A multi-scale soil moisture and temperature regularly automatic monitoring network aim at multi-satellite data validation in Tibet Plateau

2015 ◽  
Author(s):  
Lixin Dong ◽  
Shihao Tang
Keyword(s):  
Soil Moisture ◽  
Satellite Data ◽  
Monitoring Network ◽  
Automatic Monitoring ◽  
Tibet Plateau ◽  
Data Validation ◽  
Multi Scale

scholarly journals Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai–Tibet Plateau, China

2021 ◽  
Vol 13 (24) ◽  
pp. 5156
Author(s):  
Jie Wang ◽  
Duanyang Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Satellite Data ◽  
Tibet Plateau ◽  
Precipitation Threshold ◽  
Hidden Layer ◽  
Qinghai Tibet Plateau ◽  
Sample Ratio

Soil moisture is a key parameter for land-atmosphere interaction system; however, fewer existing spatial-temporally continuous and high-quality observation records impose great limitations on the application of soil moisture on long term climate change monitoring and predicting. Therefore, this study selected the Qinghai–Tibet Plateau (QTP) of China as research region, and explored the feasibility of using Artificial Neural Network (ANN) to reconstruct soil moisture product based on AMSR-2/AMSR-E brightness temperature and SMAP satellite data by introducing auxiliary variables, specifically considering the sensitivity of different combination of input variables, number of neurons in hidden layer, sample ratio, and precipitation threshold in model building. The results showed that the ANN model had the highest accuracy when all variables were used as inputs, it had a network containing 12 neurons in a hidden layer, it had a sample ratio 80%-10%-10% (training-validation-testing), and had a precipitation threshold of 8.75 mm, respectively. Furthermore, validation of the reconstructed soil moisture product (named ANN-SM) in other period were conducted by comparing with SMAP (April 2019 to July 2021) for all grid cells and in situ soil moisture sites (August 2010 to March 2015) of QTP, which achieved an ideal accuracy. In general, the proposed method is capable of rebuilding soil moisture products by adopting different satellite data and our soil moisture product is promising for serving the studies of long-term global and regional dynamics in water cycle and climate.

scholarly journals A New Automatic Monitoring Network of Surface Waters in Greece: Preliminary Data Quality Checks and Visualization

Hydrology ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 33
Author(s):  
Yiannis Panagopoulos ◽  
Anna Konstantinidou ◽  
Konstantinos Lazogiannis ◽  
Anastasios Papadopoulos ◽  
Elias Dimitriou
Keyword(s):  
Data Quality ◽  
Surface Waters ◽  
Data Dissemination ◽  
Monitoring Program ◽  
Monitoring Network ◽  
Automatic Monitoring ◽  
Water Monitoring ◽  
Marine Research ◽  
Management Actions ◽  
Quality Checks

The monitoring of surface waters is of fundamental importance for their preservation under good quantitative and qualitative conditions, as it can facilitate the understanding of the actual status of water and indicate suitable management actions. Taking advantage of the experience gained from the coordination of the national water monitoring program in Greece and the available funding from two ongoing infrastructure projects, the Institute of Inland Waters of the Hellenic Centre for Marine Research has developed the first homogeneous real-time network of automatic water monitoring across many Greek rivers. In this paper, its installation and maintenance procedures are presented with emphasis on the data quality checks, based on values range and variability tests, before their online publication and dissemination to end-users. Preliminary analyses revealed that the water pH and dissolved oxygen (DO) sensors and produced data need increased maintenance and quality checks respectively, compared to the more reliably recorded water stage, temperature (T) and electrical conductivity (EC). Moreover, the data dissemination platform and selected data visualization options are demonstrated and the need for both this platform and the monitoring network to be maintained and potentially expanded after the termination of the funding projects is highlighted.

scholarly journals A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

2021 ◽  
Vol 13 (13) ◽  
pp. 2442
Author(s):  
Jichao Lv ◽  
Rui Zhang ◽  
Jinsheng Tu ◽  
Mingjie Liao ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Satellite Data ◽  
Vegetation Index ◽  
Soil Moisture Content ◽  
Support Vector ◽  
Observation Data ◽  
Multipath Effect ◽  
Mars Model ◽  
The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

scholarly journals High-Precision Soil Moisture Mapping Based on Multi-Model Coupling and Background Knowledge, Over Vegetated Areas Using Chinese GF-3 and GF-1 Satellite Data

2020 ◽  
Vol 12 (13) ◽  
pp. 2123 ◽  
Author(s):  
Leran Han ◽  
Chunmei Wang ◽  
Tao Yu ◽  
Xingfa Gu ◽  
Qiyue Liu
Keyword(s):  
Soil Moisture ◽  
High Precision ◽  
Satellite Data ◽  
Solution Method ◽  
Pearson Correlation ◽  
Optimal Solution ◽  
Background Knowledge ◽  
Optical Data ◽  
Inversion Method ◽  
Synthetic Aperture Radar Data

