A component-based system for agricultural drought monitoring by remote sensing

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China’s Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring.

[1]  Zhao Shaohua.  Comparison and Application of PDI and MPDI for Drought Monitoring in Inner Mongolia , 2011 .

[2]  Yanbo He,et al.  Assessing relative soil moisture with remote sensing data: theory, experimental validation, and application to drought monitoring over the North China Plain , 2003 .

[3]  Alexei Lyapustin,et al.  Radiative transfer codes for atmospheric correction and aerosol retrieval: intercomparison study. , 2008, Applied optics.

[4]  Xingming Sun,et al.  Segmentation-Based Image Copy-Move Forgery Detection Scheme , 2015, IEEE Transactions on Information Forensics and Security.

[5]  Q. Qin,et al.  Implementation of Perpendicular Drought Index in Remote Sensing Supporting System , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[6]  Vasit Sagan,et al.  Quantitative Remote Sensing of Land Surface Variables: Progress and Perspective , 2019, Remote. Sens..

[7]  Fang Liu,et al.  [The new method monitoring agricultural drought based on SWIR-Red spectrum feature space]. , 2011, Guang pu xue yu guang pu fen xi = Guang pu.

[8]  E. Wood,et al.  Little change in global drought over the past 60 years , 2012, Nature.

[9]  S. Searcy,et al.  An Automated Soil Line Identification Routine for Remotely Sensed Images , 2004 .

[10]  Qiming Qin,et al.  Exploration of the spectral space based on vegetation index and albedo for surface drought estimation , 2007 .

[11]  R. Briefel,et al.  The effectiveness of a short form of the Household Food Security Scale. , 1999, American journal of public health.

[12]  M. S. Moran,et al.  Soil moisture evaluation using multi-temporal synthetic aperture radar (SAR) in semiarid rangeland , 2000 .

[13]  Yan Li,et al.  Drought Mapping Using Two Shortwave Infrared Water Indices with MODIS Data under Vegetated Season , 2015 .

[14]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[15]  A. Aghakouchak,et al.  Global integrated drought monitoring and prediction system , 2014, Scientific Data.

[16]  Qiming Qin,et al.  Photometric correction and reflectance calculation for lunar images from the Chang'E-1 CCD stereo camera. , 2015, Journal of the Optical Society of America. A, Optics, image science, and vision.

[17]  Bin Gu,et al.  A Robust Regularization Path Algorithm for $\nu $ -Support Vector Classification , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Xingming Sun,et al.  Structural Minimax Probability Machine , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[20]  D. Wilhite,et al.  Monitoring the 1996 Drought Using the Standardized Precipitation Index , 1999 .

[21]  Minjie Zhang,et al.  A belief propagation-based method for task allocation in open and dynamic cloud environments , 2017, Knowl. Based Syst..

[22]  D. Deering.  Rangeland reflectance characteristics measured by aircraft and spacecraft sensors , 1979 .

[23]  Zhihua Xia,et al.  Steganalysis of least significant bit matching using multi-order differences , 2014, Secur. Commun. Networks.

[24]  Xingming Sun,et al.  Effective and Efficient Global Context Verification for Image Copy Detection , 2017, IEEE Transactions on Information Forensics and Security.

[25]  Jun Li,et al.  Extraction of bridges over water from high-resolution optical remote-sensing images based on mathematical morphology , 2014 .

[26]  V. Smakhtin,et al.  The use of remote sensing data for drought assessment and monitoring in Southwest Asia. , 2004 .

[27]  J. Dracup,et al.  Drought monitoring , 1991 .

[28]  Naixue Xiong,et al.  Steganalysis of LSB matching using differences between nonadjacent pixels , 2016, Multimedia Tools and Applications.

[29]  Bingfang Wu,et al.  A drought monitoring operational system for China using satellite data: design and evaluation , 2016 .

[30]  Pablo Tamayo,et al.  Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies , 2014, Scientific Data.

[31]  S. Idso,et al.  Canopy temperature as a crop water stress indicator , 1981 .

[32]  E. Wood,et al.  A Drought Monitoring and Forecasting System for Sub-Sahara African Water Resources and Food Security , 2014 .

[33]  João Paulo Ramos Teixeira,et al.  Remote sensing of drought: Progress, challenges and opportunities , 2015 .

[34]  I. Sandholt,et al.  A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status , 2002 .

[35]  David R. Easterling.  Global Data Sets for Analysis of Climate Extremes , 2013 .

[36]  Zhihao Qin,et al.  Remote Sensing of Agro-droughts in Guangdong Province of China Using MODIS Satellite Data , 2008, Sensors.

[37]  Yang Hong,et al.  Bare Surface Soil Moisture Estimation Using Double-Angle and Dual-Polarization L-Band Radar Data , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Xiaodong Liu,et al.  A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment , 2016, Secur. Commun. Networks.

[39]  S. K. McFeeters.  The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[40]  Enhua Wu,et al.  Robust dense reconstruction by range merging based on confidence estimation , 2016, Science China Information Sciences.

[41]  Z. Zhiming,et al.  NIR-red spectral space based new method for soil moisture monitoring , 2007 .

[42]  Inmaculada Pulido-Calvo,et al.  Spatial and temporal variability of droughts in Portugal , 2010 .

[43]  R. Fensholt,et al.  Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment , 2003 .

[44]  A. AghaKouchak,et al.  Trends in meteorological and agricultural droughts in Iran , 2015, Theoretical and Applied Climatology.

[45]  ZhiMing Zhan,et al.  NIR-red spectral space based new method for soil moisture monitoring , 2007 .

[46]  Bin Gu,et al.  Incremental Support Vector Learning for Ordinal Regression , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[47]  Bin Gu,et al.  Incremental learning for ν-Support Vector Regression , 2015, Neural Networks.

[48]  T. Carlson,et al.  A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover , 1994 .

[49]  F. Kogan.  Application of vegetation index and brightness temperature for drought detection , 1995 .

[50]  S. Robinson,et al.  Food Security: The Challenge of Feeding 9 Billion People , 2010, Science.

[51]  Liping Di,et al.  Building an on-demand web service system for Global Agricultural Drought Monitoring and Forecasting , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[52]  Qiming Qin,et al.  The component-based design and development of remote sensing system for drought monitoring , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.