Estimation of soil moisture from UAS platforms using RGB and thermal imaging sensors in arid and semi-arid regions

Soil moisture (SM) is a connective hydrological variable between the Earth’s surface and atmosphere and affects various climatological processes. Surface soil moisture (SSM) is a key component for addressing energy and water exchanges and can be estimated using different techniques, such as in situ and remote sensing (RS) measurements. Discrete, costly and prolonged, in situ measurements are rarely capable in demonstration of moisture fluctuations. On the other hand, current high spatial resolution satellite sensors lack the spectral resolution required for many quantitative RS applications, which is critical for heterogeneous covers. RS-based unmanned aerial systems (UASs) represent an option to fill the gap between these techniques, providing low-cost approaches to meet the critical requirements of spatial, spectral and temporal resolutions. In the present study, SM was estimated through a UAS equipped with a thermal imaging sensor. To this aim, in October 2018, two airborne campaigns during day and night were carried out with the thermal sensor for the estimation of the apparent thermal inertia (ATI) over an agricultural field in Iran. Simultaneously, SM measurements were obtained in 40 sample points in the different parts of the study area. Results showed a good correlation (R2=0.81) between the estimated and observed SM in the field. This study demonstrates the potential of UASs in providing high-resolution thermal imagery with the aim to monitor SM over bare and scarcely vegetated soils. A case study based in a wide agricultural field in Iran was considered, where SM monitoring is even more critical due to the arid and semi-arid climate, the lack of adequate SM measuring stations, and the poor quality of the available data

Soil moisture monitoring in Iran by implementing satellite data into the Root-Zone SMAR model

Monitoring Surface Soil Moisture (SSM) and Root Zone Soil Moisture (RZSM) dynamics at the regional scale is of fundamental importance to many hydrological and ecological studies. This need becomes even more critical in arid and semi-arid regions, where there are a lack of in situ observations. In this regard, satellite-based Soil Moisture (SM) data is promising due to the temporal resolution of acquisitions and the spatial coverage of observations. Satellite-based SM products are only able to estimate moisture from the soil top layer; however, linking SSM with RZSM would provide valuable information on land surface-atmosphere interactions. In the present study, satellite-based SSM data from Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Soil Moisture Active Passive (SMAP) are first compared with the few available SM in situ observations, and are then coupled with the Soil Moisture Analytical Relationship (SMAR) model to estimate RZSM in Iran. The comparison between in situ SM observations and satellite data showed that the SMAP satellite products provide more accurate description of SSM with an average correlation coefficient (R) of 0.55, root-mean-square error (RMSE) of 0.078 m3 m-3 and a Bias of 0.033 m3 m-3. Thereafter, the SMAP satellite products were coupled with SMAR model, providing a description of the RZSM with performances that are strongly influenced by the misalignment between point and pixel processes measured in the preliminary comparison of SSM data

Estimation of soil moisture from UAS platforms using RGB and thermal imaging sensors in arid and semi-arid regions

Soil moisture (SM) is a connective hydrological variable between the Earth's surface and atmosphere and affects various climatological processes. Surface soil moisture (SSM) is a key component for addressing energy and water exchanges and can be estimated using different techniques, such as in situ and remote sensing (RS) measurements. Discrete, costly and prolonged, in situ measurements are rarely capable in demonstration of moisture fluctuations. On the other hand, current high spatial resolution satellite sensors lack the spectral resolution required for many quantitative RS applications, which is critical for heterogeneous covers. RS-based unmanned aerial systems (UASs) represent an option to fill the gap between these techniques, providing low-cost approaches to meet the critical requirements of spatial, spectral and temporal resolutions. In the present study, SM was estimated through a UAS equipped with a thermal imaging sensor. To this aim, in October 2018, two airborne campaigns during day and night were carried out with the thermal sensor for the estimation of the apparent thermal inertia (ATI) over an agricultural field in Iran. Simultaneously, SM measurements were obtained in 40 sample points in the different parts of the study area. Results showed a good correlation (R2=0.81) between the estimated and observed SM in the field. This study demonstrates the potential of UASs in providing high-resolution thermal imagery with the aim to monitor SM over bare and scarcely vegetated soils. A case study based in a wide agricultural field in Iran was considered, where SM monitoring is even more critical due to the arid and semi-arid climate, the lack of adequate SM measuring stations, and the poor quality of the available data