Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories

Abstract

Abstract:In the Indian monsoon region, frequent cloud cover in the rainy season results in less valid satellite observations during the vegetation growth period, making it difficult to extract land surface phenology (LSP). Even worse, many valid but humid observations were misidentified as clouds in the MODIS cloud mask, causing severe gaps in the LSP product. Using a refined cloud detection approach to separate clear-sky and cloudy observations, this study found that potentially valid observations during the vegetation growth period could be identified. Furthermore, the varied vegetation growth trajectories cannot be well-fitted by a global curve-fitting approach, but can be modelled by using the locally adjusted cubic-spline capping approach, which performed well for any seasonal patterns. Applying this approach, the start of growing season (SOS) was determined with 9.18% of vegetation growth amplitude between the maximum and minimum NDVI to generate the SOS product (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016). The valid percentage of this regional product largely increased from 29.30% to 69.76% compared with the MCD12Q2 product, and its reliability was approximate to that of deciduous broadleaf forest in North America and Europe. This product could serve as a basis for understanding the response of terrestrial ecosystems to the changing Indian monsoon.

Evaluation of Vegetation Indices and Phenological Metrics Using Time-Series MODIS Data for Monitoring Vegetation Change in Punjab, Pakistan

“…The difference between histogram dispersals of phenological metrics of estimated VIs may be due to their varying sensitivities to soil context variations. Likewise, in the drylands of Arizona, USA, the spatiotemporal variability of soil surface phenology removed from NDVI was higher than EVI [61]. The difference in highest greenness between NDVI and EVI was related to the physiological features of the vegetation types due to their different sensitivities [36].…”

Status of Phenological Research Using Sentinel-2 Data: A Review

“…The accuracy of cloud masks is critical to minimise errors in processing of time series data from satellites. Faulty cloud masks have been shown to deviate the estimated start of season by up to 10 days [142] and hence require careful consideration in modelling of vegetation index time series curves. This is particularly important for the Sentinel-2 dataset, since Pastick et al (2018) [62] showed that the cloud masks available with the HLS, which uses the popular LaSRC and Fmask algorithms, were of poor quality (accuracy of 76-89%) and had to be recalculated from Landsat-8 and Sentinel 2 data using decision trees (yielding an accuracy of 99%).…”

Assessment of Vegetation Phenological Extractions Derived From Three Satellite-Derived Vegetation Indices Based on Different Extraction Algorithms Over the Tibetan Plateau

“…The Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data provide the possibility to monitor vegetation phenology and have been increasingly used for monitoring vegetation phenology (Zhang et al, 2004;Wang et al, 2015b;Shang et al, 2018). MODIS sensors aboard Terra and Aqua satellites have been in operation since 1999 and 2002, respectively, and can provide long-term remote sensing NDVI and enhanced vegetation index (EVI) records of >10 years (Wang et al, 2021;Zhu et al, 2021).…”