrument automatically switches off at and below this voltage when no external power connected. Reconnects when battery returns to 3.1 Volts.
    Battery Life At the recommended configuration of 20 Joules at 10-minute intervals, upto 40 hours battery life has been observed without additional power supply. With a recommended power source connected, operation can be continuous.
    Bus Power Source 60mA – 200mA Variable internal charge rate, maximum charge rate of 200mA active when the external voltage rises above 16 Volts DC
    USB Power Source 100mA fixed charge rate
    External Power Requirements
    Bus Power 8-30 Volts DC, non-polarised, current draw is 100mA at 20 volts per SFM
    USB Power 5 Volts DC


    SFM1 – SFM1x Product Variations

    SFM1x Variants SFM1B SFM1C SFM1L
    Communications Short Range Bluetooth Yes Yes Yes
    Long Range Bluetooth Yes
    CAT-M1/LTE-M Yes
    LoRaWAN Yes (L1 = Global, L2 = China)
    Data Output File Types BIN, CSV CSV, JSON BIN, CSV
    Data Storage MicroSD Card
    Memory Capacity Up to 16GB, 8GB MicroSD card included.
    Manufacturer’s Product Codes SFM1x-UB SFM1x-C SFM1x-L1 or -L2
    • CH24 - 24 Volt Power Supply
      The CH24 is a 100 - 240Volts AC Mains to 24Volts DC power supply adapter; capable of outputting up to 2.5Amps. For most ICT Instruments.
    • ICT CIS - Cloud Data Analysis and Display
      The ICT CIS and DataView.
    • SFM-SK1 Installation Kit
      SFM-SK1 Installation kit
    • SFM-DR Dremel 8000
      Dremel 8000 for SFM1 installation
    • DR Dremel 800 Chuck Collet
      This DR Dremel 800 Collet is necessary in order that the small diameter drill bits, as used for the installation of the SFM1 needles, can be inserted into the SFM-DR Dremel Drill Chuck.
    • SFT1 Sap Flow Tool
      Sap Flow Tool software for HFD and HRM. Single License. Unlimited access to any number HRM or HFD datasets. Configured to analyse HRMx, CHPM, Tmax data from the SFM Sap Flow Meter. Visualise PSY1, soil moisture, and meteorological data.
    • SP22 - 20 Watt Solar Panel
      SP22 - 20 Watt Solar Panel with 4m cable suitable for powering our SFM1, PSY1, HFD, SOM1, SMM1 etc products.
    • The HRM Test Block
      The HRM Sap Flow Meter Test Block is a functional verification standard for use with the HRM Sap Flow Meter.
    • Carbon and Water Monitoring
      How can environmental research and monitoring help manage productivity, biodiversity and ecosystem services for a growing population?

    The SFM1 Sap Flow Meter is widely used in research; below is a selected list of over 150 publications that have used the Sap Flow Meter in their research.


    Benyahia, F., Bastos Campos, F., Ben Abdelkader, A., Basile, B., Tagliavini, M., Andreotti, C., & Zanotelli, D. (2023). Assessing Grapevine Water Status by Integrating Vine Transpiration, Leaf Gas Exchanges, Chlorophyll Fluorescence and Sap Flow Measurements. Agronomy, 13(2), 464. https://doi.org/10.3390/agronomy13020464
    Doody, T. M., Gao, S., Vervoort, W., Pritchard, J., Davies, M., Nolan, M., & Nagler, P. L. (2023). A river basin spatial model to quantitively advance understanding of riverine tree response dynamics to water availability and hydrological management. Journal of Environmental Management, 332, 117393. https://doi.org/10.1016/j.jenvman.2023.117393
    Perron, N., Baltzer, J. L., & Sonnentag, O. (2023). Spatial and temporal variation in forest transpiration across a forested boreal peat landscape. Hydrological Processes, n/a(n/a), e14815. https://doi.org/10.1002/hyp.14815


    Asiimwe, G., Jaafar, H., Haidar, M., & Mourad, R. (2022). Soil Moisture or ET-Based Smart Irrigation Scheduling: A Comparison for Sweet Corn with Sap Flow Measurements. Journal of Irrigation and Drainage Engineering, 148(6), 04022017.
