Plant primary production is a key driver of ecosystem dynamics and can thus influence several ecosystem functions, such as water purification capacity and secondary production by animals. Knowledge of the spatio-temporal dynamics of plant biomass production is essential to inform the management of natural resources, in conservation areas and in agro-pastoral systems [1
]—particularly in the Mediterranean and semiarid regions, where inter-annual changes in precipitation often result in large variations in plant production [5
Traditional methods for plant biomass estimation are based on in-situ observations. They can be highly accurate but often involve intensive field work and destructive methods, which makes them costly and inapplicable to inaccessible or sensitive areas, or when involving endangered species [4
]. Remote sensing constitutes an increasingly used alternative [8
], based on the relationship between satellite-derived metrics and primary production [7
]. Remote sensing may allow for the non-destructive, high-resolution coverage of large, remote, and/or inaccessible areas, such as mountains [9
], deserts [10
], or wetlands [4
]. Remote sensing allows for the reconstruction of historical trends as well, using satellite image time series: for example, the reconstruction of the hydroperiod in Doñana marsh from 1974–2014 [14
], or the assessment of rangeland conditions in semiarid regions [15
]. The most widely used methods to monitor vegetation are based on the use of vegetation indexes, such as the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI), as proxies of aboveground biomass [7
]. However, the use of these indexes is also subjected to limitations and criticism; for example, they have been shown to saturate asymptotically at high biomass values [12
In general, the assessment of plant production can be based on the analysis of single (i.e., one-date) images, bi-temporal change detection, or temporal trajectory analysis, followed by the interpretation of results over time [19
]. For vegetation, a traditional group of methods relies on quantifications of the differences in statistical metrics of the vegetation-index time series like, for example, the beginning and end of the growing season, the maximum and minimum values, the annual mean, or the variance [9
]. In regions with strongly seasonal climates, production is typically assessed by searching for anomalies in the current NDVI against the average of the whole time series, or against reference values from the same period of the year, which informs about the current status of vegetation as compared to other seasons, or to an average condition [20
]. This is made at predefined fixed dates, which works well when seasonal cycles are regular, but is often problematic when they vary across years due to climatic or environmental variability. In such cases, observed anomalies in NDVI data may simply constitute a temporal shift of the growth season—i.e., an early (positive NDVI anomaly) or delayed (negative NDVI anomaly) start of the growth season [10
]. Other key problems may be related to the lack of consistency and reliability of the NDVI images used for analysis due to noise or errors, especially when a single image per year is used. Examples include variation in viewing and illumination geometry, resolution and calibration, digital quantization errors, ground and atmospheric conditions, as well as orbital and sensor degradation [7
To overcome these limitations, the use of smoothed NDVI time series including a number of consecutive growing seasons (instead of a single image per growing season) is being proposed. Such time series analyses make use of all the information accumulated at the end of the growing season to estimate the parameters describing vegetation phenology (e.g., [21
]). Indeed, the study of vegetation phenology has become very relevant in several realms, such as productivity and the carbon cycle (e.g., [23
]), climate change and its impacts on ecosystems [25
], as well as crop and pasture monitoring [10
]. During the last decade, on-the-ground phenological studies have been complemented by studies focusing on large-scale remote sensing [28
], technically referred to as Land Surface Phenology (LSP, [12
]). LSP can be defined as the timing of recurring changes in the reflectance of electromagnetic radiation from the land surface due to concurrent life-cycle changes of vegetation [29
]. It is generally measured by deriving either vegetation parameters (e.g., leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR) or vegetation indexes (e.g., NDVI, EVI) from remote-sensing data [9
]. These vegetation indexes are used to maximize the extraction of variability assigned to certain plant features (e.g., leaf area, canopy cover, photosynthetic activity) while minimizing other unwanted effects (e.g., geometric, soil color, or atmospheric effects), thus enhancing the information contained in spectral reflectance data [12
]. LSP is then characterized using different mathematical procedures such as the identification of global/local thresholds and points of maximum increase/decrease, curve fitting and the subsequent extraction of inflection points or thresholds, and harmonic analysis [20
In this article, we present a method for estimating plant biomass production in seasonal wetlands based on the NDVI from the Moderate-resolution Imaging Spectroradiometer (MODIS). Method development included the comparison of the two different approaches discussed above, namely the use of single images versus the characterization of LSP using the whole time-series; as well as the use of different estimators within each of these two approaches to estimate biomass production across the whole study area for the 16-year series.
