Functional Traits
Leaf Dry Matter Content
Over the Tibetan Plateau, Li et al. (2018b) attempted to measure plant
dry matter content with satellite imagery and use this as a proxy
measure for the community weighted mean (CWM) of LDM content. However,
the correlation was weak, with an R2 of just 0.1. A
different approach to mapping the CWM LDM content was conducted by
Polley et al. (2020a) at a restored grasslands site in Texas. Four years
of hyperspectral reflectance measurements from the ground and UAVs, as
well as ground sampling were incorporated into a partial least squares
regression (PLSR) analysis, with the model explaining 73% of the LDM
content of their canopies. Three further studies used the same analysis
and LDM content data as Polley et al. (2020a) for different goals, such
as looking what regulates the temporal stability of grassland
metacommunities (Polley et al., 2020b), biomass production (Polley,
Collins and Fay, 2020) and the influence of community LDM content on
plant production (Polley, Collins and Fay, 2022). Returning to the
Tibetan Plateau, Zhang et al. (2022) used UAV based hyperspectral
imagery and ground sampling, to map community LDM content through
numerous different machine learning models. The generic algorithm
integrated with the PLSR performed best for LDM content, explaining just
30% of the variance.
Crude Protein
UAV and satellite data combined with handheld hyperspectral and ground
sampling were successfully used to assess grass quality, such as CP,
under varying soil management conditions in a grassland research site in
Ireland. It was found that UAV data with multi-linear regression models
worked best and performed better than satellite data (Askari et al.,
2019). At natural steppe grasslands in China, Gao et al. (2019) used a
multispectral UAV and a variety of vegetation and band indices to map
feed quality, achieving the best results using the MERIS terrestrial
chlorophyll index. In a Colombian grazed grassland, Giraldo et al.
(2023) achieved an R2 of 0.76 using multispectral UAV
VIs and a generalised additive model. However, Hart et al. (2020) failed
to achieve good accuracy with the multispectral UAV over commercial
grasslands in Switzerland. The authors blamed the open access model they
used, GrassQ, being calibrated on a different type of grassland. In
south-east Germany, Raab et al., (2020) used both Sentinel-1 and -2 and
RFs to predict CP. While a strong relationship was found, the authors
found that the benefit of the additional Sentinel-1 data inclusion was
minimal. Using MODIS derived NDVI values and RFs over Tibet, Han et al
(2022) achieved R2 values of over 0.9 for CP. Using
ground based hyperspectral measurements in combination with Sentinel-2
data, Zhao et al. (2023) mapped CP across Inner Mongolian grasslands
with an R2 0.77 using RFs regression. Across three
different farmlands in western Colombia, Zwick et al. (2024) used
Planetscope imagery and ground sampling over three years to try and
model nutrient quality with machine learning. No single machine learning
model worked best overall, with their accuracy varying depending on the
location, but the best results ranged between an R2value of 0.52 and 0.75, and RF model variants achieving the best CP
accuracy for two of the three locations.
With a UAV mounted hyperspectral camera, forage quality was assessed
over grasslands in central Germany with several different statistical
and machine learning models. Support vector regression predicted CP most
accurately, with a high R2 of 0.81 under cross
validation (Wijesingha et al., 2020). Forage quality, including CP, was
also mapped using UAV hyperspectral imagery over grasslands in northeast
Australia, in combination with SfM models for grass height and biomass
(Barnetson et al., 2020). The authors found that the simple ratio,
NIR/Red produced the strongest relationship. At experimental grassland
sites in Norway, Geipel et al. (2021) assessed UAV mounted hyperspectral
mapping for forage yield and quality with powered partial least squares
regression modelling, achieving an R2 of 0.71 for CP.
Potassium, Phosphorous and
Nitrogen
Of the 24 research (two were reviews) papers that made up the review of
Potassium (K), Phosphorous (P) and Nitrogen (N):
- 15 focused on just nitrogen
- 5 focused on both nitrogen and phosphorous
- 2 focused on just phosphorous
- 1 focused on nitrogen and potassium
- 1 focused on nitrogen, phosphorous and potassium
Due to the overlap in papers measuring these plant nutrients, K, P and N
have been grouped together, and then split in multispectral remote
sensing methods, and hyperspectral methods.
