Seasonal variation in aboveground production and radiation use efficiency of temperate rangelands estimated through remote sensing

PIÑEIRO, GERVASIO; OESTERHELD, MARTÍN; PARUELO, JOSÉ M.

ECOSYSTEMS (NEW YORK. PRINT)

Springer

Año: 2006 vol. 9 p. 357 - 373

1432-9840

ABSTRACT Aboveground net primary production (ANPP) of grasslands varies spatially and temporally. Spectral information provided by remote sensors is a promising new tool that may be able to estimate ANPP in real time and at low cost. The objectives of this study were (a) to evaluate at a seasonal scale the relationship between ANPP and the normalized difference vegetation index (NDVI), (b) to estimate seasonal variations in the coefficient of conversion of absorbed radiation into aboveground biomass (ea ), and (c) to identify the environmental controls on such temporal changes. We used biomass-based field determinations of ANPP for two grassland sites in the Flooding Pampa, Argentina, and related them with NDVI data derived from the NOAA Advanced Very High Resolution Radiometer (AV-HRR) satellites using three different models. Results were compared with data obtained from the new Moderate Resolution Imaging Spectroradiometer (MODIS) sensor at an additional site. The first model was based solely on NDVI; the second was based on the amount of photosynthetically active radiation absorbed by the green vegetation (APARg ), which was derived from NDVI and incoming photosynthetically active radiation (PAR); the third was based on APARg and ea , which was in turn estimated from climatic variables. NDVI explained between 63 and 93% of ANPP variation, depending on the site considered. Estimates of ANPP were not improved by considering the vari-ation in incoming PAR. At both sites, ea varied seasonally (from 0.2 to 1.2 g DM/MJ) and was significantly associated with combinations of pre-cipitation and temperature. Combining ea varia-tions with APARg increased our ability to account for seasonal ANPP variations at both sites. Our re-sults indicate that NDVI produces good, direct estimates of ANPP only if NDVI, PAR, and ea are correlated throughout the seasons. Thus, in most cases, seasonal variations of ea associated with temperature and precipitation must be taken into account to generate seasonal ANPP estimates with acceptable accuracy.ea ), and (c) to identify the environmental controls on such temporal changes. We used biomass-based field determinations of ANPP for two grassland sites in the Flooding Pampa, Argentina, and related them with NDVI data derived from the NOAA Advanced Very High Resolution Radiometer (AV-HRR) satellites using three different models. Results were compared with data obtained from the new Moderate Resolution Imaging Spectroradiometer (MODIS) sensor at an additional site. The first model was based solely on NDVI; the second was based on the amount of photosynthetically active radiation absorbed by the green vegetation (APARg ), which was derived from NDVI and incoming photosynthetically active radiation (PAR); the third was based on APARg and ea , which was in turn estimated from climatic variables. NDVI explained between 63 and 93% of ANPP variation, depending on the site considered. Estimates of ANPP were not improved by considering the vari-ation in incoming PAR. At both sites, ea varied seasonally (from 0.2 to 1.2 g DM/MJ) and was significantly associated with combinations of pre-cipitation and temperature. Combining ea varia-tions with APARg increased our ability to account for seasonal ANPP variations at both sites. Our re-sults indicate that NDVI produces good, direct estimates of ANPP only if NDVI, PAR, and ea are correlated throughout the seasons. Thus, in most cases, seasonal variations of ea associated with temperature and precipitation must be taken into account to generate seasonal ANPP estimates with acceptable accuracy.Rg ), which was derived from NDVI and incoming photosynthetically active radiation (PAR); the third was based on APARg and ea , which was in turn estimated from climatic variables. NDVI explained between 63 and 93% of ANPP variation, depending on the site considered. Estimates of ANPP were not improved by considering the vari-ation in incoming PAR. At both sites, ea varied seasonally (from 0.2 to 1.2 g DM/MJ) and was significantly associated with combinations of pre-cipitation and temperature. Combining ea varia-tions with APARg increased our ability to account for seasonal ANPP variations at both sites. Our re-sults indicate that NDVI produces good, direct estimates of ANPP only if NDVI, PAR, and ea are correlated throughout the seasons. Thus, in most cases, seasonal variations of ea associated with temperature and precipitation must be taken into account to generate seasonal ANPP estimates with acceptable accuracy.Rg and ea , which was in turn estimated from climatic variables. NDVI explained between 63 and 93% of ANPP variation, depending on the site considered. Estimates of ANPP were not improved by considering the vari-ation in incoming PAR. At both sites, ea varied seasonally (from 0.2 to 1.2 g DM/MJ) and was significantly associated with combinations of pre-cipitation and temperature. Combining ea varia-tions with APARg increased our ability to account for seasonal ANPP variations at both sites. Our re-sults indicate that NDVI produces good, direct estimates of ANPP only if NDVI, PAR, and ea are correlated throughout the seasons. Thus, in most cases, seasonal variations of ea associated with temperature and precipitation must be taken into account to generate seasonal ANPP estimates with acceptable accuracy.ea varied seasonally (from 0.2 to 1.2 g DM/MJ) and was significantly associated with combinations of pre-cipitation and temperature. Combining ea varia-tions with APARg increased our ability to account for seasonal ANPP variations at both sites. Our re-sults indicate that NDVI produces good, direct estimates of ANPP only if NDVI, PAR, and ea are correlated throughout the seasons. Thus, in most cases, seasonal variations of ea associated with temperature and precipitation must be taken into account to generate seasonal ANPP estimates with acceptable accuracy.ea varia-tions with APARg increased our ability to account for seasonal ANPP variations at both sites. Our re-sults indicate that NDVI produces good, direct estimates of ANPP only if NDVI, PAR, and ea are correlated throughout the seasons. Thus, in most cases, seasonal variations of ea associated with temperature and precipitation must be taken into account to generate seasonal ANPP estimates with acceptable accuracy.Rg increased our ability to account for seasonal ANPP variations at both sites. Our re-sults indicate that NDVI produces good, direct estimates of ANPP only if NDVI, PAR, and ea are correlated throughout the seasons. Thus, in most cases, seasonal variations of ea associated with temperature and precipitation must be taken into account to generate seasonal ANPP estimates with acceptable accuracy.ea are correlated throughout the seasons. Thus, in most cases, seasonal variations of ea associated with temperature and precipitation must be taken into account to generate seasonal ANPP estimates with acceptable accuracy.ea associated with temperature and precipitation must be taken into account to generate seasonal ANPP estimates with acceptable accuracy. Key words: grasslands; radiation-use efficiency; aboveground net primary production; normalized difference vegetation index (NDVI); NOAA Ad-vanced Very High Resolution Radiometer (AVHRR); Moderate Resolution Imaging Spectroradiometer (MODIS); remote sensing; Argentina.grasslands; radiation-use efficiency; aboveground net primary production; normalized difference vegetation index (NDVI); NOAA Ad-vanced Very High Resolution Radiometer (AVHRR); Moderate Resolution Imaging Spectroradiometer (MODIS); remote sensing; Argentina.