# Using PlanetScope NDVI time series to detect the phenology of individual trees in the Sahel

Yasmin Fitts, Compton Tucker, Pierre Hiernaux, Yves Auda, Laurent Kergoat

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Yasmin Fitts, Compton Tucker, Pierre Hiernaux, Yves Auda, Laurent Kergoat. Using PlanetScope NDVI time series to detect the phenology of individual trees in the Sahel. Remote Sensing of Environment, 2025, 321, 10.1016/j.rse.2025.114650. hal-05387590

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# Using PlanetScope NDVI time series to detect the phenology of individual trees in the Sahel

Yasmin Fitts<sup>a,b,d,*</sup>, Compton Tucker<sup>b</sup>, Pierre Hiernaux<sup>c</sup>, Yves Auda<sup>d</sup>, Laurent Kergoat<sup>d</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

Editor: Dr. Marie Weiss

**Keywords:**

Phenology

Sahel

PlanetScope

Trees

## ABSTRACT

New advancements in satellite technology enable more accurate observation of woody population dynamics, providing greater insights into the underlying processes that influence their change. In this study, we evaluate the use of PlanetScope NDVI time series to track the phenology of individual trees in the Sahel, where ground-based environmental surveys are scarce. Five-year NDVI time series were produced for 398 trees with known species recorded in Mali, Senegal, and Niger. Clouds and high aerosol contamination were filtered using MODIS products and focused on the dry season to minimize the influence of background NDVI directly (through crown influence) or indirectly (through adjacency effects). Each NDVI time series profile was fitted with a spline model to obtain the minimum NDVI day of year during the dry season. PlanetScope NDVI time series accurately captured the photosynthetic phenology of individual tree crowns in the Sahel, with discernable differences between individuals and species. When species were grouped based on four phenology types, deciduous and inverse deciduous species exhibited a relatively consistent phenological pattern across all sites. The phenology of semi-evergreen species and evergreen species, which include species with few leaves, was more heterogeneous. Intra-species variation was relatively modest between sites, and most species maintained a similar NDVI profile, with shifts in leaf phenology events correlating with the timing of the wet season in each site. Overlap between the different phenology groups indicates that transitions between phenology types and species are not clear-cut, and even individuals of the same species can demonstrate plasticity. Furthermore, NDVI profiles were extracted for 500 randomly selected tree samples within eight 10 km² clip boxes distributed along the West African rainfall gradient from 9.9° to 16.6° latitude at -1.6° longitude. This analysis showed a strong relationship between the phenology of woody plants and the timing and distribution of rainfall at each latitude. Green-up of woody vegetation before herbaceous vegetation was marked in the more southern Sahelo-Sudanian latitudes. Additionally, despite the prolonged dry season in the more northern semi-arid latitudes, trees retained their greenness remarkably late into the dry season. Increased air temperature and dryness as a result of climate change could impact tree function in this region and needs individual-based monitoring.

## 1. Introduction

Plant phenology is the study of the seasonal cyclical patterns of plant behavior and the genetic and environmental factors that influence them (Lieth, 1974; Piao et al., 2019). The timing of phenological stages is essential for understanding ecosystem function and organization (Justice et al., 1985), and it plays a part in reducing competition for resources between coexisting species (Cleland et al., 2007; Ryan et al.,

2017). Shedding light on the spatial and temporal variability of phenology across landscapes at the scale of the individual tree or bush can further improve knowledge of atmosphere-biosphere interactions and the carbon cycle, improve resource management, and aid in the monitoring of ecological health and biodiversity. Furthermore, focusing on the heterogeneity of inter- and intra-species phenology can reveal the degree of species plasticity, which plays a large part in ecosystem resilience (Fawcett et al., 2021).

