Soil Moisture Index Model for Retrieving Soil Moisture in Semiarid Regions of China

Abstract

There have been some limitations in acquiring an accurate representation of remotely sensed data-derived soil moisture. Here, we propose a simplified thermal inertia model (STIM). This model requires only the albedo and surface maximum temperature easily obtained from satellite imagery such as that of the Moderate Resolution Imaging Spectroradiometer (MODIS). In this study, we defined a soil moisture index (SMI) using MODIS imagery to obtain a simplified thermal inertia (STI)-ratio vegetation index (RVI) spectral feature space. SMI results from STIM were validated at several locations in the study area of western Inner Mongolia and compared with those from the apparent thermal inertia (ATI) model. Our results showed that our SMI model could explain 71% of the variance in surface soil moisture, approximately 5% higher than that of the ATI model. In a comparison of field-measured soil moisture data with data simulated using two methods, the SMI and ATI, the SMI showed better retrieval accuracy by lessening the effective error due to vegetation by 4.2-10.8%, whereas soil moisture data simulated with ATI showed an effective error of 4.5-17.0%. The SMI model was also used to map soil moisture; relative root mean square error (RRMSE) of 7.67% was recorded for the region, implying the ability of the model to map soil moisture over large areas. Here, the proposed SMI model was proven to be more suitable for estimating soil moisture in locations in which the vegetation index values ranged from 0 to 3. Thus, the proposed SMI model provides a new approach using remote sensing thermal inertia methods to quantify soil moisture at the regional-scale. Index Terms-Albedo, apparent thermal inertia (ATI), MODIS, simplified thermal inertial model (STIM), soil moisture, STI-ratio vegetation index (RVI).

Modeling Yield of Irrigated and Rainfed Bean in Central and Southern Sinaloa State, Mexico, Based on Essential Climate Variables

“…According to [27] and [74], the correlations in bold (Table 1) can be defined as significantly different from zero, since for n = 32 (1982-2013), CSC = |0.350|; that is, SC > CSC. The significant SC can be attributed to the fact that beans are one of the most sensitive crops to maximumT, minimumT, meant, and wet and dry events (ASM and CEP) [14,20,75].…”

Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

“…These models utilize high-speed sensors, which might be costly, but provide quicker results when compared with other sensing models. Similarly, scalability of these models is also evaluated, which indicates that CM [11] has the highest scalability, which is followed by DBN RBM [2], SMAP [18], SMAP RF DN [19], GOFCHS [27], SAR [29], SMI MODIS [30], SSMDI [33], P Band & L Band [34], and MSNs [39] models.…”

“…While highly accurate sensing interfaces are costly, but can be used for high-speed moisture sensing applications. Specifically, DBN RBM, CRNS, SMAP LEWS LR [1] DBN RBM [2] LR RBM [2] BP RBM [2] CRNS [3] FBG [4] MWMS [5] HPCM [6] GPS [7] UHF RFID [8] HF RFID TFS [9] PWM [10] CM [11] PPMR [12] PLMR [12] RFID UHF [13] MSR [14] PMMA [15] FFCSM [16] PHS [17] SMAP [18] SMAP RF DN [19] FoS [20] MHPS [21] PRS [22] ECT [24] PQCWC [25] HDES [26] GOFCHS [27] TDR [28] SAR [29] SMI MODIS [30] CSMOS [31] HSAAA [32] SSMDI [33] P Band & L Band [34] FTO [35] eSMAP [36] PBG [38] MSNs [39] MSOCCML [40] SMAP TFC [41] CRNS [42] kCRNS [43] Computational RF DN, GOFCHS, TDR, and P Band & L Band models outperform other models; thus, they can be used for highly accurate moisture detection applications. While, HPCM, HF RFID TFS, PWM, PMMA, FFCSM, MHPS, ECT, PQCWC, ...…”

“…These models utilize various machine learning techniques to improve quality of detection, but require high performance computational processors for effective implementation & control. Based on these models, work in [29,30] that uses Synthetic aperture radar (SAR) for surface soil moisture level detection & moderate resolution imaging spectroradiometer (MODIS) for soil moisture index (SMI) evaluation via simplified thermal inertia-ratio vegetation index spectral feature space can be improved. These models are currently utilizing linear classification interfaces, which limits their measurement performance.…”

FPGA-Embedded Smart Monitoring System for Irrigation Decisions Based on Soil Moisture and Temperature Sensors

“…The system focuses on controlling the irrigation process automatically [18][19][20]. Automation is implemented by programming in VHSIC Hardware Description Language (VHDL) code on the field-programmable gate array (FPGA) board to display and measure humidity as a function of temperature to monitor soil moisture [21][22][23].…”