A methodology to test the pertinence of remote-sensing data assimilation into vegetation models for water and energy exchange at the land surface

Agronomie ◽  
2004 ◽  
Vol 24 (4) ◽  
pp. 197-204 ◽  
Author(s):  
Jennifer Pellenq ◽  
Gilles Boulet
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Land Surface ◽  
Energy Exchange ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Vegetation Models

Progress in integrating remote sensing data and hydrologic modeling

2014 ◽  
Vol 38 (4) ◽  
pp. 464-498 ◽  
Author(s):  
Xiaoyong Xu ◽  
Jonathan Li ◽  
Bryan A. Tolson
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Land Surface ◽  
Hydrologic Modeling ◽  
Snow Water Equivalent ◽  
Research Effort ◽  
Remote Sensing Data ◽  
Area Index ◽  
Sensing Data ◽  
The Past

Remote sensing and hydrologic modeling are two key approaches to evaluate and predict hydrology and water resources. Remote sensing technologies, due to their ability to offer large-scale spatially distributed observations, have opened up new opportunities for the development of fully distributed hydrologic and land-surface models. In general, remote sensing data can be applied to land-surface and hydrologic modeling through three strategies: model inputs (basin information, boundary conditions, etc.), parameter estimation (model calibration), and state estimation (data assimilation). There has been an intensive global research effort to integrate remote sensing and land/hydrologic modeling over the past few decades. In particular, in recent years significant progress has been made in land/hydrologic remote sensing data assimilation. Hence there is a demand for an up-to-date review on these efforts. This paper presents an overview of research efforts to combine hydrologic/land models and remote sensing products (mainly including precipitation, surface soil moisture, snow cover, snow water equivalent, leaf area index, and evapotranspiration) over the past decade. This paper also discusses the major challenges remaining in this field, and recommends the directions for further research efforts.

Research on LAI Retrieving Applied for Virtual Reality Simulation of Vegetation

2012 ◽  
Vol 263-266 ◽  
pp. 1473-1477
Author(s):  
Tong Pan ◽  
Xiao Jing Li ◽  
Hao Peng Wang ◽  
Kai Zhao
Keyword(s):  
Remote Sensing ◽  
Virtual Reality ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Virtual Reality Simulation ◽  
Vegetation Model ◽  
Sensing Data ◽  
Vegetation Models ◽  
Northeast Of China ◽  
The Relationship

It is the key process for developing usability of virtual reality simulation of vegetation taking into account soil, chemistry elements of water, temperature and humidity of land surface, and lighting. Retrieving the characteristics of vegetation from remote sensing data effectively and exactly, building the relationship between remote sensing data and interfaces of VR-vegetation models are important. The paper discusses dense vegetation model, nondense vegetation model, and two-stream approximation model applied for LAI retrieving, analyzes the efficiency of the methods; presents the process and method of LAI retrieving based on FPAR. The research offers the key methods and theoretic support for virtual reality simulation of vegetation based on spatial remote sensing information. The research is the primary work of digital agriculture, and important for monitoring corn growth and estimating in northeast of China.

Retrieving accurate soil moisture over the Tibetan Plateau using multi-source remote sensing data assimilation with simultaneous state and parameter estimations

2021 ◽  
Author(s):  
Weijing Chen ◽  
Chunlin Huang ◽  
Zong-Liang Yang ◽  
Ying Zhang
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Tibetan Plateau ◽  
Data Assimilation ◽  
Brightness Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Accurate Estimation ◽  
The Tibetan Plateau ◽  
Sensing Data

AbstractData assimilation provides a practical way to improve the accuracy of soil moisture simulation by integrating a land surface model and satellite data. This study establishes a multi-source remote sensing data assimilation framework by incorporating a simultaneous state and parameter estimation method to acquire an accurate estimation of the soil moisture over the Tibetan Plateau. The brightness temperature of the Advanced Microwave Scanning Radiometer 2 (AMSR2) is directly assimilated into the coupled system of the Common Land Model (CoLM) and a microwave radiative transfer model (RTM) to improve the soil moisture simulation. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature product and the Beijing Normal University (BNU) leaf area index product are employed to not only improve the estimation of temperature and vegetation variables from the CoLM, but also provide more accurate background information for the RTM during the brightness temperature assimilation. In situ measurements from the Naqu network are used to evaluate the results. The model simulation showed an obvious underestimation of soil moisture and overestimation of soil temperature, which was alleviated by the assimilation experiments, particularly in the shallow soil layers. The estimated parameters also showed advantages in the soil moisture simulation when compared with the default parameters. The assimilation experiment presents promising results in the combination of model and multi-source remote sensing data for estimating soil moisture over the complex mountainous region in Tibet.

Study on the Application and Comparative Analysis of Land Surface Temperature Retrieval Method Based on Multi-Sensor Remote Sensing Data

2014 ◽  
Vol 1010-1012 ◽  
pp. 1276-1279 ◽  
Author(s):  
Yin Tai Na
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Urban Heat ◽  
Split Window Algorithm ◽  
Temperature Retrieval

The three commonly used remote sensing land surface temperature retrieval methods are described, namely single-window algorithm, split window algorithm and multi-channel algorithm, which have their advantages and disadvantages. The land surface temperature (LST) of study area was retrieved with multi-source remote sensing data. LST of study area was retrieved with the split window algorithm on January 10, 2003 and November 19, 2003 which is comparatively analyzed with the LST result of ETM+data with the single-window algorithm and the LST result of ASTER data with multi channel algorithm in the same period. The results show that land surface temperature of different land features are significantly different, where the surface temperature of urban land is the highest, and that of rivers and lakes is the lowest, followed by woodland. It is concluded that the expansion of urban green space and protection of urban water can prevent or diminish the urban heat island.

scholarly journals Utilization of Multi-Temporal Microwave Remote Sensing Data within a Geostatistical Regionalization Approach for the Derivation of Soil Texture

2020 ◽  
Vol 12 (16) ◽  
pp. 2660
Author(s):  
Philip Marzahn ◽  
Swen Meyer
Keyword(s):  
Remote Sensing ◽  
Soil Texture ◽  
Land Surface ◽  
Temporal Frequency ◽  
Management Strategies ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Test Site ◽  
Soil Parameters ◽  
Sensing Data

Land Surface Models (LSM) have become indispensable tools to quantify water and nutrient fluxes in support of land management strategies or the prediction of climate change impacts. However, the utilization of LSM requires soil and vegetation parameters, which are seldom available in high spatial distribution or in an appropriate temporal frequency. As shown in recent studies, the quality of these model input parameters, especially the spatial heterogeneity and temporal variability of soil parameters, has a strong effect on LSM simulations. This paper assesses the potential of microwave remote sensing data for retrieving soil physical properties such as soil texture. Microwave remote sensing is able to penetrate in an imaged media (soil, vegetation), thus being capable of retrieving information beneath such a surface. In this study, airborne remote sensing data acquired at 1.3 GHz and in different polarization is utilized in conjunction with geostatistics to retrieve information about soil texture. The developed approach is validated with in-situ data from different field campaigns carried out over the TERENO test-site “North-Eastern German Lowland Observatorium”. With the proposed approach a high accuracy of the retrieved soil texture with a mean RMSE of 2.42 (Mass-%) could be achieved outperforming classical deterministic and geostatistical approaches.

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