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.

Development of calibration algorithms for selected water content reflectometry probes for burned and non-burned organic soils of Alaska

2010 ◽  
Vol 19 (7) ◽  
pp. 961 ◽  
Author(s):  
Laura L. Bourgeau-Chavez ◽  
Gordon C. Garwood ◽  
Kevin Riordan ◽  
Benjamin W. Koziol ◽  
James Slawski
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Water Content ◽  
Soil Profile ◽  
Soil Moisture Content ◽  
Mineral Soil ◽  
Organic Soil ◽  
Organic Content ◽  
Organic Soils ◽  
Probe Type

Water content reflectometry is a method used by many commercial manufacturers of affordable sensors to electronically estimate soil moisture content. Field‐deployable and handheld water content reflectometry probes were used in a variety of organic soil‐profile types in Alaska. These probes were calibrated using 65 organic soil samples harvested from these burned and unburned, primarily moss‐dominated sites in the boreal forest. Probe output was compared with gravimetrically measured volumetric moisture content, to produce calibration algorithms for surface‐down‐inserted handheld probes in specific soil‐profile types, as well as field‐deployable horizontally inserted probes in specific organic soil horizons. General organic algorithms for each probe type were also developed. Calibrations are statistically compared to determine their suitability. The resulting calibrations showed good agreement with in situ validation and varied from the default mineral‐soil‐based calibrations by 20% or more. These results are of particular interest to researchers measuring soil moisture content with water content reflectometry probes in soils with high organic content.

scholarly journals Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

2019 ◽  
Vol 11 (7) ◽  
pp. 820 ◽  
Author(s):  
Haifeng Tian ◽  
Ni Huang ◽  
Zheng Niu ◽  
Yuchu Qin ◽  
Jie Pei ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Time Windows ◽  
Vegetation Indices ◽  
Optical Images ◽  
Landsat 7 ◽  
Multi Temporal ◽  
Winter Crop ◽  
Winter Crops ◽  
Sentinel 2

Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses.

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 Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

2011 ◽  
Vol 8 (6) ◽  
pp. 10333-10367
Author(s):  
J. Van doninck ◽  
J. Peters ◽  
H. Lievens ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Change Detection ◽  
Incidence Angle ◽  
Arable Land ◽  
Correlation Coefficients ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Second Phase ◽  
Wide Swath

Abstract. A change detection algorithm is applied on a three year time series of ASAR Wide Swath images in VV polarization over Calabria, Italy, in order to derive information on temporal soil moisture dynamics. The algorithm, adapted from an algorithm originally developed for ERS Scatterometer, was validated using a simple hydrological model incorporating meteorological and pedological data. Strong positive correlations between modelled soil moisture and ASAR soil moisture were observed over arable land, while the correlation became much weaker over more vegetated areas. In a second phase, an attempt was made to incorporate seasonality in the different model parameters. It was observed that seasonally changing vegetation and soil moisture mainly affected the multitemporal incidence angle normalization. When applying a seasonal angular normalization, correlation coefficients between modelled soil moisture and retrieved soil moisture increased overall. Attempts to account for seasonality in the other model parameters did not result in an improved performance.

scholarly journals Mapping Maize Cultivated Area Combining MODIS EVI Time Series and the Spatial Variations of Phenology over Huanghuaihai Plain

2020 ◽  
Vol 10 (8) ◽  
pp. 2667 ◽  
Author(s):  
Xueting Wang ◽  
Sha Zhang ◽  
Lili Feng ◽  
Jiahua Zhang ◽  
Fan Deng
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Regression Equations ◽  
Summer Maize ◽  
Temporal Shift ◽  
Modis Evi ◽  
Multi Temporal ◽  
Moderate Resolution Imaging Spectroradiometer

Crop phenology is a significant factor that affects the precision of crop area extraction by using the multi-temporal vegetation indices (VIs) approach. Considering the phenological differences of maize among the different regions, the summer maize cultivated area was estimated by using enhanced vegetation index (EVI) time series images from the Moderate Resolution Imaging Spectroradiometer (MODIS) over the Huanghuaihai Plain in China. By analyzing the temporal shift in summer maize calendars, linear regression equations for simulating the summer maize phenology were obtained. The simulated maize phenology was used to correct the MODIS EVI time series curve of summer maize. Combining the mean absolute distance (MAD) and p-tile algorithm, the cultivated areas of summer maize were distinguished over the Hunaghuaihai Plain. The accuracy of the extraction results in each province was above 85%. Comparing the maize area of two groups from MODIS-estimated and statistical data, the validation results showed that the R2 reached 0.81 at the city level and 0.69 at the county level. It demonstrated that the approach in this study has the ability to effectively map the summer maize area over a large scale and provides a novel idea for estimating the planting area of other crops.