    Campos, F. B., Montagnani, L., Benyahai, F., Callesen, T. O., Gonzales, C. V., Tagliavini, M., & Zanotelli, D. (2022). Disentangling the main sources of evapotranspiration in a vineyard. EGU General Assembly 2022. https://doi.org/https://doi.org/10.5194/egusphere-egu22-8231
    Edwards, E. J., Betts, A., Clingeleffer, P. R., & Walker, R. R. (2022). Rootstock-conferred traits affect the water use efficiency of fruit production in Shiraz. Australian Journal of Grape and Wine Research, 28(2), 316–327. https://doi.org/10.1111/ajgw.12553
    Jardine, K. J., Cobello, L. O., Teixeira, L. M., East, M.-M. S., Levine, S., Gimenez, B. O., Robles, E., Spanner, G., Koven, C., Xu, C., Warren, J. M., Higuchi, N., McDowell, N., Pastorello, G., & Chambers, J. Q. (2022). Stem respiration and growth in a central Amazon rainforest. Trees. https://doi.org/10.1007/s00468-022-02265-5
    Lakmali, S., Benyon, R. G., Sheridan, G. J., & Lane, P. N. J. (2022). Change in fire frequency drives a shift in species composition in native Eucalyptus regnans forests: Implications for overstorey forest structure and transpiration. Ecohydrology, 15(3), e2412. https://doi.org/10.1002/eco.2412
    Landgraf, J., Tetzlaff, D., Dubbert, M., Dubbert, D., Smith, A., & Soulsby, C. (2022). Xylem water in riparian Willow trees (Salix alba) reveals shallow sources of root water uptake by in situ monitoring of stable water isotopes. Hydrol. Earth Syst. Sci. Discuss., 26, 2073–2092. https://doi.org/https://doi.org/10.5194/hess-2021-456
    Luo, Y., & Pacheco-Labrador, J. (2022). Evergreen broadleaf greenness and its relationship with leaf flushing, aging, and water fluxes. Agricultural and Forest Meteorology, 323(109060). https://doi.org/https://doi.org/10.1016/j.agrformet.2022.109060.
    Meng, L., Chambers, J., Koven, C., Pastollero, G., Gimenez, B., Jardine, K., Tang, Y., McDowell, N., Negron-Juarez, R., Longo, M., Araujo, A., Tomasella, J., Fontes, C., Mohan, M., & Higuchi, N. (2022). Soil moisture thresholds explain a shift from light-limited to water-limited sap velocity in the Central Amazon during the 2015–16 El Niño drought. Environmental Research Letters, 17.
    Montague, M. S., Landhäusser, S. M., McNickle, G. G., & Jacobs, D. F. (2022). Preferential allocation of carbohydrate reserves belowground supports disturbance-based management of American chestnut (Castanea dentata). Forest Ecology and Management, 509, 120078. https://doi.org/10.1016/j.foreco.2022.120078
    Schoppach, R., Chun, K. P., & Klaus, J. (2022). Accounting for Dbh and Twi in Prediction of Stand-Scale Sap-Flux Density Reduces the Deviation from Measurement (SSRN Scholarly Paper No. 4129815). https://doi.org/10.2139/ssrn.4129815
    Siddiqi, S. A., & Al-Mulla, Y. (2022). Wireless Sensor Network System for Precision Irrigation using Soil and Plant Based Near-Real Time Monitoring Sensors. Procedia Computer Science, 203, 407–412. https://doi.org/10.1016/j.procs.2022.07.053
    Smith-Marin, C. M., Muscarella, R., Ankori-Karlinsky, R., Delzon, S., Farrar, S. L., Salva-Sauri, M., Thompson, J., Zimmerman, J. K., & Uriarte, M. (2022). Hydraulic traits are not robust predictors of tree species stem growth during a severe drought in a wet tropical forest. Functional Ecology, n/a(n/a). https://doi.org/10.1111/1365-2435.14235
    Sun, X., Li, J., Cameron, D., & Moore, G. (2022). On the Use of Sap Flow Measurements to Assess the Water Requirements of Three Australian Native Tree Species. Agronomy, 12(1), 52. https://doi.org/10.3390/agronomy12010052
