Spatial Scaling and Variability of Soil Moisture Over Heterogeneous Land Cover and Dynamic Vegetation Conditions

Nagarajan, K.; Judge, Jasmeet

doi:10.1109/lgrs.2012.2226430

Cited by 13 publications

(11 citation statements)

References 19 publications

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“…However, it has to be noted that vegetation may also have a homogenizing effect (see [32]). A specific analysis of each factor introducing the heterogeneity of the soil moisture can improve the knowledge of the relevant processes before a synoptic analysis of all factors in context is performed (see the discussions in [33] and [34]).…”

Section: Resultsmentioning

confidence: 99%

Estimating the Number of Reference Sites Necessary for the Validation of Global Soil Moisture Products

Montzka

Bogena²,

Herbst³

et al. 2021

IEEE Geosci. Remote Sensing Lett.

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The Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup has been established to coordinate the development of standardized validation across the satellite-derived products from different platforms, sensors, and algorithms with reference measurements from the in situ networks. Soil moisture exhibits a high variability in space that challenges the in situ validation. One of the main drivers for this variability is the characteristic heterogeneity in the soil texture. By the machine learning methods using the soil profile measurements and the remotely sensed predictors, spatially continuous maps of basic soil properties such as soil texture and bulk density are available. Those can be used to estimate soil moisture variability within a satellite product grid cell, here exemplarily shown for the Soil Moisture Active Passive (SMAP) 36-km product. The soil moisture standard deviation is described as a function of the mean soil moisture, whereby the approach needs the mean and standard deviation of the hydraulic parameters as input. The resulting global data set helps identifying the number of in situ stations necessary to validate the coarse soil moisture products. For most SMAP grid cells, three to four stations are adequate to estimate the mean soil moisture for validation; however, also regions were identified where 80 stations are necessary.

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Section: Resultsmentioning

confidence: 99%

Estimating the Number of Reference Sites Necessary for the Validation of Global Soil Moisture Products

Montzka

Bogena²,

Herbst³

et al. 2021

IEEE Geosci. Remote Sensing Lett.

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“…The proposed algorithm for disaggregation was tested using data generated by a simulation framework consisting of the Land Surface Process (LSP) model and the Decision Support System for Agrotechnology Transfer (DSSAT) model, described in [24]. A 50 × 50 km 2 region, equivalent to approximately 25 SMAP pixels at 9 km spatial resolution, was chosen in North Central Florida (see Figure 4) Fifteen-minute observations of precipitation, relative humidity, air temperature, downwelling solar radiation, and wind speed were obtained from eight Florida Automated Weather Network (FAWN) stations [28] located within the study region (see Figure 4).…”

Section: A Multiscale Synthetic Datasetmentioning

confidence: 99%

Disaggregation of Remotely Sensed Soil Moisture in Heterogeneous Landscapes Using Holistic Structure-Based Models

Chakrabarti

Judge

Bongiovanni

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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In this study, a novel machine learning algorithm is presented for disaggregation of satellite soil moisture (SM) based on self-regularized regressive models (SRRM) using high-resolution correlated information from auxiliary sources. It includes regularized clustering that assigns soft memberships to each pixel at fine-scale followed by a kernel regression that computes the value of the desired variable at all pixels. Coarse-scale remotely sensed SM were disaggregated from 10km to 1km using land cover, precipitation, land surface temperature, leaf area index, and in-situ observations of SM. This algorithm was evaluated using multi-scale synthetic observations in NC Florida for heterogeneous agricultural land covers. It was found that the root mean square error (RMSE) for 96% of the pixels was less than 0.02 m 3 /m 3 . The clusters generated represented the data well and reduced the RMSE by upto 40% during periods of high heterogeneity in land-cover and meteorological conditions. The Kullback Leibler divergence (KLD) between the true SM and the disaggregated estimates is close to 0, for both vegetated and baresoil landcovers. The disaggregated estimates were compared to those generated by the Principle of Relevant Information (PRI) method. The RMSE for the PRI disaggregated estimates is higher than the RMSE for the SRRM on each day of the season. The KLD of the disaggregated estimates generated by the SRRM is at least four orders of magnitude lower than those for the PRI disaggregated estimates, while the computational time needed was reduced by three times. The results indicate that the SRRM can be used for disaggregating SM with complex non-linear correlations on a grid with high accuracy.A version of this manuscript has been submitted to the IEEE c Transactions on Geoscience and Remote Sensing. S. Chakrabarti and J. Judge are with

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Section: A Multiscale Synthetic Datasetmentioning

confidence: 99%

“…The goal of this study is to implement a downscaling algorithm that disaggregates coarse-scale remotely sensed products with auxiliary fine-scale data. The primary objectives are to, 1) estimate T B at 1 km using T B at 10 km and other spatially correlated variables for a multi-scale synthetic dataset [9] based in North-Central Florida, and 2) to conduct a thorough statistical analysis of the downscaled T B in order to evaluate the efficacy of the SRRM downscaling algorithm.…”

Section: Introductionmentioning

confidence: 99%

Downscaling microwave brightness temperatures using self regularized regressive models

Chakrabarti

Judge

Rangarajan

et al. 2015

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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An novel algorithm is proposed to downscale microwave brightness temperatures (T B ), at scales of 10-40 km such as those from the Soil Moisture Active Passive mission to a resolution meaningful for hydrological and agricultural applications. This algorithm, called Self-Regularized Regressive Models (SRRM), uses auxiliary variables correlated to T B along-with a limited set of in-situ SM observations, which are converted to high resolution T B observations using biophysical models. It includes an information-theoretic clustering step based on all auxiliary variables to identify areas of similarity, followed by a kernel regression step that produces downscaled T B . This was implemented on a multi-scale synthetic data-set over NC-Florida for one year. An RMSE of 5.76 K with standard deviation of 2.8 k was achieved during the vegetated season and an RMSE of 1.2 K with a standard deviation of 0.9 K during periods of no vegetation.

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Spatial Scaling and Variability of Soil Moisture Over Heterogeneous Land Cover and Dynamic Vegetation Conditions

Cited by 13 publications

References 19 publications

Estimating the Number of Reference Sites Necessary for the Validation of Global Soil Moisture Products

Estimating the Number of Reference Sites Necessary for the Validation of Global Soil Moisture Products

Disaggregation of Remotely Sensed Soil Moisture in Heterogeneous Landscapes Using Holistic Structure-Based Models

Downscaling microwave brightness temperatures using self regularized regressive models

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