Earth Observation (EO) technologies have played an increasingly important role in monitoring the Sustainable Development Goals (SDG). These technologies often combined with Machine Learning (ML) models provide efficient means for achieving the SDGs. The great progress of this combination is also demonstrated by the large number of software, web tools and packages that have been made available for free use. In this paper, we introduce a software architecture to facilitate the generation of EO information targeted towards soil moisture that derive several challenges regarding the facilitation of satellite data processing. Thus, this paper presents a web-based tool for Soil Moisture Estimation (SMETool), designed for the soil moisture estimation using Sentinel-1A and Sentinel-2A data based on Eo-learn library. SMETool implements several ML techniques such as (Artificial Neural Network (ANN), Random Forest (RF), Convolutional Neural Network (CNN), etc.). The SMETool could be very useful for decision makers in the region in assessing the effects of drought and desertification events. Experiments were carried out on two sites in Tunisia during the period from 2016 to 2017. Although the performance of the used models is very close, it is clear that CNN and RF outperformed other ML models. The achieved results reveal that the soil moisture, was highly correlated to the in-situ measurements with high Pearson’s correlation coefficient r (rRF=0.86, rANN=0.75, rXGBoost=0.79, rCNN=0.87) and low Root Mean Square Error (RMSE) (RMSERF = 1.09%, RMSEANN = 1.49%, RMSEXGBoost = 1.39%, RMSECNN = 1.12%), respectively. 

SMETool: A web-based tool for soil moisture estimation based on Eo-Learn framework and Machine Learning methods

&amp;lt;p&amp;gt;Soil moisture (SM) is an essential element in the hydrological cycle influencing land-atmosphere interactions and rainfall-runoff processes. Quantification of the spatial and temporal behaviour of SM at field scale is vital for understanding water availability in agriculture, ecosystems research, river basin hydrology and water resources management. Uncrewed Arial Systems (UAS) offer an extraordinary opportunity to bridge the existing gap between point-scale field observations and satellite remote sensing providing high spatial details at relatively low costs. Moreover, UAS data can help the construction of downscaling models which can link the land surface features and SM to identify the importance level of each predictor. To optimize the usage of data from UAS surveys for generating high-resolution SM at field scale, a comparative study of various SM retrieval or downscaling methods can be beneficial.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, four methods, which include the apparent thermal inertia method, Kubelka&amp;amp;#8211;Munk method (KM), simplified temperature-vegetation triangle method, and random forest model (RF), were compared by theory background, data requirements, operation procedures and SM estimation results. The above-mentioned models have been tested using UAS data and point measurements collected on the Monteforte Cilento site (MFC2) in the Alento river basin (Campania, Italy) which is an 8 hectares cropland area (covered by walnuts, cherry, and olive trees). A number of long-term studies on the vadose zone have been conducted across a range of spatial scales. The thermal inertia model is built upon the dependence of the thermal diffusion on SM, which were inferred from diachronic thermal infrared data. The Kubelka&amp;amp;#8211;Munk Model is a spectral model to retrieve surface SM using optical data. The simplified temperature&amp;amp;#8211;vegetation triangle model, was used to map surface SM based on simultaneous information of the vegetation coverage and surface temperature. In addition, we also introduce an SM downscaling method using the RF model and SENTINEL-1 CSAR 1km SM product.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The study is concluded with the inter-comparison of methods. The results from KM have the highest resolution which is the same as the input multispectral data. The results of RF and KM provides information only for bare soil pixels according to the principle of the model. Results show good performances for all methods, but the simplified triangle and thermal inertia model provides better performances in terms of correlation coefficient and RMSE measured with respect to in-situ measurements. In addition, it is worthy to say that the RF downscaling method reveals the features controlling the spatial distributions of SM at a different scale.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This research is a part of EU COST-Action &amp;amp;#8220;HARMONIOUS&amp;amp;#8221; and waterJPI project &amp;amp;#8220;iAqueduct&amp;amp;#8221;.&amp;lt;/p&amp;gt; 

/pdf/soil-moisture-mapping-using-uncrewed-arial-systems-uas-17ie55v8.pdf

Soil Moisture Mapping Using Uncrewed Arial Systems (UAS)

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Soil Moisture Mapping Using Uncrewed Arial Systems (UAS)