Calibration of MODIS-Derived Cropland Growing Season Using the Climotransfer Function and Ground Observations

Abstract

The global environment experienced notable changes in the recent past of planet Earth. Satellite remote sensing has played an increasingly important role in monitoring and characterizing these changes. Being recognized as a sensitive indicator of global climate change, land surface phenology (LSP) observations by satellite remote sensing have received much attention in recent years; however, much less attention has been paid to the calibration of these observations using standardized procedures. Here, we propose a new approach to calibrating the satellite LSP products by developing a climotransfer function (CTF) based on a polynomial regression of the satellite-ground observation difference in key crop phenophases against climatic factors. We illustrate the model development and evaluation process with a case study of the cropland growing season in Northeast China (NEC) from 2001 to 2010 using the MODIS LSP product MCD12Q2 Collection 6 and the ground-observed crop phenology and climatic data from 98 agrometeorological stations across the region. Our results showed that the start of the cropland growing season (SOS) derived from MODIS data compared well to the ground-observed SOS, whereas the MODIS-derived season end (EOS) was delayed by 15.5 d, relative to ground observation. The MODIS-derived EOS was, therefore, spatiotemporally calibrated using a CTF model fitted to the satellite-ground difference in EOS (∆EOS) versus two climatic factors, namely, the growing degree-days on the base temperature of 10 °C (GDD10) and cloud cover (CL). The calibrated MODIS data revealed that the cropland growing season in NEC tended to shorten at 4.5 d decade−1 during 2001–2010, mainly driven by a significant delay in SOS at a similar rate, whereas no trend was detected for EOS. The calibrated data also revealed a significant shortening gradient of 1.7 d degree−1 of latitude northward. These spatiotemporal patterns would have been erroneously characterized if calibration had not been applied. More attention is therefore called to the proper calibration of satellite LSP products prior to any meaningful applications.

Fourfold Increase in Climate Contributions to Grassland Soil Organic Carbon Variabilities and Its Policy Implications

“…Therefore, involving other categories of factors, especially those easy to obtain and of higher spatial/temporal resolution, is hopefully a promising future direction. These may include soil parameters, such as texture, moisture, microbial biomass, and C:N:P stoichiometry [55,56], and plant traits and productivity proxies, such as AGB, BGB, and NDVI [23,57]. Moreover, in recognizing the lack of interaction-handling mechanisms [40] in our modeling framework, it is desirable to explicitly consider the interactions between predictor variables, especially when a categorical variable is involved, in future model development.…”

Land use intensity controls the diversity-productivity relationship in northern temperate grasslands of China

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