Remotely sensed soil moisture to estimate savannah NDVI

Satellite derived normalized difference vegetation index (NDVI) is a common data source for monitoring regional and global ecosystem properties. In dry lands it has contributed to estimation of inter-annual and seasonal vegetation dynamics and phenology. However, due to the spectral properties of NDVI it can be affected by clouds which can introduce missing data in the time series. Remotely sensed soil moisture has in contrast to NDVI the benefit of being unaffected by clouds due to the measurements being made in the microwave domain. There is therefore a potential in combining the remotely sensed NDVI with remotely sensed soil moisture to enhance the quality and estimate the missing data. We present a step towards the usage of remotely sensed soil moisture for estimation of savannah NDVI. This was done by evaluating the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture and three of its individual products with respect to their relative performance. The individual products are from the advance scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), and the Land Parameter Retrieval Model-Advanced Microwave Scanning Radiometer-Earth Observing System (LPRM-AMSR-E). Each dataset was used to simulate NDVI, which was subsequently compared to remotely sensed NDVI from MODIS. Differences in their ability to estimate NDVI indicated that, on average, CCI soil moisture differs from its individual products by showing a higher average correlation with measured NDVI. Overall NDVI modelled from CCI soil moisture gave an average correlation of 0.81 to remotely sensed NDVI which indicates its potential to be used to estimate seasonal variations in savannah NDVI. Our result shows promise for further development in using CCI soil moisture to estimate NDVI. The modelled NDVI can potentially be used together with other remotely sensed vegetation datasets to enhance the phenological information that can be acquired, thereby, improving the estimates of savannah vegetation phenology

Biodiversity decline with increasing crop productivity in agricultural fields revealed by satellite remote sensing

Increasing land-use intensity is a main driver of biodiversity loss in farmland, but measuring proxies for land-use intensity across entire landscapes is challenging. Here, we develop a novel method for the assessment of the impact of land-use intensity on biodiversity in agricultural landscapes using remote sensing parameters derived from the Sentinel-2 satellites. We link crop phenology and productivity parameters derived from time-series of a two-band enhanced vegetation index with biodiversity indicators (insect pollinators and insect-pollinated vascular plants) in agricultural fields in southern Sweden, with contrasting land management (i.e. conventional and organic farming). Our results show that arable land-use intensity in cereal systems dominated by spring-sown cereals can be approximated using Sentinel-2 productivity parameters. This was shown by the significant positive correlations between the amplitude and maximum value of the enhanced vegetation index on one side and farmer reported yields on the other. We also found that conventional cereal fields had 17% higher maximum and 13% higher amplitude of their enhanced vegetation index than organic fields. Sentinel-2 derived parameters were more strongly correlated with the abundance and species richness of bumblebees and the richness of vascular plants than the abundance and species richness of butterflies. The relationships we found between biodiversity and crop production proxies are consistent with predictions that increasing agricultural land-use intensity decreases field biodiversity. The newly developed method based on crop phenology and productivity parameters derived from Sentinel-2 data serves as a proof of concept for the assessment of the impact of land-use intensity on biodiversity over cereal fields across larger areas. It enables the estimation of arable productivity in cereal systems, which can then be used by ecologists and develop tools for land managers as a proxy for land-use intensity. Coupled with spatially explicit databases on agricultural land-use, this method will enable crop-specific cereal productivity estimation across large geographical regions.Peer reviewe

Effect of climate dataset selection on simulations of terrestrial GPP: Highest uncertainty for tropical regions

Biogeochemical models use meteorological forcing data derived with different approaches(e.g. based on interpolation or reanalysis of observation data or a hybrid hereof) to simulateecosystem processes such as gross primary productivity (GPP). This study assesses theimpact of different widely used climate datasets on simulated gross primary productivity andevaluates the suitability of them for reproducing the global and regional carbon cycle asmapped from independent GPP data. We simulate GPP with the biogeochemical modelLPJ-GUESS using six historical climate datasets (CRU, CRUNCEP, ECMWF, NCEP,PRINCETON, and WFDEI). The simulated GPP is evaluated using an observation-basedGPP product derived from eddy covariance measurements in combination with remotelysensed data. Our results show that all datasets tested produce relatively similar GPP simulationsat a global scale, corresponding fairly well to the observation-based data with a differencebetween simulations and observations ranging from -50 to 60 g m-2 yr-1. However, allsimulations also show a strong underestimation of GPP (ranging from -533 to -870 g m-2 yr-1)and low temporal agreement (r < 0.4) with observations over tropical areas. As the shortwaveradiation for tropical areas was found to have the highest uncertainty in the analyzed historicalclimate datasets, we test whether simulation results could be improved by a correction ofthe tested shortwave radiation for tropical areas using a new radiation product from the InternationalSatellite Cloud Climatology Project (ISCCP). A large improvement (up to 48%) insimulated GPP magnitude was observed with bias corrected shortwave radiation, as well asan increase in spatio-temporal agreement between the simulated GPP and observationbasedGPP. This study conducts a spatial inter-comparison and quantification of the performancesof climate datasets and can thereby facilitate the selection of climate forcing dataover any given study area for modelling purposes

