scholarly journals Evaluation of Satellite-Derived Soil Moisture in Qinghai Province Based on Triple Collocation

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1292
Author(s):  
Hongchun Zhu ◽  
Zhilin Zhang ◽  
Aifeng Lv
Keyword(s):  
Soil Moisture ◽  
Correlation Coefficient ◽  
Microwave Radiation ◽  
Satellite Data ◽  
Measurement Data ◽  
Ground Truth ◽  
Error Variance ◽  
Vegetation Coverage ◽  
Qinghai Province ◽  
Ground Measurement

Evaluating the reliability of satellite-based and reanalysis soil moisture products is very important in soil moisture research. The traditional methods of evaluating soil moisture products rely on the verification of satellite inversion data and ground observation; however, the ground measurement data is often difficult to obtain. The triple collocation (TC) method can be used to evaluate the accuracy of a product without obtaining the ground measurement data. This study focused on the whole of Qinghai Province, China (31°–40° N, 89°–103° E), and used the TC method to obtain the error variance for satellite-based soil moisture data, the signal-to-noise ratio (SNR) of the same data, and the correlation between the same data and the ground-truth soil moisture, using passive satellite products: Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity (SMOS), Fengyun-3B Microwave Radiation Imager (FY3B), Fengyun-3C Microwave Radiation Imager (FY3C), and Advanced Microwave Scanning Radiometer 2 (AMSR2); an active satellite product Advanced Scatterometer (ASCAT), and reanalysis data Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system. The TC results for the passive satellite data were then compared with the satellite-derived enhanced vegetation index (EVI) to explore the influence of vegetation coverage on the results. The following conclusions are drawn: (1) for the SMAP, SMOS, FY3B, FY3C, and AMSR2 satellite data, the spatial distributions of the TC-derived error variance, the SNR of the satellite-derived soil moisture, and the correlation coefficient between the satellite-derived and ground-truth soil moisture, were all relatively similar, which indirectly verified the reliability of the TC method; and (2) SMOS data have poor applicability for the estimation of soil moisture in Qinghai Province due to their insufficient detection capability in the Qaidam area, high error variance (median 0.0053), high SNR (median 0.43), and low correlation coefficient with ground-truth soil moisture (median 0.57).

scholarly journals A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province

2021 ◽  
Vol 13 (8) ◽  
pp. 1583
Author(s):  
Aifeng Lv ◽  
Zhilin Zhang ◽  
Hongchun Zhu
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Input Data ◽  
High Spatial Resolution ◽  
Error Variance ◽  
Statistical Relationship ◽  
Qinghai Province ◽  
Soil Moisture Data ◽  
Microwave Data

Currently, soil-moisture data extracted from microwave data suffer from poor spatial resolution. To overcome this problem, this study proposes a method to downscale the soil moisture spatial resolution. The proposed method establishes a statistical relationship between low-spatial-resolution input data and soil-moisture data from a land-surface model based on a neural network (NN). This statistical relationship is then applied to high-spatial-resolution input data to obtain high-spatial-resolution soil-moisture data. The input data include passive microwave data (SMAP, AMSR2), active microwave data (ASCAT), MODIS data, and terrain data. The target soil moisture data were collected from CLDAS dataset. The results show that the addition of data such as the land-surface temperature (LST), the normalized difference vegetation index (NDVI), the normalized shortwave-infrared difference bare soil moisture indices (NSDSI), the digital elevation model (DEM), and calculated slope data (SLOPE) to active and passive microwave data improves the retrieval accuracy of the model. Taking the CLDAS soil moisture data as a benchmark, the spatial correlation increases from 0.597 to 0.669, the temporal correlation increases from 0.401 to 0.475, the root mean square error decreases from 0.051 to 0.046, and the mean absolute error decreases from 0.041 to 0.036. Triple collocation was applied in the form of [NN, FY3C, GEOS-5] based on the extracted retrieved soil-moisture data to obtain the error variance and correlation coefficient between each product and the actual soil-moisture data. Therefore, we conclude that NN data, which have the lowest error variance (0.00003) and the highest correlation coefficient (0.811), are the most applicable to Qinghai Province. The high-spatial-resolution data obtained from the NN, CLDAS data, SMAP data, and AMSR2 data were correlated with the ground-station data respectively, and the result of better NN data quality was obtained. This analysis demonstrates that the NN-based method is a promising approach for obtaining high-spatial-resolution soil-moisture data.

