J.G.P analysis in plant pathology. He describes technical methods

J.G.P Clever and H.J.C van Leeuwen use optical and microwave
remote sensing data in combination for crop growth monitoring. They use simple
reflectance model to estimate leaf area index(LAI) from optical data, and
simple backscatter model use for estimating LAI from radar data. Subsequently,
the synergistic effect of using both optical and radar data for estimating LAI
was analyzed by studying different data acquisition scenarios. Finally, the
remote sensing models were inverted to obtain LAI estimates during the growing
season for use in calibrating the crop growth model to actual growing
conditions1. The National Agricultural
Statistical Service (NASS) of the U.S. Department of Agriculture conducts field
interviews with sampled farm operators and obtains crop cuttings to make crop
yield estimates at regional and state levels. NASS needs supplemental spatial
data that provides timely information on crop condition and potential yields.
In this research, the crop model EPIC (Erosion Productivity Impact Calculator)
was adapted for simulations at regional scales. Satellite remotely sensed data
provide a real-time assessment of the magnitude and variation of crop condition
parameters, and this study investigates the use of these parameters as an input
to a crop growth model2Hans-Eric Nilsson reviews various applications of remote
sensing and image analysis in plant pathology. He describes technical methods
and their possibilities, but also emphasize the biological prerequisites and
restrictions of practical  applications 3. Yichun Xie. et al, use remote sensing imagery in
vegetation mapping. They  focus on the
comparisons of popular remote sensing sensors, commonly adopted image
processing methods and prevailing classification accuracy assessment. Mapping vegetation through remotely sens
images involve various consideration processes and techniques. They developed
vegetation classification at first to classify and mapping vegetation cover by
remotely sensed images either at community level or species level 4. Harini Nagendra. et al, GIS and remote
sensing application in invasive plant monitoring. They discussed different
applications in this field. GIS and remote sensing used for analyzing the
spatial distribution of certain feature throughout a large landscape. They use
both tools for the understanding of invasive plant movement 5. Rajesh K Dhumal at el work on
identification / differentiation of crops of same types. They use multispectral
and hyper spectral images that contain spectral information about crops. They
use supervise and unsupervised classification techniques to map geographic
distribution of crops optical data and characterize cropping practices 6. Kyle W. Freeman use remote sensing by-plant
prediction of corn forage biomass and nitrogen uptake at various growth stages.
His research demonstrates that by-plant information can be collected and used
to direct used high resolution N applications 7. Crop growth simulation models and remote
sensing method have high potential in crop growth monitoring and yield
prediction. However crop model have limitations in regional application and
remote sensing in describing growth process. Ma Yuping use the WOFOST model
adjusted and regionalize for winter wheat in north china and coupled through
the LAI to the SAIL-PROSPECT model in order to simulate soil adjusted
vegetation index(SAVI)8. The crop model EPIC (Erosion Productivity
Impact Calculator) was adopted for simulation at regional scales. Satellite remotely
sense data provides a real time assessment of the magnitude and variation of
crops condition and parameters, to investigate the use of these parameter an
input to crop growth model (Doraiswamy at el) 2. PCM (precision crop management) is an
agricultural management, designed to target crop and soil inputs according to
within, field requirement to optimize profitability and protect the
environment. Progress in PCM has been hampered by lack of timely, distributed
information on crop and soil conditions (M.S. Moran et al) 9. RM Johnston and MM Barson developed simple
remote sensing techniques for mapping and monitoring wetland, using landsat TM
imagery of inland wetland sites in Victoria and New South Wales. A range of
classification methods are examined in attempt to map the location and extent
of wetlands and their vegetation types 10. C.S.T Daughtry et al evaluate several
spectral indices for measuring crop residue cover using satellite multispectral
and hyper spectral data and to categorize soil tillage intensity in agricultural
fields. Landsat Thematic Mapper (TM) and EO-1 Hyperion imaging spectrometer
data were acquired over agricultural fields in central Iowa in May and June
2004 11. 
Thomas G. Van Niel and Tim R McVicar determine the temporal windows for
highest overall and individual crop discrimination; and compare simple methods
for combining best single-date results to increase overall accuracy12.