Estimating large area wheat evapotranspiration from remote sensing data

2004 ◽  
Author(s):  
J. Garatuza-Payan ◽  
A. Tamayo ◽  
C. Watts ◽  
J.C. Rodriguez
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Large Area ◽  
Sensing Data

scholarly journals STUDY ON REMOTE SENSING MONITORING MODEL OF AGRICULTURAL DROUGHT BASED ON RANDOM FOREST DEVIATION CORRECTION

2021 ◽  
pp. 413-422
Author(s):  
Shao Li ◽  
Xia Xu
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Data Sources ◽  
Drought Monitoring ◽  
Monitoring Methods ◽  
Large Area ◽  
Sensing Data ◽  
Monitoring Model

Using remote sensing data to monitor large area drought is one of the important methods of drought monitoring at present. However, the traditional remote sensing drought monitoring methods mainly focus on monitoring single drought response factors such as soil moisture or vegetation status, and the research on comprehensive multi-factor drought monitoring is limited. In order to improve the ability to resist drought events, this paper takes Henan Province of China as an example, takes multi-source remote sensing data as data sources, considers various disaster-causing factors, adopts random forest method to model, and explores the method of regional remote sensing comprehensive drought monitoring using various remote sensing data sources. Compared with neural network, classification regression tree and linear regression, the performance of random forest is more stable and tolerant to noise and outliers. In order to provide a new method for comprehensive assessment of regional drought, a comprehensive drought monitoring model was established based on multi-source remote sensing data, which comprehensively considered the drought factors such as soil water stress, vegetation growth status and meteorological precipitation profit and loss in the process of drought occurrence and development.

scholarly journals Towards a continuous inland excess water flood monitoring system based on remote sensing data

2017 ◽  
Vol 10 (3-4) ◽  
pp. 9-15 ◽  
Author(s):  
Boudewijn van Leeuwen ◽  
Zalán Tobak ◽  
Ferenc Kovács ◽  
György Sipos
Keyword(s):  
Remote Sensing ◽  
Agricultural Sector ◽  
Remote Sensing Data ◽  
Large Area ◽  
Sensing Data ◽  
Excess Water ◽  
Sufficient Detail ◽  
Flood Monitoring ◽  
First Results ◽  
Hungarian Plain

Abstract Inland excess water (IEW) is a type of flood where large flat inland areas are covered with water during a period of several weeks to months. The monitoring of these floods is needed to understand the extent and direction of development of the inundations and to mitigate their damage to the agricultural sector and build up infrastructure. Since IEW affects large areas, remote sensing data and methods are promising technologies to map these floods. This study presents the first results of a system that can monitor inland excess water over a large area with sufficient detail at a high interval and in a timely matter. The methodology is developed in such a way that only freely available satellite imagery is required and a map with known water bodies is needed to train the method to identify inundations. Minimal human interference is needed to generate the IEW maps. We will present a method describing three parallel workflows, each generating separate maps. The maps are combined to one weekly IEW map. At this moment, the method is capable of generating IEW maps for a region of over 8000 km2, but it will be extended to cover the whole Great Hungarian Plain, and in the future, it can be extended to any area where a training water map can be created.

The Intelligent Classifier’s Building of Typical Vegetation Based on Hyper-Spectral Data

2014 ◽  
Vol 1073-1076 ◽  
pp. 1972-1976
Author(s):  
Jie Zhang ◽  
Hao Yan Zhao ◽  
Min Xia Zhang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Dynamic Monitoring ◽  
Large Area ◽  
Desert Vegetation ◽  
Sensing Data ◽  
Monitoring Model ◽  
Hyper Spectral ◽  
Bp Neural Network Model ◽  
Intelligent Process

With 3S comprehensive analysis on vegetation and the further development of hyper-spectral technology, the dynamic monitor of large area vegetation in long-term has become the trend. Intelligent process, combined the remote sensing data and field data, constructing dynamic monitoring model, plays an important guilding role in ecological security and balance. By using hyper-spectral remote sensing data of desert vegetation, three groups of spectral characteristic parameters were selected as input data of typical desert vegetation in the research, and vegetation types were selected as output data. Typical vegetation classifier was constructed based on the BP neural network model to study the vegetation classification.

scholarly journals Soil Salinity Inversion in Coastal Corn Planting Areas by the Satellite-UAV-Ground Integration Approach

2021 ◽  
Vol 13 (16) ◽  
pp. 3100
Author(s):  
Guanghui Qi ◽  
Chunyan Chang ◽  
Wei Yang ◽  
Peng Gao ◽  
Gengxing Zhao
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Large Scale ◽  
Yellow River ◽  
Remote Sensing Data ◽  
Soil Salinization ◽  
Large Area ◽  
Sensing Data ◽  
Inversion Model ◽  
Uav Image

Soil salinization is a significant factor affecting corn growth in coastal areas. How to use multi-source remote sensing data to achieve the target of rapid, efficient and accurate soil salinity monitoring in a large area is worth further study. In this research, using Kenli District of the Yellow River Delta as study area, the inversion of soil salinity in a corn planting area was carried out based on the integration of ground imaging hyperspectral, unmanned aerial vehicles (UAV) multispectral and Sentinel-2A satellite multispectral images. The UAV and ground images were fused, and the partial least squares inversion model was constructed by the fused UAV image. Then, inversion model was scaled up to the satellite by the TsHARP method, and finally, the accuracy of the satellite-UAV-ground inversion model and results was verified. The results show that the band fusion of UAV and ground images effectively enrich the spectral information of the UAV image. The accuracy of the inversion model constructed based on the fused UAV images was improved. The inversion results of soil salinity based on the integration of satellite-UAV-ground were highly consistent with the measured soil salinity (R2 = 0.716 and RMSE = 0.727), and the inversion model had excellent universal applicability. This research integrated the advantages of multi-source data to establish a unified satellite-UAV-ground model, which improved the ability of large-scale remote sensing data to finely indicate soil salinity.

scholarly journals SEA ICE THICKNESS MEASUREMENT BY GROUND PENETRATING RADAR FOR GROUND TRUTH OF MICROWAVE REMOTE SENSING DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 1259-1262
Author(s):  
M. Matsumoto ◽  
M. Yoshimura ◽  
K. Naoki ◽  
K. Cho ◽  
H. Wakabayashi
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Ground Truth ◽  
Ice Thickness ◽  
Large Area ◽  
Sea Ice Thickness ◽  
Sensing Data ◽  
Ground Penetrating

Observation of sea ice thickness is one of key issues to understand regional effect of global warming. One of approaches to monitor sea ice in large area is microwave remote sensing data analysis. However, ground truth must be necessary to discuss the effectivity of this kind of approach. The conventional method to acquire ground truth of ice thickness is drilling ice layer and directly measuring the thickness by a ruler. However, this method is destructive, time-consuming and limited spatial resolution. Although there are several methods to acquire ice thickness in non-destructive way, ground penetrating radar (GPR) can be effective solution because it can discriminate snow-ice and ice-sea water interface. In this paper, we carried out GPR measurement in Lake Saroma for relatively large area (200 m by 300 m, approximately) aiming to obtain grand truth for remote sensing data. GPR survey was conducted at 5 locations in the area. The direct measurement was also conducted simultaneously in order to calibrate GPR data for thickness estimation and to validate the result. Although GPR Bscan image obtained from 600MHz contains the reflection which may come from a structure under snow, the origin of the reflection is not obvious. Therefore, further analysis and interpretation of the GPR image, such as numerical simulation, additional signal processing and use of 200 MHz antenna, are required to move on thickness estimation.

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