Multispectral
In the alpine grasslands of the Tibetan Plateau Tang et al. (2021) used
a UAV with a standard high-resolution camera and PLSR to map a number of
different plant traits. Despite achieving strong correlations with most
traits, they failed to establish a significant relationship with N
content. In contrast, Oliveira et al. (2022) also used a standard drone
mounted camera, in combination with four different convoluted neural
network models in Finland, eventually achieving an R2of 0.82 with N concentration. In a legume-grass experimental site in
Germany, Grüner et al., (2021) combined terrestrial laser scanning,
multispectral UAV and RFs modelling to predict biomass and N fixation,
achieving an R2 of 0.71 when combining UAV data with
the laser scanner. Lussem et al. (2022) combined UAV mounted regular and
multispectral cameras, with a variety of machine learning models for
nitrogen uptake in west German grasslands, with RF and support vector
machine learning achieving R2 values of 0.83. In
southern Germany, a multispectral UAV and machine learning models were
again used to map N concentration in alpine grasslands (Schucknecht et
al., 2022). Most models produced poor correlations, with the maximum
R2 achieved with RF (0.47).
Several studies have employed Sentinel-2 imagery to map N and P across
parts of China. Gao et al. (2020) achieved an R2 of
0.49 in July, and 0.59 in November by using RFs to map the N/P ratio
over the Tibetan Plateau. In Inner Mongolia, Pang et al. (2022) enhanced
Sentinel-2 imagery with ground based hyperspectral imaging before
combining this with meteorological and geographic data. A fractional
differential algorithm was used to extract the spectral information
related to N and P, and a PLSR model used for estimating their contents
This approach achieved an R2 of 0.85 for P, and 0.78
for N. In the mapping of N, P and K over the Tibetan Plateau, Zhang et
al. (2023b) combined Sentinel-2 with Tiangong-2 imagery with SVM and RF
models. While the results were strong for the individual satellite and
modelling methods, combining both with RF modelling produced
R2 values of 0.78, 0.74 and 0.84 for N, P and K
respectively. In assessing grassland P in the Tibetan Plateau, Shi et
al. (2024) used an approach based on graph theory to create
hyperspectral data from Sentinel-2 bands, they then used a deep
regression inversion model to map grassland P content across different
phenological stages. The authors report R2 values
above 0.8 and significant improvements over the original low spectral
resolution data and other modelling approaches.
Sentinel-2 has also been used in various other regions to successful map
essential plant nutrients. Arogoundade et al. (2023) successfully mapped
the C:N ratio in South African grasslands with Sentinel-2 and RFs,
entirely within Google Earth Engine. Across a range of grassland sites
in Portugal, Morais et al (2023) assessed the ability of Sentinel-2 to
map N and P through machine learning methods. RF again worked best
overall, with an R2 of 0.77 and 0.71 for N and P,
respectively. Similarly, Cisneros et al. (2020) used the Three Band
Index from Sentinel-2 to map foliar nitrogen content in an experimental
plot in Brazil with 38% accuracy and Smith et al. (2023) applied
Sentinel-2 and a range of machine learning methods to map nitrogen
concentrations in a Bahia grass experimental site. However, here RFs
produced a very strong R2 in the training dataset
(0.99-1.00) but performed relatively poorly in the test data
(0.20-0.57). Finally, Dehghan-Shoar et al. (2023) combined a radiative
transfer model with a bidirectional reflectance distribution function
into a single model to predict grassland N concentration from Landsat-7
and -8, and Sentinel-2, reaching an R2 of 0.50 with
their validation dataset.
Hyperspectral
Over West African Savanna, Ferner et al. (2021) attempted to map
phosphorous concentration from a ground-based spectrometer, Hyperion
hyperspectral satellite imagery, and Sentinel-2. However, no significant
correlations were established with any of the datasets. In experimental
grassland sites in the US, both Wang et al. (2019) and Cavender-Bares et
al. (2022) mapped foliar N content with similar accuracy from aircraft
mounted hyperspectral cameras, with R2 values of 0.57
and 0.58 respectively. Gholizadeh et al. (2022a) produced similar
predictive power for both N and K, with their own aircraft mounted
hyperspectral imaging data. However, in tallgrass prairie sites, Pau et
al. (2022) found the N concentration product of the National Ecological
Observatory Network’s (NEON) surveys had an R2 of just
0.29 compared to ground sampling,
Using UAV based hyperspectral surveys, Polley et al., (2023) achieved an
R2 of 0.8 with a simple linear regression between the
red-edge chlorophyll index and community N content in experimental
grassland in Texas. In an experimental grassland in Finland, MLR and RF
were used to combine UAV based hyperspectral images and photogrammetry
for N concentration, with an R2 value of 0.90
(Oliveira et al., 2020). UAV based hyperspectral imagery had mixed
predictive power for N when combined with PLSR on both a German
grassland experimental site, R2 of 0.58 for content
(Franceschini et al., 2022) and in an Inner Mongolian monoculture test
site with an R2 for N and P of 0.87 and 0.54,
respectively (Zhao et al., 2021a). Slightly more modest results were
achieved with UAV hyperspectral data and a GA-PLSR model over natural
grassland on the Tibetan Plateau, with an R2 of 0.50
and 0.54 for community level N and P (Zhang et al., 2022).