    Thom, J. K., Fletcher, T. D., Livesley, S. J., Grey, V., & Szota, C. (2022). Supporting Growth and Transpiration of Newly Planted Street Trees With Passive Irrigation Systems. Water Resources Research, 58(1), e2020WR029526. https://doi.org/10.1029/2020WR029526


    Ali, A., Al-Mulla, Y. A., Charabi, Y., Al-Wardy, M., & Al-Rawas, G. (2021). Use of multispectral and thermal satellite imagery to determine crop water requirements using SEBAL, METRIC, and SWAP models in hot and hyper-arid Oman. Arabian Journal of Geosciences, 14(7), 1–21. https://doi.org/10.1007/s12517-021-06948-0
    Antezana-Vera, S. A., & Marenco, R. A. (2021). Transpiration of Swartzia tomentifera in response to microclimatic variability in the central Amazon: the net effect of vapor pressure deficit. CERNE, e-102999. https://cerne.ufla.br/site/index.php/CERNE/article/view/2999
    Augustaitis, A. (2021). Intra-Annual Variation of Stem Circumference of Tree Species Prevailing in Hemi-Boreal Forest on Hourly Scale in Relation to Meteorology, Solar Radiation and Surface Ozone Fluxes. Atmosphere, 12(8), 1017. https://doi.org/10.3390/atmos12081017
    Barron-Gafford, G. A., Knowles, J. F., Sanchez-Cañete, E. P., Minor, R. L., Lee, E., Sutter, L., Tran, N., Murphy, P., Hamerlynck, E. P., Kumar, P., & Scott, R. L. (2021). Hydraulic redistribution buffers climate variability and regulates grass-tree interactions in a semiarid riparian savanna. Ecohydrology, 14(3), e2271. https://doi.org/10.1002/eco.2271
    Black, K. L., Wallace, C. A., & Baltzer, J. L. (2021). Seasonal thaw and landscape position determine foliar functional traits and whole-plant water use in tall shrubs on the low arctic tundra. New Phytologist, 231(1), 94–107. https://doi.org/10.1111/nph.17375
    Coopman, R. E., Nguyen, H. T., Mencuccini, M., Oliveira, R. S., Sack, L., Lovelock, C. E., & Ball, M. C. (2021). Harvesting water from unsaturated atmospheres: deliquescence of salt secreted onto leaf surfaces drives reverse sap flow in a dominant arid climate mangrove, Avicennia marina. New Phytologist, 231(4), 1401–1414. https://doi.org/10.1111/nph.17461
    Espinosa, C. M. O., Salazar, J. C. S., Churio, J. O. R., & Mora, D. S. (2021). Los sistemas agroforestales y la incidencia sobre el estatus hídrico en árboles de cacao. Biotecnología en el Sector Agropecuario y Agroindustrial, 19(1), 256–267. https://doi.org/10.18684/bsaa.v19.n1.2021.1623
    Fabiani, G., Schoppach, R., Penna, D., & Klaus, J. (2021). Transpiration patterns and water use strategies of beech and oak trees along a hillslope. Ecohydrology, n/a(n/a), e2382. https://doi.org/10.1002/eco.2382
    Früchtenicht, E., Bock, J., Feucht, V., & Brüggemann, W. (2021). Reactions of three European oak species (Q. robur, Q. petraea and Q. ilex) to repetitive summer drought in sandy soil. Trees, Forests and People, 5, 100093. https://doi.org/10.1016/j.tfp.2021.100093
    Kannan P., Paramasivan M., Marimuthu S., Swaminathan C., & Bose, J. (2021). Applying both biochar and phosphobacteria enhances Vigna mungo L. growth and yield in acid soils by increasing soil pH, moisture content, microbial growth and P availability. Agriculture, Ecosystems & Environment, 308, 107258. https://doi.org/10.1016/j.agee.2020.107258
    Kim, A. R., Lim, C. H., Lim, B. S., Seol, J., & Lee, C. S. (2021). Phenological Changes of Mongolian Oak Depending on the Micro-Climate Changes Due to Urbanization. Remote Sensing, 13(10), 1890. https://doi.org/10.3390/rs13101890
    Lee, E., Kumar, P., Knowles, J. F., Minor, R. L., Tran, N., Barron-Gafford, G. A., & Scott, R. L. (2021). Convergent hydraulic redistribution and groundwater access supported facilitative dependency between trees and grasses in a semi-arid environment. Water Resources Research, 57(6), e2020WR028103.