scholarly journals Close co-variation between soil moisture and runoff emerging from multi-catchment data across Europe

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Navid Ghajarnia ◽  
Zahra Kalantari ◽  
René Orth ◽  
Georgia Destouni
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Large Scale ◽  
Temperature Anomaly ◽  
Important Variable ◽  
Climatic Variables ◽  
New Approach ◽  
European Climate ◽  
Moisture Conditions

AbstractSoil moisture is an important variable for land-climate and hydrological interactions. To investigate emergent large-scale, long-term interactions between soil moisture and other key hydro-climatic variables (precipitation, actual evapotranspiration, runoff, temperature), we analyze monthly values and anomalies of these variables in 1378 hydrological catchments across Europe over the period 1980–2010. The study distinguishes results for the main European climate regions, and tests how sensitive or robust they are to the use of three alternative observational and re-analysis datasets. Robustly across the European climates and datasets, monthly soil moisture anomalies correlate well with runoff anomalies, and extreme soil moisture and runoff values also largely co-occur. For precipitation, evapotranspiration, and temperature, anomaly correlation and extreme value co-occurrence with soil moisture are overall lower than for runoff. The runoff results indicate a possible new approach to assessing variability and change of large-scale soil moisture conditions by use of long-term time series of monitored catchment-integrating stream discharges.

Accuracy analysis of IMERG and CMORPH precipitation data over North China

2020 ◽  
Vol 81 ◽  
pp. 55-70
Author(s):  
L Shen ◽  
R Lin ◽  
L Lu ◽  
C Xu ◽  
Y Liu
Keyword(s):  
Large Scale ◽  
North China ◽  
Correlation Coefficients ◽  
Coastal Areas ◽  
Probability Of Detection ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Daily Precipitation Data ◽  
Mean Square Errors ◽  
Rainy Days

Large-scale agricultural production in North China makes the study of precipitation in this area vital. The performance of the Integrated Merged Multisatellite Retrievals for the Global Precipitation Measurement (IMERG) and the Climate Prediction Center morphing technique (CMORPH) precipitation products for 2015 was evaluated against daily precipitation data from 404 rain gauges in North China. Relative errors, correlation coefficients, Pearson’s chi-squared test values, and root mean square errors, as well as the probability of detection (POD), false alarm ratio, and critical success index, were used to analyze the accuracy of both IMERG and CMORPH precipitation products on daily, monthly, and seasonal timescales. The probability density function (PDF) was also considered. Overall, both products overestimated ground precipitation, especially in summer. Positive correlation coefficients between satellite-derived and rain-gauge monthly precipitation data were higher over plains and coastal areas, compared with plateau regions. The PODs of both IMERG and CMORPH data were highest in summer. The PODs of IMERG data were much higher than for CMORPH data in autumn. The PODs over coastal regions, plains, and plateaus at lower latitudes also were considerably better than over inland and plateau areas at higher latitudes. The precipitation products performed best over coastal areas, plains, and areas with high rainfall. Both CMORPH and IMERG products were prone to identifying non-rainy days as rainy days. They also overestimated light (0.1-9.9 mm d-1) and moderate (10-24.9 mm d-1) precipitation events, although the IMERG product was more sensitive to precipitation. Accordingly, we find that both of these satellite-derived precipitation products require further modification to enable them to substitute for gauge precipitation data in North China.