We developed and applied this method in a particularly challenging study area: the semiarid marsh and wetlands of the Guadalquivir river estuary (Doñana National Park, SW Spain; ‘Doñana marsh’ hereafter). As in many arid and semiarid regions, the determination of biomass production is particularly challenging due to the flooding regime, the color influence of soils, and the spatial variation in vegetation communities and species composition [34
]. The Doñana marsh consists of a diverse and complex array of ecosystems affected by a highly dynamic interplay among vegetation, soil and water [38
], whose prolonged land-use history fostered a mix of natural and semi-natural vegetation [39
]. Its vegetation provides habitat and food for a highly diverse fauna, making the area a biodiversity hotspot; but grazing by wild and domestic herbivores largely determines plant standing crop and may result in overgrazing, particularly during dry years [40
]. Studying the primary production of the marsh vegetation is therefore essential for the management and conservation of the Doñana National Park; while its huge size and accessibility problems during most of the flooding period makes this a particularly challenging task using solely on-the-ground approaches.
We have shown that MODIS Global MOD13Q1 NDVI data provides a good source of information for estimating biomass production in a challenging situation—a seasonal marsh characterized by high spatio-temporal variation in precipitation and hydroperiod [55
]. While the use of a single image per growth season provided estimates of reasonable quality (39–41% of variance explained in the calibration dataset), modeling the phenological cycle using Land Surface Phenology (LSP) techniques considerably improved the quality and robustness of such estimates (65% and 70% of variance explained using LSP-Maximum-NDVI, in the calibration and the validation datasets, respectively; see also [56
]). Furthermore, biomass production estimates derived from the best-performing model for the whole study area and time period indicate a strong role of a key climatic driver, the inter-annual variation in precipitation; and a pattern of spatio-temporal change (decreasing yields in the most productive areas) that could be consistent either with changes in vegetation community composition due to marsh siltation and changes in hydroperiod [14
] or with the impact of a key biotic driver, overgrazing by domestic and wild herbivores.
The modeling process was particularly challenging because marshes are highly dynamic and heterogeneous wetland ecosystems where the reflectance signal can change rapidly, sometimes within hours or days [6
]. Despite these challenges, the four different, NDVI-based estimators predicted biomass production with reasonable quality (39–65% of variance explained during calibration). However, the two NDVI biomass estimators derived from TIMESAT models of LSP performed significantly better than those based on a single image per year only—reinforcing previous suggestions that LSP may improve biomass determination in complex ecosystems [20
]. The improved performance of LSP estimators is probably caused by the higher sensitivity of single-image estimators to several sources of error and noise, such as sensor resolution and calibration, digital quantization errors, ground and atmospheric conditions, or orbital and sensor degradation [7
]; and to the rapid changes in the NDVI signal in heterogeneous ecosystems—which may bias such estimators, for example, if an image is taken after a rainfall episode [38
]. LSP makes use of the information gathered across the complete growth season to produce a smooth NDVI curve that integrates the whole vegetation cycle, thus reducing noise and errors [12
]. On the one hand, the difference among fitting methods was marginal for the best-performing predictor (LSP-Maximum-NDVI; Table 1
), suggesting that the smoothing provided by all fitting procedures sufficed to remove noise and ensure predictor quality—in contrast with works reporting that the over-smoothing introduced by the Asymmetric Gaussian and Double Logistic methods affected the accuracy of parameter estimates [21
]. On the other hand, the use of a baseline criterion that removed the influence of water removed the strong bias introduced on NDVI-based estimates by early-flooding years—which caused a drop in NDVI values, unrelated to plant productivity.