    Liu, Y., Nadezhdina, N., Di, N., Ma, X., Liu, J., Zou, S., Xi, B., & Clothier, B. (2021). An undiscovered facet of hydraulic redistribution driven by evaporation—a study from a Populus tomentosa plantation. Plant Physiology, 186(1), 361–372. https://doi.org/10.1093/plphys/kiab036
    Liu, Z., Liu, Q., Wei, Z., Yu, X., Jia, G., & Jiang, J. (2021). Partitioning tree water usage into storage and transpiration in a mixed forest. Forest Ecosystems, 8(1), 72. https://doi.org/10.1186/s40663-021-00353-5
    Matsunaga, H., Matsuo, N., Nakai, T., Yoshifuji, N., Tanaka, N., Tanaka, K., & Tantasirin, C. (2021). Absorption and emission of water vapor from the bark of teak (Tectona grandis), a deciduous tree, in a tropical region during the dry season. Hydrological Research Letters, 15(3), 58–63. https://doi.org/10.3178/hrl.15.58
    Miranda, M. T., Da Silva, S. F., Silveira, N. M., Pereira, L., Machado, E. C., & Ribeiro, R. V. (2021). Root Osmotic Adjustment and Stomatal Control of Leaf Gas Exchange are Dependent on Citrus Rootstocks Under Water Deficit. Journal of Plant Growth Regulation, 40(1), 11–19. https://doi.org/10.1007/s00344-020-10069-5
    Molina, A. J., González-Sanchis, M., Biel, C., & del Campo, A. D. (2021). Ecohydrological turnover in overstocked Aleppo pine plantations: Does the effect of thinning, in relation to water, persist at the mid-term? Forest Ecology and Management, 483, 118781. https://doi.org/10.1016/j.foreco.2020.118781
    Santi, L., Ardiyanto, A., Kurniawan, A., Prabowo, L. A., & Sebastian, I. (2021). Improvement of water and nutrient efficiencies oil palm through bio-silicic acid application. Menara Perkebunan, 89(1), 26–36. http://mp.iribb.org/index.php/mpjurnal/article/view/409#:~:text=The%20results%20indicated%20that%20the%20application%20of%2075-100%25,%28mature%29%20and%2050.4%25%20%28immature%29%20of%20the%20control%20treatment.
    Schoppach, R., Chun, K. P., He, Q., Fabiani, G., & Klaus, J. (2021). Species-specific control of DBH and landscape characteristics on tree-to-tree variability of sap velocity. Agricultural and Forest Meteorology, 307, 108533. https://doi.org/10.1016/j.agrformet.2021.108533
    Sidabrienė, D. (2021). TRANSPIRATION RATE OF SCOTS PINE TREES ON VERY OLIGOTROPHIC SOILS OF NORMAL MOISTURE IN RELATION TO DIFFERENT METEOROLOGICAL CONDITION. Proceedings of the International Scientific Conference “Rural Development,” 278–284. https://doi.org/10.15544/RD.2021.049
    Siddiqi, S. A., Al-Mulla, Y. A., McCann, I., AbuRumman, G., Belhaj, M., Zekri, S., Al-Ismaili, A., & Rahman, S. (2021). Smart Monitoring, Sap-Flow, Stem-Psychrometer And Soil-Moisture Measurements Tools For Precision Irrigation And Water Saving Of Date Palm. International Journal of Agriculture and Biology, 26(5), 570–578.
