Time Series Resistivity Analysis Of Water Content Vatiation In Karst Terrain, Edwards Limestone, San Anstonio, Texas

2007 ◽  
Author(s):  
Marla M. Roberts ◽  
Alan Dutton
Keyword(s):  
Time Series ◽  
Water Content ◽  
Karst Terrain

scholarly journals Effects of Irrigation and Cassava Peel Mulch on Soil Water Content and Pineapple Growth

2013 ◽  
Vol 8 (2) ◽  
pp. 99
Author(s):  
Ali Rahmat ◽  
Afandi ◽  
Tumiar K Manik ◽  
Priyo Cahyono
Keyword(s):  
Time Series ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Cassava Peel

Irigasi pada tanaman nanas sangat penting karena mempengaruhi pertumbuhan dan produksi namun biayanya sangat mahal. Penelitian ini bertujuan untuk mengetahui pengaruh irigasi dan mulsa organik pada kadar air tanah dan pertumbuhan nanas. Penelitian ini dilakukan menggunakan perlakuan faktorial (5 x 2) dalam rancangan acak kelompok dengan tiga ulangan. Faktor pertama adalah panjang waktu irigasi (I), yang terdiri dari 5 waktu yaitu tanpa irigasi (I0), irigasi 1 bulan (I1), irigasi 2 bulan (I2), irigasi 3 bulan (I3), dan irigasi 4 bulan (I4). Faktor kedua adalah dosis kulit singkong (mulsa organik) terdiri dari 2 level 0 ton/ha (M0) dan 50 ton/ha (M1). Kadar air tanah diukur menggunakan Diviner 2000. Data kadar air tanah dianalisis dengan time series. Pertumbuhan tanaman dianalisis keragamannya dan diuji BNT pada taraf 5 %. Hasil penelitian menunjukkan kulit singkong 50 ton/ha pada umumnya hanya bertahan 2,5 bulan untuk mempertahankan kadar air. Mulsa kulit singkong lebih berperan ketika tanah mulai mengering. Pemberian mulsa kulit singkong berpengaruh terhadap tinggi dan berat basah tanaman sedangkan perlakuan, irigasi secara terpisah hanya berpengaruh terhadap berat basah tanaman. Interaksi antara irigasi dan kulit singkong berpengaruh terhadap berat basah tanaman. Meskipun kadar air tanah tersedia cukup saat memasuki musim hujan, namun tidak efektif dalam memulihkan keragaan tanaman nanas. Pemulihan terjadi setelah memasuk musim hujan dimana kadar air tanah tinggi.

Time series outlier and intervention analysis: Irrigation management influences on soil water content in silty loam soil

2012 ◽  
Vol 111 ◽  
pp. 105-114 ◽  
Author(s):  
Basem Aljoumani ◽  
Jose A. Sànchez-Espigares ◽  
Nuria Cañameras ◽  
Ramon Josa ◽  
Joaquim Monserrat
Keyword(s):  
Time Series ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Irrigation Management ◽  
Intervention Analysis ◽  
Silty Loam ◽  
Loam Soil

scholarly journals Use of cosmic ray neutron sensors for soil moisture monitoring in forests

2015 ◽  
Vol 12 (9) ◽  
pp. 9813-9864 ◽  
Author(s):  
I. Heidbüchel ◽  
A. Güntner ◽  
T. Blume
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Cosmic Ray ◽  
Soil Samples ◽  
Calibration Function ◽  
Neutron Sensors ◽  
Hydrogen Mass

Abstract. Cosmic ray neutron sensors (CRS) are a promising technique to measure soil moisture at intermediate scales. To convert neutron counts to average volumetric soil water content a simple calibration function can be used (the N0-calibration of Desilets et al., 2010). This calibration function is based on soil water content derived directly from soil samples taken within the footprint of the sensor. We installed a CRS in a mixed forest in the lowlands of north-eastern Germany and calibrated it 10 times throughout one calendar year. Each calibration with the N0-calibration function resulted in a different CRS soil moisture time series, with deviations of up to 0.12 m3 m-3 for individual values of soil water content. Also, many of the calibration efforts resulted in time series that could not be matched with independent in situ measurements of soil water content. We therefore suggest a new calibration function with a different shape that can vary from one location to another. A two-point calibration proved to be adequate to correctly define the shape of the new calibration function if the calibration points were taken during both dry and wet conditions covering at least 50 % of the total range of soil moisture. The best results were obtained when the soil samples used for calibration were linearly weighted as a function of depth in the soil profile and non-linearly weighted as a function of distance from the CRS, and when the depth-specific amount of soil organic matter and lattice water content was explicitly considered. The annual cycle of tree foliation was found to be a negligible factor for calibration because the variable hydrogen mass in the leaves was small compared to the hydrogen mass changes by soil moisture variations. Finally, we provide a best practice calibration guide for CRS in forested environments.

scholarly journals MONITORING OF TIME-SERIES SOIL MOISTURE BASED ON ADVANCED DINSAR

2021 ◽  
Vol V-3-2021 ◽  
pp. 51-55
Author(s):  
C. H. Yang ◽  
A. Müterthies
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Water Content ◽  
Large Scale ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Organic Soil ◽  
Underground Water ◽  
Multi Temporal ◽  
Mean Square Errors

Abstract. Understanding soil moisture is essential for earth and environmental sciences especially in geology, hydrology, and meteorology. Remote sensing techniques are widely applied to large-scale monitoring tasks. Among them, DInSAR using multi-temporal spaceborne SAR images is able to derive surface movement up to mm level over an area. One of the factors inducing the movement is variation of soil moisture. Based on this, a semi-empirical approach can be tailored to retrieve the underground water content. However, the derived movement is often contaminated with other irrelevant noise. Besides, a time-series analysis could not be simply implemented without additional fusion and calibration. In this paper, we propose a novel modelling based on advanced DInSAR to solve these problems. The irrelevant noise will be removed as parts of the modelled elements in the DInSAR processing. A forward model on a scene is built by regressing the measured soil moisture on the DInSAR-derived movement series. We tested our approach using Sentinel-1 images in the grasslands of organic soil within State of Brandenburg, Germany. The Pearson correlation coefficients between the measured soil moistures and the DInSAR-derived movements are up to 0.91. The mean square errors of the predicted soil moistures compared with the measurements reach 3.03 % (volumetric water content) at best. Our study shows a promising new concept to develop a global monitoring of soil moisture in the future.

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