Leaf Area Index
The LAI papers total 35, excluding reviews. As only eight deal primarily
with hyperspectral sensors, it makes more sense to divide these by the
spatial resolution of the sensors:
- High Resolution e.g., UAV and Aircraft (0.01 to 1.0 m).
- Medium Resolution e.g., Goafen-2, Landsat, Senitnel-2 (3 to 30 m).
- Low Resolution e.g., MODIS and Senitnel-3 (>30 m).
High Resolution
UAVs equipped with hyperspectral cameras have been used to measure LAI
across 4 studies in Inner Mongolia since 2019. The best results were
achieved on a grassland monoculture site with an R2 of
0.87 between UAV level canopy measurements and ground sampling through
PLSR (Zhao et al., 2021a). Using linear regression to relate UAV derived
VIs to LAI, Sha et al. (2019) produced an R2 of 0.45
between the Generalized soil-adjusted vegetation index and LAI, with
more of the errors coming from regions with low LAI values. However, Zhu
et al., (2023) used the PROSAIL model to determine the optimum VIs, then
used a two-layer VI matrix to calculate LAI, with an
R2 of 0.73. Zhu et al. (2024) developed this further
over a species rich grassland by using the PROSAIL model and two simple
vegetation indices, the optimized soil-adjusted vegetation index (OSAVI)
and NDVI, achieving an R2 of 0.84.
Two additional studies were carried out focusing on aircraft mounted
spectrometers, with contrasting results. In a Tallgrass site in the USA,
the NEON LAI was not significantly related to ground-based LAI
measurements (Pau et al., 2022). With a different approach, Bandopadhyay
et al. (2019) found higher rates of sun-induced fluorescence at 687 and
760 nm was associated with greater LAI (R2 of 0.80 and
0.86 respectively) in their natural test sites in Poland, which included
many species rich grasslands. The sun-induced fluorescence measures also
correlated well with greenness related Vis, such as NDVI.
Medium Resolution
A range of methods have been employed under the medium resolution remote
sensing of LAI. Xu et al. (2018) and Qin et al. (2021) compared
different VIs to ground based LAI measurements, with the perpendicular
vegetation index from Landsat and the normalized difference phenology
index from Sentinel-2 performing best, respectively. In two studies
using the Copernicus Land Monitoring Service (CLMS) LAI products and
Sentinel-2 in Poland, Dabrowska-Zielinska et al. (2024) and
(Panek-Chwastyk et al., 2024) found strong agreements with ground-based
LAI, with R2 values of between 0.62 and 0.93.
Machine learning was also the focus of several LAI studies. In South
Africa, RF has been used to successfully map LAI with both Landsat and
Sentinel-2, but with slightly stronger results in the dry season vs the
wet season (Dube, et al., 2019; Masenyama et al., 2023). In more
mountainous South African grasslands, Tsele, Ramoelo and Mcebsi (2023)
found that the optimal regression choice, RF or stepwise multiple linear
regression, varied depending on the location. Shen et al. (2022)
assessed a range of different machine learning approaches (RF, neural
networks and support vector regression) on Landsat-8 data to model LAI,
with RFs again tending to produce the most accurate results. Three
studies have attempted to use machine learning methods to integrate SAR
data with multispectral for mapping LAI. Lu and He (2019) found the
improvements from including SAR in their RF over the southern Canadian
Prairies was marginal. However, Wang at al. (2019) found that SAR data
improved LAI estimates in their MLR model over areas of dense tallgrass
vegetation where typical VIs tend to become saturated, a finding
supported by a subsequent study of Alpine grasslands in northern Italy
(Castelli et al., 2023).
Five studies have used radiative transfer models (RTMs) with medium
resolution satellite imagery to aid in mapping LAI. In test farms in
southern England, the PROSAIL model was used with Sentinel-2 for LAI
mapping, achieving strong correlations and offering an improvement over
LAI calculated from NDVI (Punalekar et al., 2018). In Brazil, the
Automated Radiative Transfer Model Operator (ARTMO) was used with
Sentinel-2 also. The authors found that the Normalized Area Over
Reflectance Curve (NAOC) index produced the strongest results (Cisneros
et al., 2020). In Austria, Sentinel-2 was used with two RTMs for the
growing seasons of 2018 and 2019, achieving an R2 of
0.87 with direct ground measurements of LAI (Klingler et al., 2020).
