Geo-spatial analysis of the temporal trends of kharif crop phenology metrics over India and its relationships with rainfall parameters

Abstract

The Global Inventory Modeling and Mapping Studies bimonthly Normalized Difference Vegetation Index (NDVI) data of 8 × 8 km spatial resolution for the period of 1982-2006 were analyzed to detect the trends of crop phenology metrics (start of the growing season (SGS), seasonal NDVI amplitude (AMP), seasonally integrated NDVI (SiNDVI)) during kharif season (June to October) and their relationships with the amount of rainfall and the number of rainy days over Indian subcontinent. Direction and magnitude of trends were analyzed at pixel level using the Mann-Kendall test and further assessed at meteorological subdivision level using field significance test (α = 0.1). Significant pre-occurrence of the SGS was observed over northern (Punjab, Haryana) and central (Marathwada, Vidarbha and Madhya Maharashtra) parts, whereas delay was found over southern (Rayalaseema, Coastal Andhra Pradesh) and eastern (Bihar, Gangetic West Bengal and Sub-Himalayan West Bengal) parts of India. North, west, and central India showed significant increasing trends of SiNDVI, corroborating the kharif food grain production performance during the time frame. Significant temporal correlation (α = 0.1) between the rainfall/number of rainy days and crop phenology metrics was observed over the rainfed region of India. About 35-40 % of the study area showed significant correlation between the SGS and the rainfall/number of rainy days during June to August. June month rainfall/number of rainy days was found to be the most sensitive to the SGS. The amount of rainfall and the number of rainy days during monsoon were found to have significant influence over the SiNDVI in 24-30 % of the study area. The crop phenology metrics had significant correlation with the number of rainy days over the larger areas than that of the rainfall amount.

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

“…In the Indian monsoon region, the growth of vegetation is mainly controlled by precipitation [6,7], which has experienced significant changes in intensity and frequency over the past half-century [8][9][10]; these changes will certainly cause shifts in vegetation phenology, such as a significant advance in the start of growing season (SOS) in northern parts (e.g., Punjab, Haryana) of India [11]. Therefore, accurate depictions of vegetation phenology in the Indian monsoon region are urgently needed.…”

“…However, Fourier would appear as spurious oscillations in the long stable part of the vegetation growth trajectory. By using the SG filter to smooth the GIMMS NDVI data, Chakraborty, Seshasai, and Dadhwal [11] extracted cropland phenology in India with 20% of vegetation growth amplitude (shortened to VGA) between the minimum and maximum NDVI; Duncan, et al [20] extracted cropland phenology across the Indo-Gangetic Plains with 30% of VGA. However, SG was reported to be highly sensitive to noises and large successive gaps [21,22], which would influence the extracted LSP.…”

Diurnal Difference Vegetation Water Content (ddVWC) of Advance Microwave Scanning Radiometer-Earth Observing System (AMSR-E) for assessment of crop water stress at regional level

“…The TIMESAT software was used to generate smooth time series of NDVI using Savitzky-Golay filter. The details of the smoothening technique over the diverse agricultural area across India are available in Chakraborty and Seshasai (2014).…”

Synergistic Use of Radar and Optical Satellite Data for Improved Monsoon Cropland Mapping in India

“…Shang et al [29] applied refined cloud detection approach using MODIS data to separate cloudy scenes from the clear ones to model vegetation growth across India. Similarly, Chakraborty et al [30] measured the crop phenology trend of the monsoon cropland vegetation and its relationship with rainfall using the Global Inventory Modelling and Mapping Studies (GIMMS) datasets for all of India. Yet, the high revisit frequency of optical satellites such as Landsat (16-day) and Sentinel-2 (5-day) is not enough for effective mapping of monsoon cropland over large area.…”