<footnote><sup>a</sup> Biospheric Sciences Laboratory, Science Systems and Applications, Inc., Code 618 NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</footnote>

<footnote><sup>b</sup> NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</footnote>

<footnote><sup>c</sup> Pastoral Conseil, 30 chemin de Jouanal, 82160 Caylus, France</footnote>

<footnote><sup>d</sup> Géosciences Environnement Toulouse (GET), Observatoire Midi-Pyrénées, UMR 5563 (CNRS/UPS/IRD/CNES), 14 Avenue Edouard Belin, 31400 Toulouse, France</footnote>

<footnote>* Corresponding author at: Science Systems and Applications Inc., Biospheric Sciences Laboratory, Code 610, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.</footnote>

<footnote>E-mail addresses: yasmin.fitts@nasa.gov (Y. Fitts), compton.j.tucker@nasa.gov (C. Tucker), laurent.kergoat@get.omp.eu (L. Kergoat).</footnote>
Phenological timing depends on environmental factors such as precipitation and temperature, making phenology an important indicator of more subtle climate and ecological changes that can cause major feedback into overall ecosystem dynamics. With the exacerbation of anthropogenic climate change over the 21st century, the need for increased understanding of its effects on all levels is now more important than ever. Important knowledge gaps nonetheless persist as a result of a large research bias in favor of the temperate or boreal zones as opposed to the tropics, especially for the dry tropics (Adole et al., 2016; Seghieri et al., 2012a). Long-term field surveys are uncommon in many dry tropical areas and remote sensing is often the only source of data. Though phenology field measurements are more precise and can include other stages of the phenological cycle such as budburst flowering and fruiting, they are time and labor-intensive, and limited sampling makes up-scaling difficult. Except for emerging citizen science networks restricted so far to the temperate and boreal areas, these types of studies are usually carried out on very small scales (Delbart and Picard, 2007).

Remote sensing has thus broadened the spatial reach of phenology studies (Goward et al., 1985; Herrmann et al., 2005; Heumann et al., 2007; Heumann et al., 2007; Moulin et al., 1997; Zhang et al., 2003), though these large-scale remote sensing studies have had their own sets of limitations. Satellite spatial or temporal resolution of the most widely used remote sensing datasets often necessitates trade-offs, with most large-scale remote sensing studies assuming some degree of homogeneity in each pixel. In many ecosystems, coarser resolution imagery often fails to detect non-forest trees scattered throughout the landscape, which are usually merged with their background (crops, pastures, savannas, etc.). This kind of spatial distribution is widespread in the tropics, where parklands, steppes, savannas, crop, fallow, and rangeland mosaics often dominate. This study accordingly focuses on the Sahel, a region composed of these types of landscapes with the characteristic scattered spatial distribution of woody vegetation, generally increasing in density southward with the rainfall gradient. Brandt et al. (2020) found that from the sub-humid areas near the Sudanian savannas to the hyper-arid areas towards the Sahara Desert, tree canopy cover decreases on average from 13.3 % to 0.1 %, respectively.

Major climate and environmental changes have been reported in the West African Sahel during the last fifty years. After enduring the largest multi-decadal drought of the 20th century in the 1970s and 1980s, Sahelian ecosystems have recovered with the return of near-average rainfall. The resulting re-greening is primarily attributed to the fast response of herbaceous vegetation and crops to rainfall over the 1980–2000 period (Dardel et al., 2014). The role of trees in this regreening is poorly documented, however, although a recovery has also been suggested (Anchang et al., 2019; Brandt et al., 2019). Evergreen and deciduous tree species coexist in the Sahel, as in other steppes and savanna ecosystems (Masia et al., 2018), but impacts on the proportion of phenology types and species as a response to drought are still unclear (Gonzalez, 2001; Hänke et al., 2016).

In addition to the recent multidecadal drought, the response of trees to changes in rainfall regime (Brandt et al., 2019), air temperature and humidity, the rise of atmospheric carbon dioxide concentrations, and land use change, especially cropping expansion, also raise important issues, as well as their role in the carbon and hydrological cycle. Tree foliage phenology and its drivers are also needed by Earth System Models so that proper Plant Functional Types (PFTs) can be defined. The more precise knowledge of phenology at the scale of the individual tree can therefore increase the accuracy of large-scale modeling (Seghieri et al., 2012a; Richardson et al., 2012).

Recent developments in satellite technology create new possibilities with imagery that is both spatially and temporally high resolution, permitting analysis of individual tree phenology with daily time series. PlanetScope Dove CubeSats from Planet Labs Inc. provides daily coverage of the Earth at 3-m resolution, with four multispectral bands (RGB, NIR) to calculate NDVI (Tucker, 1979; Tucker et al., 2005).