Besides their statistical properties, the choice of estimator may influence its potential use by managers or policy makers. Management applications that rely on an early prediction of the season’s standing crop, for example to adjust the stocking rates of domestic herbivores (cattle and horses), will be best served by those based on single images taken at early dates—such as the May-NDVI, chosen to coincide with the average NDVI maximum without requiring the uptake of ulterior images to identify the exact time of the season’s maximum. Similarly, one of the two indicators based on LSP can be calculated at a much earlier point than the other—since LSP-Maximum-NDVI only requires the maximum value to be reached, while LSP-Accumulated-NDVI can only be calculated at the end of the growth season. Under such circumstances, it might be more useful to use a statistically weaker estimator that can be estimated earlier, as long as the associated decrease in accuracy is acceptable. Unfortunately, single-image estimators such as May-NDVI had a much lower accuracy than LSP-based estimators (39–41% vs. 65–70% of variance explained). We therefore recommend the use of LSP-Maximum-NDVI, which provides the best estimates at a relatively early date.
Estimators based on NDVI have been shown to saturate asymptotically at high biomass values [12
]. While the relationship between NDVI and biomass production was multiplicative (i.e., the slope decreased with increasing NDVI, following a logarithmic relationship), the best-performing estimator LSP-Maximum-NDVI was far from reaching a plateau at the highest biomass production values we measured. As a consequence, estimates based on LSP-Maximum-NDVI performed reasonably well in the validation exercise. We cannot rule out, however, a saturation of these estimators in situations (years or localities) with higher biomass production—which would result in a disproportionate increase in prediction errors. We decided to build our models using NDVI because it is the most frequently used vegetation index, but as it is prone to saturation, and to noise caused by soil color and water, it would be interesting to test whether models can be improved using EVI, a vegetation index less prone to these problems [60
Testing the robustness and validating the performances of the best estimator with independent data was particularly relevant given the high heterogeneity, complexity and unpredictability of the Doñana marsh ecosystems [14
]. Validation yielded satisfactory levels of predictive ability, particularly given the characteristics of the study system and the high variation in species composition detected. More importantly, the estimator also proved to be robust to the influence of environmental variables (precipitation and hydroperiod), spatial variation in baseline productivity, and species composition—suggesting that it can be safely used under the variety of situations present in the Doñana marshes, as well as in similar systems.
The analysis of the spatial and temporal variation of biomass production in the Doñana marsh confirmed that production is both highly variable and highly heterogeneous. Based on previous studies we expected precipitation, which determines the flooding regime, to account for a large percentage of the variation in biomass production [62
]. Indeed, precipitation explained 69% of the temporal variation in biomass production (summed across the study area). The relationship between precipitation and biomass production was however non-linear, indicating that biomass production is strongly dependent on precipitation in dry years but it tends to saturate in very wet years (similar to what Coe et al. [63
] report). Whether this saturation results from self-thinning effects (intra- and inter-specific competition) and/or from the negative effect of prolonged inundation on plant development remains a topic for future studies.
The effect of herbivores on the marsh vegetation is another important source of variability. Specifically, changes in plant consumption caused by variation in the number and distribution of domestic (cattle and horses) and wild (fallow deer Dama dama
, red deer Cervus elaphus
, wild boar Sus scrofa
) herbivores have been shown to determine the abundance and distribution of plant biomass, reducing it severely in dry years [40
]. The spatio-temporal trends detected using the Theil-Sen slope estimator suggest that biomass production has decreased, during the last 16 years, precisely in the areas where this production was more abundant. This pattern could be consistent with changes in vegetation community composition due to temporal trends in mean hydroperiod [14
] probably due to marsh siltation. However, they could also be reflecting the effect of overgrazing by herbivores, which may be expected to concentrate their grazing (thus consuming more biomass) in the areas with higher biomass yield—particularly in dry years with low biomass production. Indeed, herbivores do not distribute uniformly in the marsh; they move tracking food and water availability, and avoiding heavily flooded areas. Mapping the biomass is an important first step to monitor and manage the effects of herbivores [5
]. It can support management programs that rationalize the number of domestic animals and find a dynamic balance between cattle and vegetation [65
], helping to prevent land degradation, soil erosion and biodiversity loss [67
]. In this regard the study of the vegetation patterns could be improved by correlating the changes in vegetation biomass with hydroperiod trends, and with the spatial distribution and movements of domestic and wild herbivores. The modeling process in a heterogeneous ecosystem such as the Doñana marsh could also benefit from increasing the spatial resolution using other sensors such as Landsat.