Evaluation of SMAP Core Validation Site Representativeness Errors Using Dense Networks of In Situ Sensors and Random Forests

Abstract

In order to validate its soil moisture products, the NASA Soil Moisture Active Passive (SMAP) mission utilizes sites with permanent networks of in situ soil moisture sensors maintained by independent calibration and validation partners in a variety of ecosystems around the world. Measurements from each core validation site (CVS) are combined in a weighted average to produce an estimate of soil moisture at a 33-km scale that represents the SMAP's radiometer-based retrievals. Since upscaled estimates produced in this manner are dependent on the weighting scheme applied, an independent method of quantifying their biases is needed. Here we present one such method that uses soil moisture measurements taken from a dense, but temporary, network of soil moisture sensors deployed at each CVS to train a Random Forests regression expressing soil moisture in terms of a set of spatial variables. The regression then serves as an independent source of upscaled estimates against which permanent network upscaled estimates can be compared in order to calculate bias statistics. This method, which offers a systematic and unified approach to estimate bias across a variety of validation sites, was applied to estimate biases at four CVSs. The results showed that the magnitude of the uncertainty in the permanent network upscaling bias can sometimes exceed 80% of the upper limit on SMAP's entire allowable unbiased root-meansquare error (ubRMSE). Such large CVS bias uncertainties could make it more difficult to assess biases in soil moisture estimates from SMAP.

Validation of Soil Moisture Data Products from the NASA SMAP Mission

“…These temporary measurements are useful as a reference for the permanent network measurements. As expected, SM measurements from the permanent and temporary networks were very well correlated overall, but the absolute SM difference ranged from 0.009 m 3 /m 3 to 0.034 m 3 /m 3 [93], which supports the finding by [77] that significant uncertainties in absolute SM remain even with relatively dense spatial sampling.…”

Validation of Soil Moisture Data Products From the NASA SMAP Mission

“…These temporary measurements are useful as a reference for the permanent network measurements. As expected, SM measurements from the permanent and temporary networks were very well correlated overall, but the absolute SM difference ranged from 0.009 to 0.034 m 3 /m 3 [98], which supports the finding by Chen et al [82] that significant uncertainties in absolute SM remain even with relatively dense spatial sampling.…”

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

“…Based on this estimation, statistical evaluation of these models is tabulated in Based on this comparison, it can be observed that different models are useful for different application requirements. For instance, in terms of accuracy as observed from figure 5, DBN RBM [2], CRNS [3], SMAP RF DN [19], GOFCHS [27], TDR [28], and P Band & L Band [34] models outperform other models, thus, they can be used for highly accurate moisture detection applications. Similarly, cost of deployment & computational complexity is visualized from figure 6, wherein it is observed that HPCM [6], HF RFID TFS [9], PWM [10], PMMA [15], FFCSM [16], MHPS [21], ECT [24], PQCWC [25], and HSAAA [32] require lowest deployment cost, while HPCM [6], PHS [17], ECT [24], and PQCWC [25] have lower computational complexity when compared with other models.…”

“…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, ...…”