PlanetScope daily imagery has already been used in other studies to

analyze phenology. Cheng et al. (2020) evaluated the use of PlanetScope and Sentinel-2 imagery over a Kenyan rangeland to study dryland phenology at plot scale. They identified the ability of PlanetScope to differentiate individual trees from their surroundings in a semi-arid setting, noting that the daily revisit time allowed for a higher frequency of cloud-free images compared to other datasets, which becomes pivotal during the rainy season. Dixon et al. (2021) created a machine learning model using PlanetScope imagery in conjunction with drones to detect pixel-level eucalypt tree flowering in Western Australia. Wu et al. (2021) used a combination of PlanetScope imagery and drone observations to study temperate autumn phenology of individual tree crowns in Northeast China, finding strong agreement between local PhenoCam and PlanetScope-detected phenological timing. Wang et al. (2020) cross-calibrated PlanetScope surface reflectance with MODIS to assess PlanetScope imagery for tropical forest leaf phenology in the Amazon.

These studies all integrated field data, drone, or more well-tested datasets such as Sentinel-2 or MODIS to compare and cross-validate PlanetScope measurements, and all found that PlanetScope can effectively be used for phenological studies on small spatial scales. However, none of these studies used PlanetScope to identify different leaf phenology functional types of woody vegetation and how that varies by latitude, nor on a scale as large as in this study.

Given the above-described observational advantages of such an approach, we detail below our use of PlanetScope NDVI time series in an attempt to accurately capture leaf phenological changes of individual tree crowns in the Sahel. We used five years of PlanetScope data to study the seasonal pattern of individual trees with known species inventoried in the field, as well as tree crown polygons automatically produced using very high-resolution Maxar data by Tucker et al. (2023). In doing so, we address the following questions:

1. Can PlanetScope daily NDVI time series effectively be used to characterize the leaf phenology of individual tree crowns?

2. Can the different leaf phenology types be differentiated using PlanetScope time series?

3. Is there variability among species, leaf phenology types, or between sites?

4. How does the average leaf phenology of woody vegetation change across a latitudinal transect, and how does the overall leaf phenology of Sahelian trees differ from that of sub-humid areas?

## 2. Data and methods

### 2.1. PlanetScope imagery

PlanetScope from Planet Labs is a constellation of 130+ small satellites in sun-synchronous orbit that provide daily 4-band (RGB and NIR) imagery of the Earth at 3-m spatial resolution (Planet Imagery Product Specifications, 2023). Currently in orbit are the Dove Classics, the improved Dove-Rs with more spectral and radiometric precision and sharpness, and the hyperspectral SuperDoves. Most images used in the study were daily Dove Classic imagery, or the newer generation Dove-R images (https://www.planet.com/).

We used the level 3B Surface Reflectance (SR) product, which is orthorectified and atmospherically corrected by Planet Labs with their in-house processing. The SR product is not corrected for the effects of haze or thin cirrus clouds, stray light, or adjacency effects, and aerosol type is based only on a single, global model (Planet Surface Reflectance Version 2.0, 2023). In addition, though Planet Labs provides an unusable data mask raster layer with each PlanetScope SR image, we found that cloudy pixels were not always detected. Consequently, we also used MODIS MCD19A2 Aerosol Optical Depth and Cloud Mask to further filter images.
## 2.2. MODIS/Terra+aqua MAIAC land aerosol optical depth daily L2G 1 km SIN grid (MCD19A2.061)

We used MCD19A2.061 Aerosol Optical Depth (AOD) daily product at 1 km pixel resolution to filter for aerosols and clouds. It is produced from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. MODIS imagery is divided into 1 × 1 km grid cells and time series are produced for each cell to determine the base background from which to make short-term change comparisons (Lyapustin et al., 2018). A MODIS Quality Assessment (QA) cloud mask band is also provided based on cloud/background difference, as measured by spectral signature, spectral combinations, or temporal and/or spatial fluctuations (Frey et al., 2008; Lyapustin et al., 2018).

## 2.3. Field data and field site characteristics

Field data was collected by the authors in Niakhar and Dahra, Senegal, Gourma, Mali, and Fakara, Niger intermittently from 2009 to 2023. We recorded species, latitude/longitude under the tree canopy, physiological characteristics, and details on leaf on/leaf off stage at the time of recording. Sites were visited once, with most data in the Niakhar, Dahra, and Fakara sites acquired during the dry season. We tried to collect samples of large trees that were isolated to best accommodate the 3-m spatial resolution of the PlanetScope sensors. In addition, we tried to take at least 10 samples of key species at each site while in the field to cover a variety of leaf phenological behaviors and to better make phenological comparisons between sites. Table 1a summarizes the species composition of samples for each site (see also 2.7).