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

2016 ◽  
Vol 20 (3) ◽  
pp. 1269-1288 ◽  
Author(s):  
Ingo Heidbüchel ◽  
Andreas Güntner ◽  
Theresa Blume
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Cosmic Ray ◽  
Soil Samples ◽  
Calibration Function ◽  
Total Range ◽  
Hydrogen Mass

Abstract. Measuring soil moisture with cosmic-ray neutrons is a promising technique for intermediate spatial 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). The calibration is based on soil water content derived directly from soil samples taken within the footprint of the sensor. We installed a cosmic-ray neutron sensor (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 0.1 m3 m−3 (24 % of the total range) 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 modified calibration function with a different shape that can vary from one location to another. A two-point calibration was found to effectively define the shape of the modified calibration function if the calibration points were taken during both dry and wet conditions spanning at least half 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 nonlinearly 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. As a final point, we provide a calibration guide for a CRS in forested environments.

scholarly journals Mapping Weather-Type Influence on Senegal Precipitation Based on a Spatial–Temporal Statistical Model*

2013 ◽  
Vol 26 (20) ◽  
pp. 8189-8209 ◽  
Author(s):  
Henning W. Rust ◽  
Mathieu Vrac ◽  
Benjamin Sultan ◽  
Matthieu Lengaigne
Keyword(s):  
Atmospheric Circulation ◽  
Large Scale ◽  
Pearson Correlation ◽  
Precipitation Amount ◽  
Correlation Coefficients ◽  
Drought Risk ◽  
Synoptic Scale ◽  
Weather Types ◽  
Easterly Waves ◽  
Precipitation Occurrence

Abstract Senegal is particularly vulnerable to precipitation variability. To investigate the influence of large-scale circulation on local-scale precipitation, a full spatial–statistical description of precipitation occurrence and amount for Senegal is developed. These regression-type models have been built on the basis of daily records at 137 locations and were developed in two stages: (i) a baseline model describing the expected daily occurrence probability and precipitation amount as spatial fields from monsoon onset to offset, and (ii) the inclusion of weather types defined from the NCEP–NCAR reanalysis 850-hPa winds and 925-hPa relative humidity establishing the link to the synoptic-scale atmospheric circulation. During peak phase, the resulting types appear in two main cycles that can be linked to passing African easterly waves. The models allow the investigation of the spatial response of precipitation occurrence and amount to a discrete set of preferred states of the atmospheric circulation. As such, they can be used for drought risk mapping and the downscaling of climate change projections. Necessary choices, such as filtering and scaling of the atmospheric data (as well as the number of weather types to be used), have been made on the basis of the precipitation models' performance instead of relying on external criteria. It could be demonstrated that the inclusion of the synoptic-scale weather types lead to skill on the local and daily scale. On the interannual scale, the models for precipitation occurrence and amount capture 26% and 38% of the interannual spatially averaged variability, corresponding to Pearson correlation coefficients of rO = 0.52 and ri = 0.65, respectively.

scholarly journals An Empirical Model for Estimating Soil Thermal Conductivity from Soil Water Content and Porosity

2016 ◽  
Vol 17 (2) ◽  
pp. 601-613 ◽  
Author(s):  
Bing Tong ◽  
Zhiqiu Gao ◽  
Robert Horton ◽  
Yubin Li ◽  
Linlin Wang
Keyword(s):  
Thermal Conductivity ◽  
Water Content ◽  
Empirical Model ◽  
Large Scale ◽  
Soil Heat Flux ◽  
Soil Thermal Conductivity ◽  
New Model ◽  
Scale Models ◽  
Complex Models ◽  
Mean Square Errors

Abstract Soil thermal conductivity λ is a vital parameter for soil temperature and soil heat flux forecasting in hydrological models. In this study, an empirical model is developed to relate λ only to soil volumetric water content θ and soil porosity θs. Measured λ values for eight soils are used to establish the empirical model, and data from four other soils are used to evaluate the model. The new model is also evaluated by its performance in the Simple Biosphere Model 2 (SiB2). Results show that the root-mean-square errors (RMSEs; ranging from 0.097 to 0.266 W m−1 K−1) of the new model estimates of λ are lower than those (ranging from 0.416 to 1.006 W m−1 K−1) for an empirical model of similar complexity reported in the literature earlier. Further, with simple inputs and equations, the new model almost has the accuracy of other more complex models (RMSE of λ ranging from 0.040 to 0.354 W m−1 K−1) that require additional detailed soil information. The new model can be readily incorporated in large-scale models because of its simplicity as compared to the more complex models. The new model is tested for its effectiveness by incorporating it into SiB2. Compared to the original SiB2 λ model, the new λ model provides better estimates of surface effective radiative temperature and soil wetness. Owing to the newly presented empirical model’s requirement for simple, available inputs and its accuracy, its usage is recommended within large-scale models for applications where detailed information about soil composition is lacking.

Sign in / Sign up

Export Citation Format

Share Document