Evaluation of Temporal Resolution Effect in Remote Sensing Based Crop Phenology Detection Studies. Zhao, H., Yang, Z., Di, L., & Pei, Z. In Li, D. & Chen, Y., editors, Computer and Computing Technologies in Agriculture V, of IFIP Advances in Information and Communication Technology, pages 135–150, 2012. Springer Berlin Heidelberg. abstract bibtex Remote 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.
@inproceedings{zhao_evaluation_2012,
series = {{IFIP} {Advances} in {Information} and {Communication} {Technology}},
title = {Evaluation of {Temporal} {Resolution} {Effect} in {Remote} {Sensing} {Based} {Crop} {Phenology} {Detection} {Studies}},
isbn = {978-3-642-27278-3},
abstract = {Remote 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.},
language = {en},
booktitle = {Computer and {Computing} {Technologies} in {Agriculture} {V}},
publisher = {Springer Berlin Heidelberg},
author = {Zhao, Hu and Yang, Zhengwei and Di, Liping and Pei, Zhiyuan},
editor = {Li, Daoliang and Chen, Yingyi},
year = {2012},
keywords = {Crop phenology, double logistic function fitting, least square, temporal resolution evaluation},
pages = {135--150},
file = {Springer Full Text PDF:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/ZIZQJNSZ/Zhao et al. - 2012 - Evaluation of Temporal Resolution Effect in Remote.pdf:application/pdf}
}
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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. 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