## 2.4. Maxar WV01, WV02, WV03, and GE01 high resolution imagery and crown map

We used a 50 cm processed Maxar mosaic by Tucker et al. (2023) with NDVI and panchromatic bands created from multispectral and panchromatic level 1B WV01, WV03, and GE01 imagery. Field data was then identified using the large database of tree polygons detected with machine learning on the Maxar image mosaic, which ensured that tree crown borders were precisely delineated. This also allowed us to record the tree crown area as estimated by the machine learning algorithm. This data can be found and downloaded by the public at the Oak Ridge National Laboratory's Distributed Active Archive Center: doi: https://doi.org/10.3334/ORNLDAAC/2117.

## 2.5. Methods

### 2.5.1. Satellite data acquisition and MODIS aerosol and cloud filtering

PlanetScope 3B Surface Reflectance data was downloaded in bulk for each site using the Planet API from January 2018 to the end of December 2022. Images were clipped to include smaller more localized clusters of inventoried trees to save on storage and processing time.

In addition, dust is a common problem in the area that can reduce surface contrast and decrease NDVI values anomalously. Therefore, we also used well-tested MODIS products, which use many more bands and can better detect various types of clouds, especially cirrus, and aerosols. The MCD19A2 MODIS/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1 km SIN Grid daily product was downloaded from NASA's LAADS DAAC repository for each study site over the study period.

We matched MODIS and PlanetScope image dates to obtain the MODIS aerosol optical depth and cloud fraction within each PlanetScope image footprint. Because the MCD19A2 MODIS product combines both the Aqua and Terra sensors, there are multiple overpass times throughout the day. Though it is not always at the exact capture time of PlanetScope data, the multiple observations provide a good representation of overall daily atmospheric conditions and cloud cover.

Planet images were then automatically filtered based on the detected aerosol optical depth (AOD). We chose 0.5 as the limiting lower threshold, where any values over this threshold were considered to indicate a moderate to high aerosol thickness.

After AOD filtering, the images were passed through a cloud filter using the MODIS QA cloud mask based on the presence of cloud and cloud shadow bit flags. Because the spatial resolution is vastly different between PlanetScope and MODIS, PlanetScope image footprints that had above 50% cloud cover as measured by MODIS were omitted from further study.

### 2.5.2. Georeferencing

To effectively use the tree polygons produced from 50 cm Maxar imagery, Planet images must be very well georeferenced to the Maxar mosaic as well as to one another. This is particularly important when the study subject consists of just a few pixels at Planet's 3-m resolution considering a small shift between imagery can lead to inconsistent pixel detections outside of the tree polygons. Therefore, all PlanetScope images over the study site were co-registered to the Maxar mosaic using global registration from the open-source Python AROSICS package (Scheffler et al., 2017). The AROSICS algorithm uses phase correlation, which is based on intensity as opposed to object matching. The algorithm also outputs registration information for verification, like x- and y-shifts, and no anomalously high shifts were detected. A minimum crown

