Soil Moisture Estimates From AMSR-E Brightness Temperatures by Using a Dual-Frequency Algorithm

Paloscia, S.; Macelloni, Giovanni; Santi, Emanuele

doi:10.1109/tgrs.2006.881714

Cited by 116 publications

(52 citation statements)

References 35 publications

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“…Soil moisture from AMSR-E is calibrated and validated at different sites. Good agreement is found between AMSR-E soil moisture and ground data at different sites ranging from natural vegetation to crops [7][8][9][10]. This consistent level of agreement between AMSR-E soil moisture with ground observation over a range of meteorological and surface conditions, offers a promise for application of AMSR-E soil moisture data to areas with different environmental conditions.…”

Section: Introductionsupporting

confidence: 57%

Examining the Spatial and Temporal Variability of Soil Moisture in Kentucky Using Remote Sensing Data

Masri¹

2017

BJSTR

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Section: Introductionsupporting

confidence: 57%

Examining the Spatial and Temporal Variability of Soil Moisture in Kentucky Using Remote Sensing Data

Masri¹

2017

BJSTR

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“…Remote sensing from active (SAR and scatterometer) and passive sensors (radiometers) have demonstrated to be good and flexible tools to detect spatial and temporal SMC [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Comparison between SAR Soil Moisture Estimates and Hydrological Model Simulations over the Scrivia Test Site

et al. 2013

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Abstract:In this paper, the results of a comparison between the soil moisture content (SMC) estimated from C-band SAR, the SMC simulated by a hydrological model, and the SMC measured on ground are presented. The study was carried out in an agricultural test site located in North-west Italy, in the Scrivia river basin. The hydrological model used for the simulations consists of a one-layer soil water balance model, which was found to be able to partially reproduce the soil moisture variability, retaining at the same time simplicity and effectiveness in describing the topsoil. SMC estimates were derived from the application of a retrieval algorithm, based on an Artificial Neural Network approach, to a time series of ENVISAT/ASAR images acquired over the Scrivia test site. The core of the algorithm was represented by a set of ANNs able to deal with the different SAR configurations in terms of polarizations and available ancillary data. In case of crop covered soils, the effect of vegetation was accounted for using NDVI information, or, if available, for the cross-polarized channel. The algorithm results showed some ability in retrieving SMC with RMSE generally <0. for the SMC retrieval from Sentinel-1 data. The algorithm should take into account the GMES requirements of SMC accuracy (≤5% in volume), spatial resolution (≤1 km) and timeliness (3 h from observation). The SMC estimated by the SAR algorithm, the SMC estimated by the hydrological model, and the SMC measured on ground were found to be in good agreement. The hydrological model simulations were performed at two soil depths: 30 and 5 cm and showed that the 30 cm simulations indicated, as expected, SMC values higher than the satellites estimates, with RMSE higher than 0.08 m

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“…Various algorithms have been developed for the retrieval of soil moisture from AMSR-E data. Several researchers have used these data products provided by AMSR-E and have validated these algorithms [34][35][36][37]. In the case of AMSR-E data, nonlinear iterative algorithm [38] has been used for the generation of soil moisture products and the final data product is being distributed worldwide by the National Snow and Ice Data Centre (NSIDC).…”

Section: Challenges Of Soil Moisture Estimation Over Large Areasmentioning

confidence: 99%

Passive Microwave Remote Sensing of Soil Moisture: A Step-ByStep Detailed Methodology using AMSR-E Data over Indian SubContinent

Singh

Srivastava

Shashi

et al. 2015

IJARSG

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This paper presents a detailed methodology to process AMSR-E soil moisture data to generate average soil moisture maps of desired durations be it weekly, monthly or yearly over a large geographical area like a continent or a sub-continent. The paper also explores utility of AMSR-E soil moisture product (AE_Land 3 product) to understand the soil moisture variations over Indian subcontinent by analysing daily soil moisture data for entire calendar year of 2009. In order to demonstrate the developed methodology the year 2009 was selected wherein a total of 730 AMSR-E daily scenes (365 each for ascending as well as descending passes) were processed and analysed. Although the absolute values of soil moisture derived from AMSR-E are not showing good agreement with soil moisture status on ground which is due to large variability in soil moisture within the coarseresolution cell offered by passive sensors [1] but in general AMSR-E derived soil moisture values are well explained on the basis of rainfall data and agricultural practices adopted in different states of Indian sub-continent. The soundness of the detailed methodology proposed in this paper has been well supported by studying the variations in AMSR-E derived soil moisture with seasonal variations and rainfall data. It has been observed that the soil moisture variations are in line with the seasonal changes as well as the rainfall variations.

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Soil Moisture Estimates From AMSR-E Brightness Temperatures by Using a Dual-Frequency Algorithm

Cited by 116 publications

References 35 publications

Examining the Spatial and Temporal Variability of Soil Moisture in Kentucky Using Remote Sensing Data

Examining the Spatial and Temporal Variability of Soil Moisture in Kentucky Using Remote Sensing Data

Comparison between SAR Soil Moisture Estimates and Hydrological Model Simulations over the Scrivia Test Site

Passive Microwave Remote Sensing of Soil Moisture: A Step-ByStep Detailed Methodology using AMSR-E Data over Indian SubContinent

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