    Smith, A., Tetzlaff, D., Landgraf, J., Dubbert, M., & Soulsby, C. (2021). Modelling temporal variability of <em>in-situ</em> soil water and vegetation isotopes reveals ecohydrological couplings in a willow plot. Biogeosciences Discussions, 1–28. https://doi.org/10.5194/bg-2021-278
    Suárez, J. C., Casanoves, F., Bieng, M. A. N., Melgarejo, L. M., Di Rienzo, J. A., & Armas, C. (2021). Prediction model for sap flow in cacao trees under different radiation intensities in the western Colombian Amazon. Scientific Reports, 11(1), 10512. https://doi.org/10.1038/s41598-021-89876-z
    Treydte, K., Lehmann, M. M., Wyczesany, T., & Pfautsch, S. (2021). Radial and axial water movement in adult trees recorded by stable isotope tracing. Tree Physiology, 41(12), 2248–2261. https://doi.org/10.1093/treephys/tpab080
    Wallace, T. A., Gehrig, S. L., Doody, T. M., Davies, M. J., Walsh, R., Fulton, C., Cullen, R., & Nolan, M. (2021). A multiple-lines-of-evidence approach for prioritising environmental watering of wetland and floodplain trees. Ecohydrology, 14(3), e2272. https://doi.org/https://doi.org/10.1002/eco.2272
    Western, A. W., Arora, M., Burns, M. J., Bonneau, J., Thom, J. K., Yong, C. F., James, R. B., Poelsma, P. J., & Fletcher, T. D. (2021). Impacts of stormwater infiltration on downslope soil moisture and tree water use. Environmental Research Letters, 16(10), 104014. https://doi.org/10.1088/1748-9326/ac1c2a


    Kangur, O., Tullus, A., & Sellin, A. (2020). Night-time transpiration, predawn hydraulic conductance and water potential disequilibrium in hybrid aspen coppice. Trees, 34(1), 133–141. https://doi.org/10.1007/s00468-019-01903-9
    Luo, Z., Deng, Z., Singha, K., Zhang, X., Liu, N., Zhou, Y., He, X., & Guan, H. (2020). Temporal and spatial variation in water content within living tree stems determined by electrical resistivity tomography. Agricultural and Forest Meteorology, 291, 108058. https://doi.org/10.1016/j.agrformet.2020.108058
    Magh, R.-K., Eiferle, C., Burzlaff, T., Dannenmann, M., Rennenberg, H., & Dubbert, M. (2020). Competition for water rather than facilitation in mixed beech-fir forests after drying-wetting cycle. Journal of Hydrology, 587, 124944. https://doi.org/10.1016/j.jhydrol.2020.124944
    Marchionni, V., Daly, E., Manoli, G., Tapper, N. J., Walker, J. P., & Fatichi, S. (2020). Groundwater Buffers Drought Effects and Climate Variability in Urban Reserves. Water Resources Research, 56(5), e2019WR026192. https://doi.org/10.1029/2019WR026192
    Merlin, M., Solarik, K. A., & Landhäusser, S. M. (2020). Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree. Agricultural and Forest Meteorology, 287, 107926. https://doi.org/10.1016/j.agrformet.2020.107926
    Rogers, C. A., Chen, J. M., Zheng, T., Croft, H., Gonsamo, A., Luo, X., & Staebler, R. M. (2020). The Response of Spectral Vegetation Indices and Solar-Induced Fluorescence to Changes in Illumination Intensity and Geometry in the Days Surrounding the 2017 North American Solar Eclipse. Journal of Geophysical Research: Biogeosciences, 125(10), e2020JG005774. https://doi.org/10.1029/2020JG005774
    Takeuchi, S., Shinozaki, K., Matsushima, D., & Iida, S. (2020). Calibration of the heat ratio method by direct measurements of transpiration with the weighing root-ball method for Michelia figo. Acta Horticulturae, 21–28. https://doi.org/10.17660/ActaHortic.2020.1300.4
    Thom, J. K., Szota, C., Coutts, A. M., Fletcher, T. D., & Livesley, S. J. (2020). Transpiration by established trees could increase the efficiency of stormwater control measures. Water Research, 173, 115597. https://doi.org/10.1016/j.watres.2020.115597
    Thomsen, S., Reisdorff, C., Gröngröft, A., Jensen, K., & Eschenbach, A. (2020). Responsiveness of mature oak trees (Quercus robur L.) to soil water dynamics and meteorological constraints in urban environments. Urban Ecosystems, 23(1), 173–186. https://doi.org/10.1007/s11252-019-00908-z
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    2013 and earlier