Analyzing savannah vegetation phenology with remotely sensed data, lagged time-series models and phenopictures

It is predicted that savannah regions will see changes in precipitation patterns due to current climate change pro-jections. The change will most likely affect leaf phenology which controls net primary production. It is thereforeimportant to; 1) study those changes and its drivers, 2) to be able to correctly model the changes to vegetationphenology due to climate change. To our knowledge there is no existing global savannah phenology model thatcan capture both the phenological events and the vegetation state between the events. We therefore, investigate howday length, mean annual precipitation and soil moisture affects and controls the vegetation phenology of savannahs(using MODIS NDVI as a proxy for phenological state) with a lagged time series model for global application. Wefurthermore use phenological pictures (phenopictures) to investigate savannah tree and grass phenology. Phenopic-tures are pictures taken with a digital time-lapse camera with the purpose of recording and studying phenologicalevents. We used climate data from 15 flux towers sites located in 4 continents together with normalized differencevegetation index from MODIS for the model development. Two of the sites located in Africa were further ana-lyzed using phenopictures. The developed model identified all three considered variables as usable for modellingof savannah leaf phenology but showed some inconsistent result for some of the sites indicating the difficultiesin creating a simple common model that works equally well across sites. We attribute some of these difficultiesto site specific differences (e.g. grazing or tree and grass ratio) that the simplified model did not consider. Butwe expect it to on average give the cross-validated result (r2= 0.6, RMSE = 0.1) when applied to other savannahareas. The preliminary analysis of the phenological pictures with respect to tree and grass to some extent supportthis by showing differences in the start of the leaves development in the beginning of the season. However, thisdiffered between the two studied sites which further highlights the difficulties in creating a common model thatworks equally well for individual sites

NDVI modelled with CCI compared to MODIS-NDVI for a longer temporal extent (2003 until 2014).

<p>Only pixels with significant correlation are shown. Land borders have been created using data from thematicmapping.org Top: correlation coefficient between modelled and measured NDVI. Middle: Amplitude difference between model and measured NDVI for the entire time period. (Negative indicates an underestimation of amplitude in the modelled NDVI). In total only 0.2% of the significant pixels had an amplitude difference above zero (red color). Bottom: Willmott Index of Agreement between modelled NDVI and MODIS-NDVI.</p

Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model.

Savannah regions are predicted to undergo changes in precipitation patterns according to current climate change projections. This change will affect leaf phenology, which controls net primary productivity. It is of importance to study this since savannahs play an important role in the global carbon cycle due to their areal coverage and can have an effect on the food security in regions that depend on subsistence farming. In this study we investigate how soil moisture, mean annual precipitation, and day length control savannah phenology by developing a lagged time series model. The model uses climate data for 15 flux tower sites across four continents, and normalized difference vegetation index from satellite to optimize a statistical phenological model. We show that all three variables can be used to estimate savannah phenology on a global scale. However, it was not possible to create a simplified savannah model that works equally well for all sites on the global scale without inclusion of more site specific parameters. The simplified model showed no bias towards tree cover or between continents and resulted in a cross-validated r2 of 0.6 and root mean squared error of 0.1. We therefore expect similar average results when applying the model to other savannah areas and further expect that it could be used to estimate the productivity of savannah regions

Normalized CCI modelled NDVI (CCI-NDVI) and MODIS-NDVI.

<p>Example time series shown for latitude 9.625, longitude 30.375. Pixel chosen since the correlation (r = 0.71) between MODIS-NDVI and CCI-NDVI) had the closest to the average correlation for all pixels. CCI-NDVI was for this figure filtered with the same Savitsky-Golay method as used for the MODIS-NDVI. Data was normalized between zero and one for each year separately.</p

Best dataset analysis.

<p>Land borders have been created using data from thematicmapping.org. <b>Top:</b> Per-pixel best dataset identified as the one with the highest correlation between modelled NDVI and MODIS-NDVI using each of the four different soil moisture datasets. All NA indicate missing data in all datasets, no significant correlation to NDVI or correlation below 0.5 in all datasets. Data have for visual purposes been filtered with a 3x3 modal filter. <b>Bottom:</b> Best correlation (r) of modelled NDVI vs observed NDVI per-pixel. For each pixel, the highest correlation value shown as identified in the top panel. Non-significant or missing values are not shown in the plot. The grey color indicates a correlation below 0.5.</p

Correlation (r) between soil moisture modelled NDVI and MODIS-NDVI.

<p><b>Left:</b> Distribution shown for all significant pixels. The few values below zero omitted for visual purposes. <b>Right:</b> Heat-map of the average correlation for each dataset used to model NDVI which has been divided into aridity classes; arid (0.03 < AI < 0.2), semi-arid (0.2 < AI < 0.5), dry sub-humid (0.5 < AI < 0.65), and humid (AI > 0.65). Average correlation values shown within heat-map grid.</p