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Part 1: GIS, GPS, RS and Precision FarmingInternational audienceRemote sensing based phenology detection method has been employed to study agriculture, forestry and other vegetations for its potential to reflect the variations in climate change. These studies usually utilized time series Normalized Difference Vegetation Index (NDVI) generated from various sensors through a Maximum Value Compositing (MVC) process, which minimized the contamination from cloud and simultaneously introduce degradation of temporal accuracy. In this study, we assess the impact of temporal resolution on crop phenology derivation researches by comparing three different Moderate Resolution Imaging Spectroradiometer (MODIS) datasets: daily surface reflectance, 8 day composited surface reflectance and 16 day composited NDVI. The surface reflectance data were first filtered by employing auxiliary data which contained quality and viewing geometry information, and then used to calculate NDVI with specific date. A least square method was taken to fit the survival data points to double logistic function. And finally, seven time-related metrics were obtained and matched with field observation crop phenology stages. These remote sensing derivate phenology dates were compared to National Agricultural Statistics Service (NASS) weekly crop progress reports to evaluate the capability of these datasets in temporal sensitive studies. The results illustrated that daily surface reflectance datasets were the most accurate source for time-sensitive studies. However, extra ancillary datum and appropriate denoising techniques should be applied to reconstruct the time series curve. Phenology matching process is a necessary step before detecting phenological information from remote sensing imagery for specific land cover type since same phenological stages of different crop types might have different counterparts on time series curve

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Using remote sensing data to estimate crop area is efficient to a wide range of end-users, including government agencies, farmers and researchers. Moderate spatial resolution (MSR) image data are widely used to estimate crop area. But its accuracy can't meet the demands of precision. Spatial sampling techniques integrated the strengths of remote sensing and sampling survey are being widely used. This method need large sample size which is cannot be guaranteed by remote sensing due to weather. The Unmanned Aerial Vehicle (UAV) can be used as an effective way to guarantee enough sample size. This paper proposed a spatial sampling method using MSR image classification results and UAV transects, a stratified random sampling method was proposed, area-scale (from MSR image classification) was used as auxiliary variable to guide the distribution of UAV transects, which had proved that 2% sampling ratio can make the crop area estimation accuracy more than 95% with a 95% confidence interval.

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