Abstract
Successive efforts have processed the Advanced Very High Resolution Radiometer (AVHRR) sensor archive to produce Normalized Difference Vegetation Index (NDVI) datasets (i.e., PAL, FASIR, GIMMS, and LTDR) under different corrections and processing schemes. Since NDVI datasets are used to evaluate carbon gains, differences among them may affect nations’ carbon budgets in meeting international targets (such as the Kyoto Protocol). This study addresses the consistency across AVHRR NDVI datasets in the Iberian Peninsula (Spain and Portugal) by evaluating whether their 1982–1999 NDVI trends show similar spatial patterns. Significant trends were calculated with the seasonal Mann-Kendall trend test and their spatial consistency with partial Mantel tests. Over 23% of the Peninsula (N, E, and central mountain ranges) showed positive and significant NDVI trends across the four datasets and an additional 18% across three datasets. In 20% of Iberia (SW quadrant), the four datasets exhibited an absence of significant trends and an additional 22% across three datasets. Significant NDVI decreases were scarce (croplands in the Guadalquivir and Segura basins, La Mancha plains, and Valencia). Spatial consistency of significant trends across at least three datasets was observed in 83% of the Peninsula, but it decreased to 47% when comparing across the four datasets. FASIR, PAL, and LTDR were the most spatially similar datasets, while GIMMS was the most different. The different performance of each AVHRR dataset to detect significant NDVI trends (e.g., LTDR detected greater significant trends (both positive and negative) and in 32% more pixels than GIMMS) has great implications to evaluate carbon budgets. The lack of spatial consistency across NDVI datasets derived from the same AVHRR sensor archive, makes it advisable to evaluate carbon gains trends using several satellite datasets and, whether possible, independent/additional data sources to contrast.
Highlights
Since the early eighties, the Advanced Very High Resolution Radiometer (AVHRR) sensors onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series have been capturing daily images of the world, providing spectral information to monitor atmospheric, oceanic, vegetation, and land properties of the Earth
We focused on the Iberian Peninsula to compare the 1982–1999 Normalized Difference Vegetation Index (NDVI) trends across four datasets derived from the Global Area Coverage (GAC) archive of the AVHRR sensor (NOAA-7, -9, -11, and -14 satellites; for a comparison of the datasets see Table 1 in this paper, and Table 1 in Baldi et al [4])
Coarse-resolution satellite datasets of the NDVI derived from the AVHRR GAC archive are one of the most valuable sources to evaluate temporal trends of carbon gains at the global, regional, and national scales
Summary
The Advanced Very High Resolution Radiometer (AVHRR) sensors onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series have been capturing daily images of the world, providing spectral information to monitor atmospheric, oceanic, vegetation, and land properties of the Earth. Repeated efforts have processed the GAC archive attempting to produce datasets of consistent time-series of surface reflectance and spectral indices with enough quality to study the long-term dynamics and trends of different properties of the Earth. NDVI is calculated from the reflectance in the AVHRR red (channel 1, 580–680 nm) and near infrared (channel 2, 725–1,100 nm) bands as follows [5,6]: NDVI = (NIR − R)/(NIR + R) This spectral index is strongly related to the fraction of the incoming photosynthetically active radiation intercepted by green vegetation [7] and it is widely and satisfactorily used for monitoring changes in ecosystem structure and function [8], detecting long-term trends in vegetation growth and phenology [9,10], providing inputs for primary production [11] and global circulation [12] models, and providing a reference to model the carbon balance worldwide [13,14,15]. As far as we know, this is the first evaluation of the performance of the new LTDR dataset to detect NDVI trends
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