A Novel Scheme for Merging Active and Passive Satellite Soil Moisture Retrievals Based on Maximizing the Signal to Noise Ratio

Abstract

In this research, we developed and evaluated a new scheme for merging soil moisture (SM) retrievals from both passive and active microwave satellite estimates, based on maximized signal-to-noise ratios, in order to produce improved SM products using least-squares theory. The fractional mean-squared-error (fMSE) derived from the triple collocation method (TCM) was used for this purpose. The proposed scheme was applied by using a threshold between signal and noise at fMSE equal to 0.5 to maintain the high-quality SM observations. In the regions where TCM is unreliable, we propose four scenarios based on the determinations of correlations between all three SM products of TCM at significance levels (i.e., p-values). The proposed scheme was applied to combine SM retrievals from Soil Moisture Active Passive (SMAP), Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) to produce SMAP+ASCAT and AMSR2+ASCAT SM datasets at a global scale for the period from June 2015 to December 2017. The merged SM dataset performance was assessed against SM data from ground measurements of international soil moisture network (ISMN), Global Land Data Assimilation System-Noah (GLDAS-Noah) and ERA5. The results show that the two merged SM datasets showed significant improvement over their parent products in the high average temporal correlation coefficients (R) and the lowest root mean squared difference (RMSE), compared with in-situ measurements over different networks of ISMN. Moreover, these datasets outperformed their parent products over different land cover types in most regions of the world, with a high overall average temporal R and the lowest overall average RMSE value with GLDAS and ERA5. In addition, the suggested scenarios improved SM performance in the regions with unreliable TCMs.

Monitoring Land Use And Land Cover Changes Using Geospatial Techniques, A Case Study Of Fateh Jang, Attock, Pakistan

“…Correspondingly, temperature increase could also lead to changes in hydrological fluxes, soil disintegration and faster behavioural expansion, causing reduced yields and accelerated maturation (Bonan 2008;Hanson and Weltzin 2000). Precipitation is the most important factor in the continuing degradation of forests and agricultural land (Mousa et al 2020). Higher rainfall has required a higher degree of output in some places of Pakistan and offered more water for irrigation.…”

“…RS and GIS are essential and the most common techniques for obtaining detailed and timely spatial data on LULC and to analyse its regional changes (Lee et al 2012;Pradhan et al 2009;Singh S. and Rai 2018). Satellite imagery can be used to easily track land use changes, providing an outstanding foundation of statistics from which the information on LULC dynamics can be retrieved, tested and reproduced (Nachappa et al 2019;Singh S.K. et al 2018).…”

Prediction of flash flood susceptibility using integrating analytic hierarchy process (AHP) and frequency ratio (FR) algorithms

“…On the contrary, the lower FSI value suggests that chance of a flood event is less. To identify and recognize the flood risk zones, the FSI database was organized into 5 susceptibility areas (Mousa et al, 2020;Freeshah et al, 2021;Tariq et al, 2021f;Zainab et al, 2021). The zones were categorized into very low, low, moderate, high, and very high-risk zones, covering an area of 2, 458.26, 361.87, 496.11, 239.81, and 86.25 km 2 , respectively (Table 5).…”

Comparison of Three Machine Learning Algorithms Using Google Earth Engine for Land Use Land Cover Classification

“…Earth observation (EO) data have a vital role in producing LULC maps of any place around the globe ( Mousa et al 2020 ;Siddiqui et al 2020 ;Zainab et al 2021 ;Hussain et al 2023 ) due to its costeffective information of land ( Tavares et al 2019 ). The availability of EO data including low-resolution images (Moderate Resolution Imaging Spectroradiometer to medium-resolution (Sentinel-2 and Landsat-8) and high-resolution images (Advanced Very High Resolution Radiometer) is a great contribution to researchers all over the world ( Al-Najjar et al 2019 ;Zhu et al 2021 ).…”