J.G.P. and potential yields. In this research, the

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Last updated: September 7, 2019

J.

G.P. Clever and H.

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J.C. van Leeuwen uses data from remote optical and microwave sensors in combination to monitor crop growth.

They use a simple reflectance model to estimate the leaf area index (LAI) from optical data, and the use of the simple backscatter model to estimate the LAI from radar data. Subsequently, the synergic effect of the use of optical and radar data to estimate IAF was analyzed through the study of different data acquisition scenarios. Finally, remote sensing models were inverted to obtain IAF estimates during the growing season for use in calibrating the crop growth model to actual growth conditions 1. The National Agricultural Statistics Service (NASS) of the US Department of Agriculture UU He conducts field interviews with the operators of the sampled farms and obtains crop cuts to make estimates of crop yields at the regional and state levels. NASS needs additional spatial data that provides timely information on crop status and potential yields. In this research, the EPIC (Erosion Productivity Impact Calculator) model was adapted for simulations at regional scales. The remotely sensed satellite data provide a real-time evaluation of the magnitude and variation of the condition parameters of the crop, and this study investigates the use of these parameters as an input for a crop growth model 2 Hans-Eric Nilsson reviews several applications of remote sensors and image analysis in plant pathology. It describes the technical methods and their possibilities, but also emphasizes the prerequisites and biological restrictions of practical applications 3.

Yichun Xie. et al, use remote sensing images in vegetation mapping. They focus on comparisons of popular remote sensing sensors, commonly adopted image processing methods and the evaluation of prevailing classification accuracy.

Mapping vegetation through remote images involves several processes and techniques of consideration. First they developed the classification of the vegetation to classify and map the cover of the vegetation by means of images obtained by remote sensors, either at the community level or at the 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 to analyze the spatial distribution of certain features in a large landscape. They use both tools to understand the invasive movement of plants 5. Rajesh K Dhumal in the work of identification / differentiation of crops of the same types. They use multispectral and hyperspectral images that contain spectral information about crops. They use supervised and unsupervised classification techniques to map the geographical distribution of optical data cultures and characterize cultivation practices 6.

Kyle W. Freeman uses remote sensing by plant prediction of corn forage biomass and nitrogen uptake in various stages of growth. His research demonstrates that plant information can be collected and used to direct high-resolution used N applications 7. Crop growth simulation models and the remote sensing method have a high potential in monitoring crop growth and yield prediction. However, the crop model has limitations in regional application and remote perception to describe the growth process. Ma Yuping uses the adjusted and regionalized WOFOST model for winter wheat in northern China and coupled through the LAI to the SAIL-PROSPECT model to simulate the soil adjusted vegetation index (SAVI) 8. The EPIC (Erosion Productivity Impact Calculator) model was adopted for simulation at a regional scale.

The remote sensing satellite data provide a real-time evaluation of the magnitude and variation of the condition and parameters of the crops, to investigate the use of these parameters as an input to the crop growth model (Doraiswamy at el) 2 PCM (precision crop management) is an agricultural management, designed to orient crops and soil inputs according to the field requirement to optimize profitability and protect the environment. Progress in PCM has been hampered by the lack of timely and distributed information on crops and soil conditions (M.S. Moran et al) 9. RM Johnston and MM Barson developed simple remote sensing techniques for wetland mapping and monitoring, using Landsat TM siting images

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