<table><caption>**Table 1**
Species composition and leaf phenology type of trees sampled in each of the study sites.</caption><thead><tr><th>Species</th><th>Common name</th><th>Phenology type</th><th>Dahra</th><th>Fakara</th><th>Gourma</th><th>Niakhar</th></tr></thead><tbody><tr><td>Acacia ehrenbergiana</td><td>Salam tree</td><td>Deciduous</td><td></td><td></td><td>5</td><td></td></tr><tr><td>Acacia nilotica</td><td>Scented-pod acacia</td><td>Semi-deciduous</td><td>15</td><td>2</td><td>6</td><td>7</td></tr><tr><td>Acacia raddiana</td><td>Umbrella thorn acacia</td><td>Semi-deciduous</td><td>27</td><td>2</td><td>23</td><td></td></tr><tr><td>Acacia senegal</td><td>Gum acacia</td><td>Deciduous</td><td>6</td><td>5</td><td>2</td><td></td></tr><tr><td>Acacia seyal</td><td>White thorn</td><td>Deciduous</td><td>5</td><td>4</td><td>4</td><td></td></tr><tr><td>Adansonia digitata</td><td>Baobab</td><td>Deciduous</td><td>15</td><td>3</td><td></td><td>9</td></tr><tr><td>Anogeissus leiocarpus</td><td>African birch</td><td>Semi-deciduous</td><td>9</td><td></td><td></td><td>10</td></tr><tr><td>Azadirachta indica</td><td>Neem</td><td>Evergreen</td><td></td><td>16</td><td>1</td><td></td></tr><tr><td>Balanites aegyptiaca</td><td>Desert date</td><td>Evergreen</td><td></td><td></td><td></td><td></td></tr><tr><td>Boscia senegalensis</td><td>Hanza</td><td>Evergreen</td><td></td><td></td><td>2</td><td></td></tr><tr><td>Combretum glutinosum</td><td></td><td>Evergreen</td><td>9</td><td>9</td><td>6</td><td>2</td></tr><tr><td>Cordyla pinnata</td><td>Bush mango</td><td>Deciduous</td><td></td><td></td><td></td><td>7</td></tr><tr><td>Diospyros mespiliformis</td><td>African ebony</td><td>Evergreen</td><td></td><td></td><td></td><td>9</td></tr><tr><td>Faidherbia albida</td><td>Apple-ring acacia</td><td>Inverse deciduous</td><td>11</td><td>37</td><td>2</td><td>16</td></tr><tr><td>Maerua crassifolia</td><td></td><td>Evergreen</td><td></td><td></td><td>1</td><td></td></tr><tr><td>Piliostigma reticulata</td><td>Camel's foot</td><td>Evergreen</td><td></td><td>8</td><td></td><td></td></tr><tr><td>Sclerocarya birrea</td><td>Marula</td><td>Deciduous</td><td>14</td><td>2</td><td></td><td>7</td></tr><tr><td>Tamarindus indica</td><td>Tamarind</td><td>Evergreen</td><td></td><td></td><td></td><td>6</td></tr><tr><td>Ziziphus mauritania</td><td>Jujube</td><td>Semi-deciduous</td><td></td><td>5</td><td>1</td><td></td></tr></tbody></table>
area of 25 m² was set to account for any possible small residual shifts
that co-registration was not able to correct.

2.5.3. *NDVI statistics*

Background polygons (called reference) were also drawn manually
in each of the 1–3 km² clip boxes to serve as a representation of annual
herbaceous vegetation. The purpose was to characterize the background
phenology to provide a baseline comparison for the phenology of the
tree crowns. NDVI rasters were calculated for each multispectral Plan-
etScope image using the basic formula NDVI = (NIR - Red) / (NIR +
Red). Fig. 1 displays a PlanetScope NDVI raster from January 20, 2018,
focusing on a cluster of A. raddiana tree crowns in the Dahra site. The
reference polygon is shown adjacent to the trees. A corresponding 50
cm-resolution WV03 image from February 9, 2015, is included as a
guide, showing how PlanetScope imagery effectively detects the indi-
vidual tree crowns.

For each image, we extracted NDVI raster statistics (mean, max, min) for both individual trees and the reference polygon. All pixels touched by the polygon were counted as well to better account for any possible residual shifts. Maximum crown NDVI was retained to minimize tree crown heterogeneity effect and mixed pixels. Although the reference polygons were manually drawn in areas that could best characterize herbaceous vegetation, the reference polygon may likely have picked up some signal from sub-pixel trees or bushes, or the presence of pixel mixing of surrounding tree boundaries in the 3-m PlanetScope data. These factors could influence the maximum NDVI values within the reference polygon. We therefore used the reference mean, which better represents the high spatial variability of herbaceous vegetation, bare soil, water content, and light and shadow variations across the different areas.