Similar success was achieved in grassland of northern China using the
PROSAIL model again, with an R2 of 0.82 between the
newly developed Chlorophyll-Insensitive VI (CIVI) and LAI (Zhang et al.,
2023A). In northeastern Germany, Schwieder et al. (2020) tested the
accuracy of two methods for assessing LAI using Sentinel-2 – RF
regression and a soil-leaf-canopy (SLC) RTM, with both models
demonstrating strong predictive power. Brown et al. (2021) compared a
novel Level 2 processor for Sentinel-2 data (SLP2-D), with updated
artificial neural networks retrieval methodology. The updated method was
close to or better than the old over many vegetation types, but slightly
worse over grasslands. Jiang et al. (2024) developed a new
Bi-directional Reflectance Distribution Function (BRDF) for the Gaofen-1
satellite to improve vegetation parameter accuracy with tests over
grasslands in northeast China. The new BRDF produced an
R2 of 0.58, 0.14 higher than the previous method. In
central China, Peng et al. (2024) applied topographic corrections to a
large range of LAI models, and compared them to LAI products, such as
from MODIS and GLASS, and ground sampling. Topographic corrections, when
combined with RTMs, improves the correlations (R2improvements of 0.18 to 0.04) and reduces the errors more than ML
combined with RTMs. They also produced an R2improvement of >0.2 compared to MODIS and GLASS LAI products.
The research is focused on mountainous terrain and so may not be as
applicable in flatter grasslands.
Low Resolution
Many low-resolution global LAI products currently exist and have been
used in a large range of studies over recent years. These products
include, for example, the MODIS derived MOD15A2 and MOD15A2h, Geoland2
Version 1 (GEOV1) and Global Land Surface Satellite (GLASS), each with
different development methods, temporal and spatial resolutions.
Several recent studies have compared these LAI products with ground
measurements and high-resolution satellite data across different
ecosystems and countries. Li et al. (2018a) compared MOD15A2, GLASS,
Global LAI Product of Beijing Normal University (GLOBALBNU) and Global
LAI Map of Chinese Academy of Sciences (GLOBMAP), with ground
measurements both across the globe and, with special emphasis, over
China. Overall, GLASS performed best in both situations, with
R2 values of 0.70 and 0.94 respectively. Even though
grasslands made up 43.1% of the assessment area in China, specific
correlations for grasslands are not provided. Another comparison was
carried out by Liu et al. (2018) between the MOD15A2, GLASS and the
Four-Scale Geometric Optical Model (FSGOM), over a mixture of land cover
types in China. FSGOM was found to perform slightly better in
grasslands, with and R2 of 0.5, 0.09 and 0.22 better
than MOD15A2 and GLASS respectively. A specific grassland comparison of
GEOV2, GLASS, GLOBMAP, and MOD15A2h was carried out in Inner Mongolia,
with GLOBMAP performing best in meadows, GLASS best in typical steppe
and GEOV2 best in desert steppe, but all with R2values below 0.4 (Shen et al., 2023). Yin et al. (2020) demonstrated
that the temporal resolution is also important to consider. Comparing
MOD15A2, MOD15A2h, GEOV1 and GLASS, the authors found that the MODIS
based LAI products had lower R2 compared to the other
datasets, but the shorter temporal window allowed for sudden changes to
be detected, while GEOV1 and GLASS had high R2 values,
but missed grazing induced sudden changes due to their broad temporal
windows.
Munier et al. (2018) used Kalman filtering to disaggregate global GEOV1
data, allowing them to assign different LAI values to different
vegetation types within a single pixel. While producing improvements
over most vegetation types, this method reduced the accuracy over
grasslands, with the R2 dropping from 0.89 to 0.82
compared to the original data. Several attempts have been made to fuse
high resolution, but temporally sparse, LAI data with low resolution
global products, but with mixed results. Li et al. (2018v) used
Landsat-7, -8 and Sentinel-2 to generate 30 m resolution LAI maps in
northern China using PROSAIL. These were combined with the MODIS data
using a spatial and temporal adaptive reflectance fusion model (STARFM).
The authors note reductions in errors and noise in their new fused
datasets, with the R2 of 0.62 vs 0.53 for the original
MODIS LAI product. Zhou et al. (2020) took a different approach, using a
timeseries of MOD15A2H as a long-term background signal, ground
measurements and Landsat-7 and -8 were fused using a back propagating
neural network to create 30 m LAI maps for the study regions in Ukraine
and China. A modified ensemble Kalman filter model (MEnKF) using both
the Landsat and MODIS data, allowed for the 30 m LAI to be spread over
the space and time of the MODIS data, achieving an R2of 0.88 over grasslands. Across mixed test sites in the USA, another
approach to combining field data, MODIS and Landsat through a deep
transfer learning framework failed to produce substantial improvements
over grasslands but was successful over croplands and forests (Zhou et
al., 2023).
Finally, in northern China, Li et al. (2024) compared the ability of
Sentinel-3 to retrieve vegetation parameters such as LAI, with MODIS and
PROBA-V, using a range of prediction methods. Sentinel-3 had better
accuracy than other platforms, likely due to the red edge bands, but
only slightly compare to PROBA-V (R2 of 0.63 each)