Developing geospatial Web service and system for SMAP soil moisture monitoring

Hu, Lei; Di, Liping; Yu, Genong; Yue, Peng; Tang, Jinshan; Lin, Li; Zhang, Chen; Sun, Ziheng; Hu, Ruiheng; Shrestha, Ranjay; Rahman, Md. Shahinoor

doi:10.1109/agro-geoinformatics.2017.8047066

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Many remote-sensing-derived products such as NDVI, MVCI, and EVI are readily available because of the development of computer systems, data cataloging, and dissemination systems. The advanced web application makes dissemination of these data easy, available, and convenient [119,120]. The historical data on crop condition (e.g., VIs) are also available from these large archive systems, which can be utilized to develop crop loss assessment models.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

Rahman

2020

Agriculture

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This article reviews case studies which have used remote sensing data for different aspects of flood crop loss assessment. The review systematically finds a total of 62 empirical case studies from the past three decades. The number of case studies has recently been increased because of increased availability of remote sensing data. In the past, flood crop loss assessment was very generalized and time-intensive because of the dependency on the survey-based data collection. Remote sensing data availability makes rapid flood loss assessment possible. This study groups flood crop loss assessment approaches into three broad categories: flood-intensity-based approach, crop-condition-based approach, and a hybrid approach of the two. Flood crop damage assessment is more precise when both flood information and crop condition are incorporated in damage assessment models. This review discusses the strengths and weaknesses of different loss assessment approaches. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat are the dominant sources of optical remote sensing data for flood crop loss assessment. Remote-sensing-based vegetation indices (VIs) have significantly been utilized for crop damage assessments in recent years. Many case studies also relied on microwave remote sensing data, because of the inability of optical remote sensing to see through clouds. Recent free-of-charge availability of synthetic-aperture radar (SAR) data from Sentinel-1 will advance flood crop damage assessment. Data for the validation of loss assessment models are scarce. Recent advancements of data archiving and distribution through web technologies will be helpful for loss assessment and validation.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

Rahman

2020

Agriculture

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“…• RF-CLASS [81] • • • • FluxDataONE [82] • Mudisshu et al [83] • CLIMSAVE [84] • • Zheng et al [85] • • SMAP [86] •…”

Section: Tool Type Platform and Development Statusmentioning

confidence: 99%

A review of visualisations in agricultural decision support systems: An HCI perspective

Gutiérrez

Htun

Schlenz³

et al. 2019

Computers and Electronics in Agriculture

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Decision Support Systems (DSSs) are used in precision agriculture to provide feedback to a variety of stakeholders, including farmers, advisers, researchers and policymakers. However, increments in the amount of data might lead to data quality issues, and as these applications scale into big, real-time monitoring systems the problem gets even more challenging. Visualisation is a powerful technique used in these systems that provides an indispensable step in assisting end-users to understand and interpret the data. In this paper, we present a systematic review to synthesise literature related to the use of visualisation techniques in the domain of agriculture. The search identified 61 eligible articles, from which we established end-users, visualisation techniques and data collection methods across different application domains. We found visualisation techniques used in various areas of agriculture, including viticulture, dairy farming, wheat production and irrigation management. Our results show that the majority of DSSs utilise maps, together with satellite imagery, as the central visualisation. Also, we observed that there is an excellent opportunity for dashboards to enable end-users with better interaction support to understand the uncertainty of data. Based on this analysis, we provide design guidelines towards the implementation of more interactive and visual DSSs.

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“…Moreover, surface soil moisture is a direct measurement in the top five centimeters of soil column, whereas, root zone soil moisture is the estimated value informed by and consistent with surface soil moisture for one-meter soil below the surface. SMAP L4 surface soil moisture data are downloaded from Geospatial Web Service and System [48] for SMAP soil moisture monitoring. Soil moisture data from five-days before the flood event to five-days after the event are considered to cover the whole flood event in each of the five cases.…”

Section: Data Descriptionmentioning

confidence: 99%

Rapid Flood Progress Monitoring in Cropland with NASA SMAP

Rahman

et al. 2019

Remote Sensing

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Research in different agricultural sectors, including in crop loss estimation during flood and yield estimation, substantially rely on inundation information. Spaceborne remote sensing has widely been used in the mapping and monitoring of floods. However, the inability of optical remote sensing to cloud penetration and the scarcity of fine temporal resolution SAR data hinder the application of flood mapping in many cases. Soil Moisture Active Passive (SMAP) level 4 products, which are model-driven soil moisture data derived from SMAP observations and are available at 3-h intervals, can offer an intermediate but effective solution. This study maps flood progress in croplands by incorporating SMAP surface soil moisture, soil physical properties, and national floodplain information. Soil moisture above the effective soil porosity is a direct indication of soil saturation. Soil moisture also increases considerably during a flood event. Therefore, this approach took into account three conditions to map the flooded pixels: a minimum of 0.05 m3m−3 increment in soil moisture from pre-flood to post-flood condition, soil moisture above the effective soil porosity, and the holding of saturation condition for the 72 consecutive hours. Results indicated that the SMAP-derived maps were able to successfully map most of the flooded areas in the reference maps in the majority of the cases, though with some degree of overestimation (due to the coarse spatial resolution of SMAP). Finally, the inundated croplands are extracted from saturated areas by Spatial Hazard Zone areas (SHFA) of Federal Emergency Management Agency (FEMA) and cropland data layer (CDL). The flood maps extracted from SMAP data are validated with FEMA-declared affected counties as well as with flood maps from other sources.

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Developing geospatial Web service and system for SMAP soil moisture monitoring

Cited by 7 publications

References 9 publications

A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment

A review of visualisations in agricultural decision support systems: An HCI perspective

Rapid Flood Progress Monitoring in Cropland with NASA SMAP

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