Using APAR to Predict Aboveground Plant Productivity in Semi-Arid Rangelands: Spatial and Temporal Relationships Differ

Abstract

Monitoring of aboveground net primary production (ANPP) is critical for effective management of rangeland ecosystems but is problematic due to the vast extent of rangelands globally, and the high costs of ground-based measurements. Remote sensing of absorbed photosynthetically active radiation (APAR) can be used to predict ANPP, potentially offering an alternative means of quantifying ANPP at both high temporal and spatial resolution across broad spatial extents. The relationship between ANPP and APAR has often been quantified based on either spatial variation across a broad region or temporal variation at a location over time, but rarely both. Here we assess: (i) if the relationship between ANPP and APAR is consistent when evaluated across time and space; (ii) potential factors driving differences between temporal versus spatial models, and (iii) the magnitude of potential errors relating to space for time transformations in quantifying productivity. Using two complimentary ANPP datasets and remotely sensed data derived from MODIS and a Landsat/MODIS fusion data product, we find that slopes of spatial models are generally greater than slopes of temporal models. The abundance of plant species with different structural attributes, specifically the abundance of C4 shortgrasses with prostrate canopies versus taller, more productive C3 species with more vertically complex canopies, tended to vary more dramatically in space than over time. This difference in spatial versus temporal variation in these key plant functional groups appears to be the primary driver of differences in slopes among regression models. While the individual models revealed strong relationships between ANPP to APAR, the use of temporal models to predict variation in space (or vice versa) can increase error in remotely sensed predictions of ANPP.

Seasonal grassland productivity forecast for the U.S. Great Plains using Grass‐Cast

“…This flexibility is most limited during dry/drought conditions as adaptive management decision‐making is constrained (Derner and Augustine 2016), and livestock production and net revenue decline with increasing precipitation variability (Hamilton et al 2016, Bastian et al 2018, Irisarri et al 2019) which influences decision‐making by land managers. Advances in remote sensing (Gaffney et al 2018) and modeling (Derner et al 2012, Fang et al 2014, Del Grosso et al 2018) to predict plant productivity, and multisite analyses of productivity responses to precipitation across decadal scales (Chen et al 2017, Petrie et al 2018), provide more synthetic knowledge and understanding to advance site‐ and regional‐level forecasting of aboveground biomass. Practical and functional applications of forecasting efforts, however, are still limited and may be improved by integrated, multidisciplinary approaches to fuse near real‐time remote sensing, short‐term and seasonal weather forecasts, process models, uncertainty, and web technology to visually display spatial and temporal patterns of grassland productivity.…”

Grazing Intensity Alters Leaf and Spike Photosynthesis, Transpiration, and Related Parameters of Three Grass Species on an Alpine Steppe in the Qilian Mountains

“…This offered a good explanation for why the daily mean values of the leaf Pn of A. inebrians decreased with increasing grazing intensity. Grazing-induced variation in plant composition will be critical for predicting the secondary production robustness estimates in semi-arid grassland area [ 44 ].…”

Remote Sensing of Grassland Production and Management—A Review

“…Productivity and biomass estimations based on physical models do not need field measurements as model input. However, due to the heterogeneity and temporal variability of grasslands at small scales, field data-based calibration, for example with eddy covariance tower measurements, is often necessary to achieve reasonable production estimations [184][185][186].…”