Soil moisture estimation using time-series radar measurements of bare and vegetated fields in Washita '92

2002 ◽  
Author(s):  
P.E. O'Neill ◽  
A.Y. Hsu ◽  
J.C. Shi
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Radar Measurements ◽  
Moisture Estimation

An Extension of the Alpha Approximation Method for Soil Moisture Estimation Using Time-Series SAR Data Over Bare Soil Surfaces

2017 ◽  
Vol 14 (8) ◽  
pp. 1328-1332 ◽  
Author(s):  
Lian He ◽  
Qiming Qin ◽  
Rocco Panciera ◽  
Mihai Tanase ◽  
Jeffrey P. Walker ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Approximation Method ◽  
Bare Soil ◽  
Sar Data ◽  
Moisture Estimation

scholarly journals Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Xiang Zhang ◽  
Xinming Tang ◽  
Xiaoming Gao ◽  
Hui Zhao
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Root Mean Square Error ◽  
Field Measurements ◽  
Mean Square ◽  
Approximation Model ◽  
Unknown Parameters ◽  
Sar Data ◽  
Agricultural Areas ◽  
Moisture Estimation

The objective of this research is to optimize the Alpha approximation model for soil moisture retrieval using multitemporal SAR data. The Alpha model requires prior knowledge of soil moisture range to constrain soil moisture estimation. The solution of the Alpha model is an undetermined problem due to the fact that the number of observation equations is less than the number of unknown parameters. This research primarily focused on the optimization of Alpha model by employing multisensor and multitemporal SAR data. The disadvantage of the Alpha model can be eliminated by the combination of multisensor SAR data. The optimized Alpha model was evaluated on the basis of a comprehensive campaign for soil moisture retrieval, which acquired multisensor time series SAR data and coincident field measurements. The agreement between the estimated and measured soil moisture was within a root mean square error of 0.08 cm3/cm3 for both methods. The optimized Alpha model shows an obvious improvement for soil moisture retrieval. The results demonstrated that multisensor and multitemporal SAR data are favorable for time series soil moisture retrieval over bare agricultural areas.

scholarly journals Spatial Distribution of Soil Moisture in Mongolia Using SMAP and MODIS Satellite Data: A Time Series Model (2010–2025)

2021 ◽  
Vol 13 (3) ◽  
pp. 347
Author(s):  
Enkhjargal Natsagdorj ◽  
Tsolmon Renchin ◽  
Philippe De Maeyer ◽  
Bayanjargal Darkhijav
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Soil Moisture ◽  
Correlation Coefficient ◽  
Crop Yield ◽  
Arima Model ◽  
Agricultural Drought ◽  
Monthly Precipitation ◽  
Precipitation Temperature ◽  
Moisture Estimation

Soil moisture is one of the essential variables of the water cycle, and plays a vital role in agriculture, water management, and land (drought) and vegetation cover change as well as climate change studies. The spatial distribution of soil moisture with high-resolution images in Mongolia has long been one of the essential issues in the remote sensing and agricultural community. In this research, we focused on the distribution of soil moisture and compared the monthly precipitation/temperature and crop yield from 2010 to 2020. In the present study, Soil Moisture Active Passive (SMAP) and Moderate Resolution Imaging Spectroradiometer (MODIS) data were used, including the MOD13A2 Normalized Difference Vegetation Index (NDVI), MOD11A2 Land Surface Temperature (LST), and precipitation/temperature monthly data from the Climate Research Unit (CRU) from 2010 to 2020 over Mongolia. Multiple linear regression methods have previously been used for soil moisture estimation, and in this study, the Autoregressive Integrated Moving Arima (ARIMA) model was used for soil moisture forecasting. The results show that the correlation was statistically significant between SM-MOD and soil moisture content (SMC) from the meteorological stations at different depths (p < 0.0001 at 0–20 cm and p < 0.005 at 0–50 cm). The correlation between SM-MOD and temperature, as represented by the correlation coefficient (r), was 0.80 and considered statistically significant (p < 0.0001). However, when SM-MOD was compared with the crop yield for each year (2010–2019), the correlation coefficient (r) was 0.84. The ARIMA (12, 1, 12) model was selected for the soil moisture time series analysis when predicting soil moisture from 2020 to 2025. The forecasting results are shown for the 95 percent confidence interval. The soil moisture estimation approach and model in our study can serve as a valuable tool for confident and convenient observations of agricultural drought for decision-makers and farmers in Mongolia.

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