scholarly journals Description of the UCAR/CU Soil Moisture Product

2020 ◽  
Vol 12 (10) ◽  
pp. 1558 ◽  
Author(s):  
Clara Chew ◽  
Eric Small
Keyword(s):  
Soil Moisture ◽  
Standard Deviation ◽  
Correlation Coefficient ◽  
Random Noise ◽  
Soil Surface ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Noise Component ◽  
Median Correlation

Currently, the ability to use remotely sensed soil moisture to investigate linkages between the water and energy cycles and for use in data assimilation studies is limited to passive microwave data whose temporal revisit time is 2–3 days or active microwave products with a much longer (>10 days) revisit time. This paper describes a dataset that provides soil moisture retrievals, which are gridded to 36 km, for the upper 5 cm of the soil surface at sparsely sampled 6-hour intervals for +/− 38 degrees latitude for 2017–present. Retrievals are derived from the Cyclone Global Navigation Satellite System (CYGNSS) constellation, which uses GNSS-Reflectometry to obtain L-band reflectivity observations over the Earth’s surface. The product was developed by calibrating CYGNSS reflectivity observations to soil moisture retrievals from NASA’s Soil Moisture Active Passive (SMAP) mission. Retrievals were validated against observations from 171 in-situ soil moisture probes, with a median unbiased root-mean-square error (ubRMSE) of 0.049 cm3 cm−3 (standard deviation = 0.026 cm3 cm−3) and median correlation coefficient of 0.4 (standard deviation = 0.27). For the same stations, the median ubRMSE between SMAP and in-situ observations was 0.045 cm3 cm−3 (standard deviation = 0.025 cm3 cm−3) and median correlation coefficient was 0.69 (standard deviation = 0.27). The UCAR/CU Soil Moisture Product is thus complementary to SMAP, albeit with a larger random noise component, providing soil moisture retrievals for applications that require faster revisit times than passive microwave remote sensing currently provides.

Experimental Study of C Band Passive Microwave Remote Sensing of Soil Moisture

2013 ◽  
Vol 477-478 ◽  
pp. 624-627
Author(s):  
Xiao Liu Gao ◽  
Hui Hui Zhang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Emission Rate ◽  
Soil Surface ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Passive Microwave Remote Sensing ◽  
Soil Surface Roughness ◽  
Relationship Of

Passive microwave remote sensing is one of the most effective methods for inversing soil moisture. Under the condition of laboratory, firstly, C band microwave radiation was used to achieve the trial of ground-based remote sensing soil moisture, and then regression analysis was carried out according to the data measured, finally, got the C band experience regression model of soil moisture inversion. The results showed that: in the level-off state of soil surface, soil humidity and soil microwave emission rate is linear negative correlation, in the other words, soil microwave emission rate decreased while the soil moisture increased. Besides, with the increasing of soil surface roughness, both the value of microwave polarization index (MPDI) and microwave emission rate polarization difference Δe have the same trend of quick drop, stabilization and slow raise, and it presented the relationship of quadratic curve with the change of roughness.

Passive Microwave Remote Sensing Soil Moisture Data in Agricultural Drought Monitoring: Application in Northeastern China

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2777
Author(s):  
Tao Cheng ◽  
Siyang Hong ◽  
Bensheng Huang ◽  
Jing Qiu ◽  
Bikui Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Pearson Correlation ◽  
Microwave Remote Sensing ◽  
Northeastern China ◽  
Passive Microwave ◽  
Agricultural Drought ◽  
Passive Microwave Remote Sensing ◽  
Soil Moisture Data

Drought is the costliest disaster around the world and in China as well. Northeastern China is one of China’s most important major grain producing areas. Frequent droughts have harmed the agriculture of this region and further threatened national food security. Therefore, the timely and effective monitoring of drought is extremely important. In this study, the passive microwave remote sensing soil moisture data, i.e., the SMOS soil moisture (SMOS-SM) product, was compared to several in situ meteorological indices through Pearson correlation analysis to assess the performance of SMOS-SM in monitoring drought in northeastern China. Then, maps based on SMOS-SM and in situ indices were created for July from 2010 to 2015 to identify the spatial pattern of drought distributions. Our results showed that the SMOS-SM product had relatively high correlation with in situ indices, especially SPI and SPEI values of a nine-month scale for the growing season. The drought patterns shown on maps generated from SPI-9, SPEI-9 and sc-PDSI were also successfully captured using the SMOS-SM product. We found that the SMOS-SM product effectively monitored drought patterns in northeastern China, and this capacity would be enhanced when field capacity information became available.

