J. G. P. Cle.

And H. J. C. van Leeuwen combine microwave optics and remote sensing data to monitor the growth of the crop. They use a simple reflectance model to estimate leaf area index (LAI) from optical data and a simple backscatter model to estimate LAI from radar data. Subsequently, synergy using optical data and radar data for LAI estimation was analyzed by studying different data collection scenarios. Finally, to calibrate the crop growth model against actual growing conditions, the remote sensing model was reversed between ecosystems to obtain LAI estimates 1.

The National Agricultural Statistics Service (NASS) of the US Department of Agriculture has field interviews with sampled farmers and has obtained crop cuts to estimate crop yields at regional and state levels. NASS requires additional spatial data that provides timely information on crop conditions and potential yields. In this research, we applied the EPIC (Erosion Productivity Impact Calculator) model to regional scale simulation. The satellite remote sensing data provides a real-time evaluation of the magnitude and variation of the crop condition parameters.

This study is considering using these parameters as input to the crop growth model. Includes image analysis in plant pathology. It not only describes the technical method and its possibilities but also highlights the biological preconditions and limitations of practical applications 3. Yichun Xie. Use remote sensing images in vegetation mapping.

They focus on comparing common remote sensing sensors that employ common image processing methods with evaluations with excellent classification accuracy. Remote Sensing Mapping vegetation through images requires a variety of processes and inspection techniques. They first developed vegetation classifications and classified and mapped vegetation covers with remotely sensed images either at the regional or species level 4. Harini Nagendra. Remote sensing applications in GIS and invasive plant monitoring They discussed various applications in this area.

GIS and remote sensing were used to analyze the spatial distribution of specific entities in a vast landscape. They use both tools to understand the invasive plant movement 5. Rajesh K Dhumal is conducting research on the identification / differentiation of the same type of culture. They use multispectral and hyperspectral images that contain spectral information on crops. They map the geographical distribution of the crop’s optical data and use classification techniques not supervised to characterize cultural practice 6.

Kyle W. Freeman uses prediction of corn feed biomass and nitrogen intake at various growth stages by the remote sensing station. His research shows that facility information can be collected and used to drive high resolution N applications 7. Crop growth simulation models and remote sensing techniques have great potential to monitor crop growth and yield prediction.

However, the culture model has limitations on regional application and remote sensing in describing the growth process. Ma Yuping uses a coordinated and localized WOFOST model for North China’s winter wheat, coupled to the SAIL-PROSPECT model through LAI and simulates the ground adjusted vegetation index (SAVI) 8. Erosion Productivity Impact Calculator (EPIC) model is adopted for regional scale simulation. Satellite remote sensing data provides a real-time assessment of the size and variation of crop conditions and parameters (Doraiswamy at El) to study the use of these parameters in the growth model of the crop. PCM (Precision Crop Management) is agricultural management, targeting agricultural crops and soils necessary to maximize profitability and protect the environment.

The progress of GCP has been hampered by the lack of timely information on crop and soil conditions (M.S. Moran et al.) 9. RM Johnstone and MM Burson,


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