2.5.4. *Averaging and spline model fitting*

Despite filtering using the MODIS aerosol optical depth product, time
series still contained some noise. This is bound to happen given the large
number of fine temporal resolution images, which may vary in daily
atmospheric composition and water vapor content, changes in sun/
sensor geometry, and the presence of shadows. Additionally, with the
130+ PlanetScope sensors in orbit, there is more of a chance of fluctu-
ation in radiometric calibration and/or slight differences in hardware
and hardware durability between sensors. Working at the very fine
spatial scale of individual tree crowns made up of 3–4 PlanetScope pixels
further enhances the effect of these factors on NDVI measurements, as
opposed to working on larger areas that average aggregates of pixels. We

stack the 5-year time series by day of year to compare individuals, as
well as aggregations of phenology types and species inter- and intra-site.
We then applied a periodic spline model to each day of year average to
describe the yearly phenological behavior. The spline model is data-
driven and not constrained by the pre-defined shape of other
phenology models that are sometimes used for temperate phenology
studies (Mader, 2012). It consists of a set of polynomials fit to smaller
sections of the dataset with user-defined “knots.” The fitted polynomials
are then combined at their ends to form an uninterrupted curve (Mader,
2012).

2.5.5. *Leaf phenology parameters*

Leaf phenology parameters were based on the minima of the spline
fits, after Zhang et al. (2003). The leaf phenology of Sahelian trees
inferred from satellite data is difficult to describe using models tailored
to more temperate biomes, based on the start and end of season for
instance, and we expected some errors during the wet season caused by
background herbaceous vegetation phenology (see results and discus-
sion sections). Consequently, we selected a parameter that emphasizes
dry season behavior. This approach provides more reliable comparisons
of variations of woody vegetation between individuals. Based on the
spline model, the minimum dry season NDVI value and corresponding
day of year for each tree were identified using the day of year averages.
This was done for individual trees and trees aggregated by species and
phenology type.

2.5.6. Characterization of phenology along a latitudinal transect

Eight 10 km² sample sites were gathered at one-degree latitudinal
increments ranging from 16.6° and 9.9° latitude, all centered at -1.6°
longitude. For each site, we randomly selected 500 trees of unknown
species from the Maxar tree polygons above a crown area of 30 m²
(where there were enough samples available). All tree profiles were then
averaged and fitted with a periodic spline model to compare overall
woody vegetation phenology between sites along the rainfall gradient.

2.6. *Study area*

The Sahelian zone extends over the African continent, forming a
bioclimatic transition between the Sahara Desert to the north and the
sub-humid Sudanian savannas to the south (Biasutti, 2019). The climate
is arid to semi-arid tropical, and rainfall patterns are driven by the West
African Monsoon (Nicholson, 2013). There is a single wet season that
reaches its peak in August, though a strong climatic gradient towards the

<figure><img src="image_4.png" /><figcaption>Fig. 1. Example of A. raddiana trees in Dahra, Senegal. The left image shows the reference Maxar mosaic image from February 9, 2015, and the right image shows a co-registered PlanetScope NDVI raster from January 20, 2018. Tree crowns appear more pixelated in the PlanetScope NDVI image because of the lower spatial resolution (3-m) as compared to the WV03 image (50 cm). The tree polygons from Tucker et al. (2023) are delineated in orange (left) and black (right). The largest polygon adjacent to the trees represents the reference polygon.</figcaption></figure>
Sahara progressively shortens the duration of the wet season (De Bie et al., 1998). Vegetation is primarily two-tiered, consisting mainly of an herbaceous layer of annual grasses or forbs alternated with scattered trees and shrubs (Le Houerou, 1980). The herbaceous layer responds quickly to precipitation and starts to grow rapidly with the onset of the first rains, followed rapidly by senescence with the onset of the dry season in mid-September (Hiernaux and Houérou, 2006). In cultivated areas, the grasses and forbs layer is overtaken by rain-fed annual crops. Tree inventories were carried out in Gourma, Mali, Fakara, Niger, Dahra and Niakhar, Senegal. These sites characterize the variations in climate, landscapes, and species composition from Senegal on the West African coast and then eastward towards land-locked Mali and Niger in the central Sahelian zone (Fig. 2).

In the Dahra site in Senegal, eight clip boxes were drawn with an average area of 14 km² between 15° 19' 30.7308" N, 15° 30' 44.7768" W and 16° 0' 7.4412" N, 15° 14' 32.2188" W. The average annual rainfall for the years 2018–2022 was 410 mm, and vegetation in the area consists mainly of annual-dominated rangeland with less than 10 % tree cover (Dendoncker et al., 2020).