This paper proposes a combined approach comprising a set of methods for the high-precision mapping of soil moisture in a study area located in Jiangsu Province of China, based on the Chinese C-band synthetic aperture radar data of GF-3 and high spatial-resolution optical data of GF-1, in situ experimental datasets and background knowledge. The study was conducted in three stages: First, in the process of eliminating the effect of vegetation canopy, an empirical vegetation water content model and a water cloud model with localized parameters were developed to obtain the bare soil backscattering coefficient. Second, four commonly used models (advanced integral equation model (AIEM), look-up table (LUT) method, Oh model, and the Dubois model) were coupled to acquire nine soil moisture retrieval maps and algorithms. Finally, a simple and effective optimal solution method was proposed to select and combine the nine algorithms based on classification strategies devised using three types of background knowledge. A comprehensive evaluation was carried out on each soil moisture map in terms of the root-mean-square-error (RMSE), Pearson correlation coefficient (PCC), mean absolute error (MAE), and mean bias (bias). The results show that for the nine individual algorithms, the estimated model constructed using the AIEM (mv1) was significantly more accurate than those constructed using the other models (RMSE = 0.0321 cm³/cm³, MAE = 0.0260 cm³/cm³, and PCC = 0.9115), followed by the Oh model (m_v5) and LUT inversion method under HH polarization (mv2). Compared with the independent algorithms, the optimal solution methods have significant advantages; the soil moisture map obtained using the classification strategy based on the percentage content of clay was the most satisfactory (RMSE = 0.0271 cm³/cm³, MAE = 0.0225 cm³/cm³, and PCC = 0.9364). This combined method could not only effectively integrate the optical and radar satellite data but also couple a variety of commonly used inversion models, and at the same time, background knowledge was introduced into the optimal solution method. Thus, we provide a new method for the high-precision mapping of soil moisture in areas with a complex underlying surface.

Filtration and assimilation of soil moisture satellite data

2015 ◽  
Author(s):  
Nikolay N. Bogoslovskiy ◽  
Sergei I. Erin ◽  
Irina A. Borodina ◽  
Lubov I. Kizhner
Keyword(s):  
Soil Moisture ◽  
Satellite Data

A Multiscale Soil Moisture and Freeze–Thaw Monitoring Network on the Third Pole

2013 ◽  
Vol 94 (12) ◽  
pp. 1907-1916 ◽  
Author(s):  
Kun Yang ◽  
Jun Qin ◽  
Long Zhao ◽  
Yingying Chen ◽  
Wenjun Tang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Monitoring Network ◽  
Freeze Thaw ◽  
The Third

scholarly journals The Murrumbidgee soil moisture monitoring network data set

2012 ◽  
Vol 48 (7) ◽  
Author(s):  
A. B. Smith ◽  
J. P. Walker ◽  
A. W. Western ◽  
R. I. Young ◽  
K. M. Ellett ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Monitoring Network ◽  
Network Data ◽  
Data Set ◽  
Soil Moisture Monitoring

scholarly journals Remotely sensed latent heat fluxes for improving model predictions of soil moisture: a case study

2010 ◽  
Vol 7 (4) ◽  
pp. 6179-6205
Author(s):  
J. M. Schuurmans ◽  
F. C. van Geer ◽  
M. F. P. Bierkens
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Latent Heat ◽  
Satellite Data ◽  
Hydrological Model ◽  
Remotely Sensed ◽  
Heat Fluxes ◽  
Model Errors ◽  
Small Catchment

Abstract. This paper investigates whether the use of remotely sensed latent heat fluxes improves the accuracy of spatially-distributed soil moisture predictions by a hydrological model. By using real data we aim to show the potential and limitations in practice. We use (i) satellite data of both ASTER and MODIS for the same two days in the summer of 2006 that, in association with the Surface Energy Balance Algorithm for Land (SEBAL), provides us the spatial distribution of daily ETact and (ii) an operational physically based distributed (25 m×25 m) hydrological model of a small catchment (70 km2) in The Netherlands that simulates the water flow in both the unsaturated and saturated zone. Firstly, model outcomes of ETact are compared to the processed satellite data. Secondly, we perform data assimilation that updates the modelled soil moisture. We show that remotely sensed ETact is useful in hydrological modelling for two reasons. Firstly, in the procedure of model calibration: comparison of modeled and remotely sensed ETact together with the outcomes of our data assimilation procedure points out potential model errors (both conceptual and flux-related). Secondly, assimilation of remotely sensed ETact results in a realistic spatial adjustment of soil moisture, except for the area with forest and deep groundwater levels. As both ASTER and MODIS images were available for the same days, this study provides also an excellent opportunity to compare the worth of these two satellite sources. It is shown that, although ASTER provides much better insight in the spatial distribution of ETact due to its higher spatial resolution than MODIS, they appeared in this study just as useful.

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