scholarly journals ANALYSIS OF WHEAT CROP FORECASTS, IN INDIA, GENERATED USING REMOTE SENSING DATA, UNDER FASAL PROJECT

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 223-228
Author(s):  
S. Kumar ◽  
S. Saxena ◽  
S. K. Dubey ◽  
K. Chaudhary ◽  
S. Sehgal ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Spatial Resolution ◽  
Satellite Data ◽  
National Level ◽  
Remote Sensing Data ◽  
State Level ◽  
Ground Truth ◽  
District Level ◽  
Wheat Crop ◽  
Ground Truth Data

<p><strong>Abstract.</strong> Wheat (<i>Triticum aestivum</i> L.) is a major cereal crop of the world, which plays an important role in global food and nutritional security. In India, wheat grown areas are more as compared to other food crops, except for rice. The total area under wheat cultivation is 30.60 million hectares with production of 98.38 million tonnes and the productivity is 3.22 tonnes /hectare (DES, 2017). The main objective of this paper is to highlight the development of satellite-based methodology, compare the relative deviations (%) at national level, RMSE (%) and correlation coefficient at state level and correlation coefficient at district level between DES and FASAL estimates from 2013 to 2017. It was observed that the area and production estimates improved with improvement in the satellite resolution and ground truth data. During the last 10 years of estimation the spatial resolution of the satellite data has gradually improved from 23.5 meter of (Reourcesat-2, LISS-III) and finally 10&amp;thinsp;m of Sentinel-2, MSI, which is being currently used for acreage estimation purpose. Hooda R.S et al (2006) studied that the improvement in the spatial resolution, spectral and temporal resolution of the satellite data has also improved the crop discrimination. Both accuracy as well as precision of the estimates has improved over the years from 2013 to 2017, as reflected by relative deviation, RMSE (%) and Coefficient of correlation values at national, state and district level respectively.</p>

scholarly journals A METHOD OF MICROWAVE SOIL MOISTURE INVERSION WITHOUT DEPENDENCE ON THE FIELD MEASUREMENT DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 459-465
Author(s):  
L. Han ◽  
L. Chen ◽  
Y. Zhang ◽  
X. Qin
Keyword(s):  
Surface Roughness ◽  
Soil Moisture ◽  
Measurement Data ◽  
Measured Data ◽  
Vegetation Coverage ◽  
Inversion Method ◽  
Sampling Point ◽  
Effective Roughness ◽  
Empirical Function ◽  
Soil Moisture Inversion

In the process of retrieving soil moisture (<i>M<sub>v</sub></i>) by active microwave, surface roughness is an important parameter affecting the accuracy of <i>m<sub>v</sub></i> retrieval.Using effective roughness to replace the original measurement value of surface roughness can effectively avoid the error. In the existing methods of soil water inversion, the value of fixed mean square root height (<i>S</i>) was used to retrieve the surface of different types and vegetation coverage, neglecting the difference of different ground surface. This paper proposed a LUT (look up table) soil moisture inversion method based on the pixel scale effective roughness. First, the effective roughness was obtained by using the measured soil moisture value of the sampling point based on the LUT method. And then the empirical function between the optimum roughness (<i>S</i>, Correlation length-<i>l</i>) and the backscattering coefficient of VV/HH polarization was obtained. The value of each pixel's <i>S</i> and <i>L</i> was obtained by using the empirical function. Finally, the soil moisture was retrieved by the LUT method. Using the measured data of the Linze sample area to verify, the results showed that the proposed method was superior to the LUT inversion method using the <i>S</i> fixed value without dependence on the measured data of the roughness. It was also proved that the inversion method proposed in this paper is not only applicable to the bare soil area, but still maintained a high precision in the soil moisture inversion results in the area with large vegetation coverage.

scholarly journals Extraction and Discrimination of MBT Anomalies Possibly Associated with the Mw 7.3 Maduo (Qinghai, China) Earthquake on 21 May 2021

2021 ◽  
Vol 13 (22) ◽  
pp. 4726
Author(s):  
Yuan Qi ◽  
Lixin Wu ◽  
Yifan Ding ◽  
Yingjia Liu ◽  
Shuai Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Microwave Radiation ◽  
Joint Effect ◽  
Influential Factor ◽  
Shielding Effect ◽  
Human Beings ◽  
Qinghai Province ◽  
Detailed Technique ◽  
Bayan Har

