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SMAP products for prediction of surface soil moisture by ELM network model and agricultural drought index

Karamvand Aliakbar, Hosseini Seyed Abbas, Sharafati Ahmad

Acta Geophysica

2023

Vol. 71, no. 4

1845--1856

In recent decades, the earth’s surface data have been collected more efficiently using remote sensing, which needs drought indexes update. In this study, soil moisture (SM) data were collected from the surface layer of a high humidity climate in northern Iran using Soil Moisture Active Passive (SMAP) and field measurement. After analyzing the data, we found that the average RMSE between the field and SMAP measurement was 0.054 m³/m³. Considering the same agricultural land use and the strong correlation of 0.92 between them, the validated SMAP data were used to propose an agricultural drought index. After data validation, the extreme learning machine (ELM) model was put to the test using sigmoid, triangular, sine, and hard-limit activation functions. Of all the activation functions tested, the model with the sigmoid activation function yielded the lowest amount of error and was therefore chosen. Five years of continuous daily SM as a target, five-year daily normalized difference vegetation index, land surface temperature, and precipitation were inputs to predict one-year daily SM time series in the humid climate. From 2021 to 2022, daily surface SM was predicted with the average RMSE=0.03 m³/m³ compared to the SMAP data. Finally, a new regional agricultural drought index based on 4 years of SMAP and 1-year prediction of SMAP from 2022 to 2023 was proposed. Further investigation is needed to conclude that the application of the presented index is reliable in other climates.

Assessment of surface soil moisture from ALOS PALSAR 2 in small scale maize felds using polarimetric decomposition technique

Gururaj Punithraj, Umesh Pruthviraj, Shetty Amba

Acta Geophysica

2021

Vol. 69, no. 2

579--588

Surface soil moisture knowledge is important, especially in agriculture and irrigation management. Properties of microwave remote sensing like penetration power and longer wavelength facilitate retrieval of surface soil moisture. ALOS PALSAR-2, quad polarized data are used to retrieve surface soil moisture using polarization decomposition techniques in a marginal farmer small-scale maize feld. The focus of the study is to explore the utility of ALOS PALSAR-2 in retrieving surface soil moisture using the polarization decomposition technique. The demonstration of the study is carried out in Malavalli village, southern India, an agricultural predominant area. The study involves feld soil moisture sampling in synchronous with satellite pass, measuring soil properties, preprocessing of SAR data, polarization decomposition, proportional analysis, regression analysis, model calibration and validation. Van Zyl decomposition gave the highest surface scattering component (43%) and reduced volumetric scattering component compared to Yamaguchi and Freeman–Durden decomposition. Surface scattering component of Yamaguchi decomposition gave a good coefcient of determination (R2=0.8029) with feld-measured surface soil moisture. The semi-empirical model (SEM) was developed using surface scattering component and depolarization ratio with adjusted R2=0.75 at 95% confdence interval. On its comparison with existing soil moisture models, it is observed that the developed model is performing well with RMSE and AEmax of 1.81 and 2.88, respectively. Implying the applicability of ALOS PALSAR-2 in soil moisture retrieval in marginal farmer small-scale maize felds gave satisfactory results of accuracy.