Evaluation of Temporal Resolution Effect in Remote Sensing Based Crop Phenology Detection Studies

Abstract

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

Evaluating Heavy Metal Stress Levels in Rice Based on Remote Sensing Phenology

“…Compared with growth ratio, we can gain a more conspicuous discrimination result for three stress levels using base level. The trend in growth rate was consistent with previous studies that heavy metal stress reduced the growth rate of rice, which led to the unfolding of the leaves and the inhibition of radicle growth [ 12 ]. In comparison with the above phenological indicators, the differentiation in length of season was not obvious, as presented in Figure 7 d. We distinguished three stress levels according to the seasonal integral ( Figure 7 e).…”

“…The results showed that the heavier the heavy metal stress is, the smaller the phenological indicator values would be, which can be used to distinguish stress levels. The results can be explained from two aspects: firstly, when the rice is under heavy metal stress, the activity of the enzyme required for chlorophyll formation is inhibited, and chlorophyll content decreased, resulting in chlorosis symptoms in rice [ 9 , 10 , 11 , 12 ], which performed in the NDVI time-series is the reduction of maximum and minimum NDVI values, that may reduce seasonal amplitude, base level, seasonal integral. Secondly, heavy metal stress leads to changes in rice morphology, such as curly leaves and fallen leaves.…”

“…However, heavy metal stress information has not been acquired throughout the entire growth stage, since the spectral data only reflected the stress state of one or several growth stages, which may lead to randomness. Research on the physiological and ecological effects of heavy metal pollution in rice demonstrated that heavy metal poisoning can lead to thin plant growth, short leaves, serious yellowing, and phenological changes [ 9 , 10 , 11 , 12 ]. Phenology is often used as an important indicator of crop yield estimation and field management; the length of the phenological period and growth rate are important factors for crop growth simulation [ 12 , 13 , 14 , 15 , 16 ].…”

“…Research on the physiological and ecological effects of heavy metal pollution in rice demonstrated that heavy metal poisoning can lead to thin plant growth, short leaves, serious yellowing, and phenological changes [ 9 , 10 , 11 , 12 ]. Phenology is often used as an important indicator of crop yield estimation and field management; the length of the phenological period and growth rate are important factors for crop growth simulation [ 12 , 13 , 14 , 15 , 16 ]. The phenological information extracted using remote sensing technology not only reflects the continuous growth and stress state of rice throughout the entire growth stage, but also avoids uncertainty of selecting the spectral parameters most sensitive to physiological elements.…”

A logistic-based method for rice monitoring from multitemporal MODIS-Landsat fusion data

“…The phenological transition events (i.e., greenup onset, maturity onset, senescence onset, and greenness offset) corresponding to the times at which the rate of change in curvature of the EVI2 fitted using logistic models exhibiting local minima or maximums can be identified using derivative algorithms [Zhang et al, 2003]. A number of studies have been conducted to monitor vegetation and crop phenology at regional to global scales from satellite-based vegetation indices (e.g., NDVI and EVI) using the double logistic algorithm over the past decade [Zhang et al, 2003;Beck et al, 2006;Wardlow et al, 2006;Julien and Sobrino, 2009;Zhao et al, 2012]. In this study, the time-series EVI2 data were assumed to characterize the temporal responses of rice crop phenology through the growing season, following a typical "S" curve behavior of the logistic model and the key phenological growth stages of rice plants can be identified using derivative algorithms.…”