Passive Microwave Remote Sensing of Soil Moisture and Regional Drought Monitoring

2005 ◽  
Author(s):  
Thomas J. Jackson
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Drought Monitoring ◽  
Passive Microwave Remote Sensing ◽  
The Past ◽  
Physically Based ◽  
Effects Of Drought

Mitigating the effects of drought can be improved through better information on the current status, the prediction of occurrence, and the extent of drought. Soil moisture can now be measured using a new generation of microwave remote sensing satellites. These measurements can be used to monitor drought conditions on a daily basis over the entire earth. The quality of these products will continue to improve over time as new sensors are launched. These satellite products, combined with existing in situ observations and models, should be exploited in drought monitoring, assessment, and prediction. Measuring soil moisture on a routine basis has the potential to significantly improve our understanding of climatic processes and strengthen our ability to model and forecast these processes. Leese et al. (2001) concluded that the optimal approach to monitoring soil moisture would be a combination of model-derived estimates using in situ and remotely sensed measurements. In this regard, each method produces soil moisture values that are both unique and complementary. This concept is essentially the process of data assimilation described by Houser et al. (1998). In situ measurements of soil moisture have been made in a few countries over the past 70 years (Robock et al., 2000). However, due to cost and sensor limitations, there are few soil moisture sensor systems available today, especially for automated measurements. A lack of routine observations of soil moisture has led to the use of surrogate measurements (i.e., antecedent precipitation index) and modeled estimates, which limits the possibility of physically based model validation and acceptance. Current tools to predict drought, such as drought indices and Global Climate Models (GCMs), do not include any direct observations of the soil condition, which is critical for agriculture. Passive microwave remote sensing instruments respond to the amount of moisture in the soil. Several methods have the potential to provide both soil moisture and drought information. In the past, the options have been limited by the availability of satellite systems. Even with these limitations, investigators have explored the potential of these data in soil moisture studies with some success.

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

Improved Northern Hemisphere Snow Water Equivalent product from passive microwave remote sensing and in situ data

2021 ◽  
Author(s):  
Colleen Mortimer ◽  
Lawrence Mudryk ◽  
Chris Derksen ◽  
Kari Luojus ◽  
Pinja Venalainen ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Snow Density ◽  
Snow Season ◽  
Snow Water ◽  
Snow Mass

<p>The European Space Agency Snow CCI+ project provides global homogenized long time series of daily snow extent and snow water equivalent (SWE). The Snow CCI SWE product is built on the Finish Meteorological Institute's GlobSnow algorithm, which combines passive microwave data with in situ snow depth information to estimate SWE. The CCI SWE product improves upon previous versions of GlobSnow through targeted changes to the spatial resolution, ancillary data, and snow density parameterization.</p><p>Previous GlobSnow SWE products used a constant snow density of 0.24 kg m<sup>-3</sup> to convert snow depth to SWE. The CCI SWE product applies spatially and temporally varying density fields, derived by krigging in situ snow density information from historical snow transects to correct biases in estimated SWE. Grid spacing was improved from 25 km to 12.5 km by applying an enhanced spatial resolution microwave brightness temperature dataset. We assess step-wise how each of these targeted changes acts to improve or worsen the product by evaluating with snow transect measurements and comparing hemispheric snow mass and trend differences.</p><p>Together, when compared to GlobSnow v3, these changes improved RMSE by ~5 cm and correlation by ~0.1 against a suite of snow transect measurements from Canada, Finland, and Russia. Although the hemispheric snow mass anomalies of CCI SWE and GlobSnow v3 are similar, there are sizeable differences in the climatological SWE, most notably a one month delay in the timing of peak SWE and lower SWE during the accumulation season. These shifts were expected because the variable snow density is lower than the former fixed value of 0.24 kg m<sup>-3</sup> early in the snow season, but then increases over the course of the snow season. We also examine intermediate products to determine the relative improvements attributable solely to the increased spatial resolution versus changes due to the snow density parameterizations. Such systematic evaluations are critical to directing future product development.</p>

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