At the Niakhar site in Senegal, one large clip box covering 125 km² was drawn for the area between 14° 26.18' N, 16° 27.96' W and 14° 30.39' N, 16° 18.95' W. The average annual rainfall for 2018–2022 was 550 mm, and vegetation is crop-dominated, with some *Faidherbia albida*-dominated parkland with 5 % tree cover and a few low-lying rangelands.

In the Fakara site in Niger, seven clip boxes were drawn for the area extending from 13° 30' 41.3568" N, 2° 36' 6.624" E to 13° 34' 31.134" N, 2° 50' 9.3588" E. Average yearly rainfall for the study period was about 520 mm, and woody vegetation accounts for about 5 % of landcover and is made up of either sparsely distributed trees and shrubs or scattered dense shrubs and thickets such as the vegetation formation called tiger bush (Hiernaux et al., 2022).

In the Gourma region of Mali, fifteen clip boxes were drawn with an average area of 9 km² each between the coordinates 15° 52' 26.1048" N, 1° 54' 29.5884" W and 16° 47' 15.2052" N, 1° 14' 22.4016" W. Average yearly rainfall for the study period was about 250 mm. Vegetation is mostly herbaceous, while about 3 % of the land surface is covered by woody vegetation. Land use is primarily pastoral rangelands, with a small percentage of cultivated cropland (Mougin et al., 2009).

Last, the latitudinal transect zone extends from 9° 51' 22.536" N, 1° 38' 41.712" W to 16° 35' 36.24", 1° 33' 18.288" W. The transect increments characterize the strong latitudinal gradient of precipitation

from the north to the south of the Sahelian zone. The transect also passes over the Sahelian biome, more characterized by savanna grasslands, and the Sudanian biome boundary, more characterized by a mix of savanna and woodlands (Le Houérou, 1989). Table 2 gives the center point latitude of each clip box of the transect along –1.6° longitude with the average annual rainfall from 1981 to 2023. All annual rainfall data was based on CHIRPS precipitation data (Funk et al., 2014).

## 2.7. Sahelian phenology types

Sahelian woody vegetation shows diverse phenological patterns that can vary by species, and even between individuals within the same species. Many woody species retain their leaves well beyond the end of the wet season, in contrast to the herbaceous vegetation. Some species start to re-grow leaves before the first rains in what is known as pre-rain green-up, a strategy that permits maximum photosynthesis when the rains arrive (Archibald and Scholes, 2007; De Bie et al., 1998; Ryan et al., 2017).

Different phenological types have been defined for the African dry tropics, though there is no definitive classification system established. Masia et al. (2018) for instance, proposed four classes: evergreen, semi-evergreen (that lose a significant part of their foliage), rain-stimulated deciduous, and obligate deciduous (that flush leaves irrespective of rainfall patterns). Hiernaux et al. (1994) proposed six classes, separating the deciduous category into inverse, short, medium, and long deciduous, and the evergreen category into evergreen with peak leaf flush during the wet season and evergreen with peak leaf flush during the dry season.

<caption>**Table 2**</caption>

CHIRPS annual average rainfall for each 10 km² clip box along the latitudinal transect.

<table><thead><tr><th>Latitude</th><th>Avg. rainfall/yr (mm)<br>(1981–2023)</th></tr></thead><tbody><tr><td>16.6° N</td><td>174</td></tr><tr><td>15.6° N</td><td>278</td></tr><tr><td>14.7° N</td><td>417</td></tr><tr><td>13.7° N</td><td>535</td></tr><tr><td>12.8° N</td><td>713</td></tr><tr><td>11.8° N</td><td>864</td></tr><tr><td>10.9° N</td><td>977</td></tr><tr><td>9.9° N</td><td>1053</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** The study sites where tree inventories were recorded in the field in the Dahra regional and near the village of Niakhar in Senegal, the Fakara region of Niger, and the Gourma region of Mali. Eight additional sample sites were taken centered on –1.6° longitude and between 9.9° and 16.6° latitude at approximately one-degree increments. The blue lines represent the CHIRPS isolines with average yearly rainfall in mm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
Seghieri et al. (2012a) also defined four classes, using short-leafing and long-leafing deciduous types together with evergreen and semi-evergreen. We use here the classification proposed by De Bie et al. (1998), which defines three classes based on field studies of 120 Sahelian woody species in Mali and Burkina Faso. We have also added a fourth class to account for the unique phenology of *F. albida* trees.

