Remote Sensing of Soil Moisture Using Airborne Hyperspectral Data

Finn, Michael P.; Lewis, Mark; Bosch, David D.; Giraldo, Mauricio; Yamamoto, Kristina H.; Sullivan, D. G.; Kincaid, Russell; Luna, Ronaldo; Allam, Gopala Krishna; Kvien, C. K.; Williams, Michael S.

doi:10.2747/1548-1603.48.4.522

Cited by 63 publications

(28 citation statements)

References 34 publications

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“…To this end, ensemble weather forecasts (Molteni et al, 1996) will help assessing a reasonable range of uncertainty for forthcoming hours and days. To reduce uncertainty with respect to simulated water contents, soil wetness patterns can be determined by remote sensing (Finn et al, 2011) or using wireless networks of low-cost water content sensors (Bogena et al, 2007). There is evidence that the spatial patterns at larger scale, including macro-permeability and the local hydrogeology, are key factors for the initiation of landslides.…”

Section: Assessing and Reducing Uncertainty Related To The Triggeringmentioning

confidence: 99%

Monitoring and prediction in early warning systems for rapid mass movements

Stähli

Sättele²,

Huggel

et al. 2015

Nat. Hazards Earth Syst. Sci.

120

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Abstract. Rapid mass movements (RMM) pose a substantial risk to people and infrastructure. Reliable and cost-efficient measures have to be taken to reduce this risk. One of these measures includes establishing and advancing the state of practice in the application of early warning systems (EWSs). EWSs have been developed during the past decades and are rapidly increasing. In this paper, we focus on the technical part of EWSs, i.e., the prediction and timely recognition of imminent hazards, as well as on monitoring slopes at risk and released mass movements. Recent innovations in assessing spatial precipitation, monitoring and precursors of the triggering and deformation of RMM offer new opportunities for next-generation EWSs. However, technical advancement can only be transferred into more reliable, operational EWSs with an adequate well-instructed dedicated staff. To this end, an intense dialog between scientists, engineers and those in charge of warning, as well as further experience with new comprehensive prototype systems jointly operated by scientists and practitioners, will be essential.

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Section: Assessing and Reducing Uncertainty Related To The Triggeringmentioning

confidence: 99%

Monitoring and prediction in early warning systems for rapid mass movements

Stähli

Sättele²,

Huggel

et al. 2015

Nat. Hazards Earth Syst. Sci.

120

View full text Add to dashboard Cite

show abstract

“…To help monitor crops precisely and instantaneously, spatial sensor webs have been widely used for comprehensive and persistent SM monitoring and disaster-warning applications in land, ocean, atmosphere, and ecological environments. The monitoring of SM with sensors, commonly aboard satellites, is an active research area [10,11]. In addition to the use of in situ sensors in SM monitoring [12,13], Earth Observation data (EOD) are also widely applied in SM monitoring [14,15].…”

Section: Sensor Web and Soil Moisture (Sm) Monitoring In Precision Agmentioning

confidence: 99%

A Cloud Computing-Enabled Spatio-Temporal Cyber-Physical Information Infrastructure for Efficient Soil Moisture Monitoring

Zhou

Chen

2016

IJGI

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Abstract:Comprehensive surface soil moisture (SM) monitoring is a vital task in precision agriculture applications. SM monitoring includes remote sensing imagery monitoring and in situ sensor-based observational monitoring. Cloud computing can increase computational efficiency enormously. A geographical web service was developed to assist in agronomic decision making, and this tool can be scaled to any location and crop. By integrating cloud computing and the web service-enabled information infrastructure, this study uses the cloud computing-enabled spatio-temporal cyber-physical infrastructure (CESCI) to provide an efficient solution for soil moisture monitoring in precision agriculture. On the server side of CESCI, diverse Open Geospatial Consortium web services work closely with each other. Hubei Province, located on the Jianghan Plain in central China, is selected as the remote sensing study area in the experiment. The Baoxie scientific experimental field in Wuhan City is selected as the in situ sensor study area. The results show that the proposed method enhances the efficiency of remote sensing imagery mapping and in situ soil moisture interpolation. In addition, the proposed method is compared to other existing precision agriculture infrastructures. In this comparison, the proposed infrastructure performs soil moisture mapping in Hubei Province in 1.4 min and near real-time in situ soil moisture interpolation in an efficient manner. Moreover, an enhanced performance monitoring method can help to reduce costs in precision agriculture monitoring, as well as increasing agricultural productivity and farmers' net-income.

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“…Despite these drawbacks, there is a real interest to estimate the SMC from such sensors [41]. Several papers describe soil surface moisture estimation using airborne data [42][43][44][45]. Haubrock et al [46] show a clear connection between their image based data retrieved by using the Normalised Soil Moisture Index (NSMI) and in-situ measurements of top layer soil moisture.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

et al. 2015

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Abstract:The aim of this work is to study the constraints and performance of SMC retrieval methodologies in the VNIR (Visible-Near InfraRed) and SWIR (ShortWave InfraRed) regions (from 0.4 to 2.5 µm) when passing from controlled laboratory conditions to field conditions. Five different approaches of signal processing found in literature were considered. Four local criteria are spectral indices (WISOIL, NSMI, NINSOL and NINSON). These indices are the ratios between the spectral reflectances acquired at two specific wavelengths to characterize moisture content in soil. The last criterion is based in the convex hull concept and it is a global method, which is based on the analysis of the full spectral signature of the soil. The database was composed of 464 and 9 spectra, respectively, measured over bare soils in laboratory and in-situ. For each measurement, SMC and texture were well-known and the database was divided in two parts dedicated to calibration and validation steps. The calibration part was used to define the empirical relation between SMC and SMC retrieval approaches, with coefficients of determination (R 2 ) between 0.72 and 0.92. A clay content (CC) dependence was detected for the NINSOL and NINSON indices. Consequently, two new criteria were proposed taking into account the CC contribution (NINSOLCC and NINSONCC). The well-marked regression between SMC and global/local indices, and the interest of using the CC, were confirmed during the validation step using laboratory data (R² in all cases) and using in-situ data, where WISOIL, NINSOLCC and NINSONCC criteria stand out among the NSMI and CH.

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Remote Sensing of Soil Moisture Using Airborne Hyperspectral Data

Cited by 63 publications

References 34 publications

Monitoring and prediction in early warning systems for rapid mass movements

Monitoring and prediction in early warning systems for rapid mass movements

A Cloud Computing-Enabled Spatio-Temporal Cyber-Physical Information Infrastructure for Efficient Soil Moisture Monitoring

Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments

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