Estimating root zone moisture from surface soil using limited data

• Autorzy: Zeng W. Z. , Lei G. Q. , Zhang H. Y. , Hong M. H. , Xu C. , Wu J. W. , Huang J. S.

Identyfikatory

DOI

10.1515/eces-2017-0033

Warianty tytułu

PL

Określanie wilgotności gleby powierzchniowej w strefie korzeniowej na podstawie ograniczonej liczby danych

• Języki publikacji: EN

Abstrakty

EN

For estimation of root-zone moisture content from EO-1/Hyperion imagery, surface soil moisture was first predicted by hyperspectral reflectance data using partial least square regression (PLSR) analysis. The textures of more than 300 soil samples extracted from a 900 m × 900 m field site located within the Hetao Irrigation District in China were used to parameterize the HYDRUS-1D numerical model. The study area was spatially discretized into 18,000 compartments (30 m × 30 m × 0.02 m), and Monte Carlo simulations were applied to generate 2000 different soil-particle size distributions for each compartment. Soil hydraulic properties for each realization were determined by application of artificial neural network analysis and used to parameterize HYDRUS-1D to simulate averaged soil-moisture contents within the root zone (0-40 cm) and surface (approximately 0-4 cm). Then the link between surface moisture and root zone was established by use of linear regression analysis, resulting in R and RMSE of 0.38 and 0.03, respectively. Kriging and co-kriging with observed surface moisture, and co-kriging with surface moisture obtained from Hyperion imagery were also used to estimate root-zone moisture. Results indicated that PLSR is a powerful tool for soil moisture estimation from hyperspectral data. Furthermore, co-kriging with observed surface moisture had the highest R (0.41) and linear regression model, and HYDRUS Monte Carlo simulations had a lowest RMSE (0.03) among the four methods. In regions that have similar climatic and soil conditions to our study area, a linear regression model with HYDRUS Monte Carlo simulations is a practical method for root-zone moisture estimation before sowing and it can be easily coupled with remote sensing technology.

Słowa kluczowe

EN

Monte Carlo simulation; HYDRUS; root zone; soil moisture; kriging; co-kriging

• Wydawca: Towarzystwo Chemii i Inżynierii Ekologicznej
• Czasopismo: Ecological Chemistry and Engineering. S
• Rocznik: 2017
• Tom: Vol. 24, nr 4
• Strony: 501--516
• Opis fizyczny: Bibliogr. 49 poz., rys., wykr., tab.

Twórcy

autor

Zeng W. Z.

• State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, P.R. China

autor

Lei G. Q.

• State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, P.R. China

autor

Zhang H. Y.

• School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, P.R. China

autor

Hong M. H.

• State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, P.R. China

autor

Xu C.

• Changjiang Institute of Survey, Planning, Design and Research, Wuhan, 430010, P.R. China

autor

Wu J. W.

• State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, P.R. China

autor

Huang J. S.

• State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, P.R. China

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).