Earthquakes are one of the most threatening natural disasters to human beings, and pre- and post-earthquake microwave brightness temperature (MBT) anomalies have attracted increasing attention from geosciences as well as remote sensing communities. However, there is still a lack of systematic description about how to extract and then discriminate the authenticity of seismic MBT anomalies. In this research, the first strong earthquake occurring near the northern edge of eastern Bayan Har block in nearly 20 years, the recent Mw 7.3 Maduo earthquake in Qinghai province, China on 21 May 2021, was selected as a case study. Based on the monthly mean background of MBT, the spatiotemporal features of MBT residuals with 10.65 GHz before and after the earthquake was firstly revealed. Referring to the spatial patterns and abnormal amplitudes of the results, four typical types of evident MBT positive residuals were obtained, and the time series of intensity features of each category was also quantitatively analyzed. Then, as the most influential factor on surface microwave radiation, air temperature, soil moisture and precipitation were analyzed to discriminate their contributions to these residuals. The fourth one, which occurred north to the epicenter after the earthquake, was finally confirmed to be caused by soil moisture reduction and thus ruled out as being related to seismicity. Therefore, the three retained typical MBT residuals with 10.65 GHz could be identified as possible anomalies associated with the Maduo earthquake, and were further analyzed collaboratively with some other reported abnormal phenomena related to the seismogenic process. Furthermore, through time series analysis, the MBT positive residuals inside the Bayan Har block were found to be more significant than that outside, and the abnormal behaviors of MBT residuals in the elevation range of 4000–5000 m reflected the shielding effect on microwave radiation from thawing permafrost on the plateau in March and April, 2021. This research provides a detailed technique to extract and discriminate the seismic MBT anomaly, and the revealed results reflect well the joint effect of seismic activity and regional coversphere environment on satellite-observed MBT.

scholarly journals Field-Scale Soil Moisture Retrieval Using PALSAR-2 Polarimetric Decomposition and Machine Learning

Agronomy ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 35
Author(s):  
Xiaodong Huang ◽  
Beth Ziniti ◽  
Michael H. Cosh ◽  
Michele Reba ◽  
Jinfei Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Canopy Cover ◽  
The United States ◽  
Optical Data ◽  
Study Region ◽  
Model Based ◽  
Key Indicator ◽  
Ground Measurement ◽  
L Band

Soil moisture is a key indicator to assess cropland drought and irrigation status as well as forecast production. Compared with the optical data which are obscured by the crop canopy cover, the Synthetic Aperture Radar (SAR) is an efficient tool to detect the surface soil moisture under the vegetation cover due to its strong penetration capability. This paper studies the soil moisture retrieval using the L-band polarimetric Phased Array-type L-band SAR 2 (PALSAR-2) data acquired over the study region in Arkansas in the United States. Both two-component model-based decomposition (SAR data alone) and machine learning (SAR + optical indices) methods are tested and compared in this paper. Validation using independent ground measurement shows that the both methods achieved a Root Mean Square Error (RMSE) of less than 10 (vol.%), while the machine learning methods outperform the model-based decomposition, achieving an RMSE of 7.70 (vol.%) and R2 of 0.60.

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

scholarly journals Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss

2021 ◽  
Vol 13 (4) ◽  
pp. 680
Author(s):  
Lei Wang ◽  
Wen Zhuo ◽  
Zhifang Pei ◽  
Xingyuan Tong ◽  
Wei Han ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Arabian Peninsula ◽  
Vegetation Coverage ◽  
Observation Data ◽  
Desert Locust ◽  
Meteorological Observations ◽  
Earth Observation Data ◽  
Vegetation Loss

Massive desert locust swarms have been threatening and devouring natural vegetation and agricultural crops in East Africa and West Asia since 2019, and the event developed into a rare and globally concerning locust upsurge in early 2020. The breeding, maturation, concentration and migration of locusts rely on appropriate environmental factors, mainly precipitation, temperature, vegetation coverage and land-surface soil moisture. Remotely sensed images and long-term meteorological observations across the desert locust invasion area were analyzed to explore the complex drivers, vegetation losses and growing trends during the locust upsurge in this study. The results revealed that (1) the intense precipitation events in the Arabian Peninsula during 2018 provided suitable soil moisture and lush vegetation, thus promoting locust breeding, multiplication and gregarization; (2) the regions affected by the heavy rainfall in 2019 shifted from the Arabian Peninsula to West Asia and Northeast Africa, thus driving the vast locust swarms migrating into those regions and causing enormous vegetation loss; (3) the soil moisture and NDVI anomalies corresponded well with the locust swarm movements; and (4) there was a low chance the eastwardly migrating locust swarms would fly into the Indochina Peninsula and Southwest China.

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