1. **Evergreen species:** These species remain in leaf flush throughout the year. They have adapted features that allow them to either access a water source deep in the soil layer during the dry season or reduce physiological processes to maximize water conservation during the dry season. Hiernaux et al. (1994) proposed further subdivisions, but the spectral and spatial resolution of Planet data along with the limited number of large tree samples from each group create a challenge for further subdivision of this class.

2. **Semi-evergreen species:** Species from this group senesce late in the dry season for a short period, on the order of 1–2 weeks, and some species start leaf flush shortly before the start of the wet season. Pre-rain green allows them to also maximize photosynthetic activity with the onset of the rains. These trees also have traits and processes that permit deep water access and/or conservation during the dry season.

3. **Deciduous species:** These species have two distinct leaf-on and leaf-off stages lasting a few months. Leaf shed takes place shortly after the start of the dry season.

4. **Inverse deciduous species:** This group is represented in our study by the single species *F. albida*. Paradoxically, these trees lose their leaves during the wet season, and experience leaf flush during the dry season. Roupsard et al. (1999) attribute this to the species' root system, low spatial density, and low competition with the overall phenology of surrounding vegetation.

## 3. Results

### 3.1. Individual tree crown profiles derived from Planetscope NDVI time series

Fig. 3 shows multiyear NDVI times series, day of year average NDVI, and the corresponding Maxar images used to delineate the tree polygons for five individuals of different species. Each fig. (3a-3f) represents a single tree crown, with the interannual NDVI time series on the left panel, the 5-year average with a fitted spline model on the central panel, and the corresponding tree polygon superimposed on the 50-cm Maxar mosaic image. Rainfall was averaged for the years 2018–2022 in the central panel.

As expected, the impact of the herbaceous signal on wet season NDVI is significant, resulting in the rapid rise and fall in tree crown NDVI at the start and end of the wet season, respectively. For the case of *F. albida*, the wet season NDVI mainly captures the background signal since this species has little or no leaves during this period. Crown transparency, mixed pixel, and adjacency effects variations may cause confusion during that period (see discussion).

Therefore, the analysis was focused only on the dry season and excluded days 180 to 350, a period that covers the average wet season timing across sites. For additional emphasis on patterns during the dry season, we adjusted the day of year chronology on averaged figures to start from day 220.

Individual tree crowns show readily different dry season leaf phenology in the PlanetScope NDVI profiles. *A. indica* (Fig. 3a) displays an evergreen profile with consistently elevated NDVI that remains well separated from the reference during the whole dry season. *C. glutinosum* (Fig. 3b) has a relatively overall lower absolute NDVI value than *A. indica* but maintains an evergreen profile that varies little (slow decrease) throughout the dry season. *A. digitata* (Fig. 3c) has a markedly deciduous NDVI profile. The tree quickly loses its leaves after the rainy season with a rapid decrease in NDVI. The profile then is relatively static during the remainder of the dry season until a clear green-up slightly

before the wet season. *A. raddiana* (Fig. 3e), and *A. nilotica* (Fig. 3d) are classified as semi-deciduous, both decreasing gradually throughout the dry season. Interestingly, the deciduous and semi-evergreen tree species generally appear to green up slightly earlier than the off-crown reference. The *F. albida* (Fig. 3f) tree has a characteristic inverse deciduous profile. Green-up during the dry season is clear, and NDVI remains low during the wet season. As indicated by the minimally changing reference curve, this particular tree was in a landscape that showed little herbaceous green-up, even during the wet season. The tree's phenology profile is therefore particularly marked during the wet season without any additional correction. However, this was not the case for most tree samples of this species, which were surrounded by herbaceous vegetation that was green during the wet season when *F. albida* trees are bare.

### 3.2. Inter- and intra-species variability across individuals of the same site

Four individuals sampled from both Dahra and Fakara illustrate that most tree individuals of the same species demonstrate phenological synchronicity in the same site (Fig. 4). The main differences observed within a species are mostly in absolute NDVI, which can be a result of several non-phenological factors, such as sun/sensor geometry, shadow, and atmospheric conditions as well as tree shapes and crown density. Despite the remarkable similarity in individual tree profiles of the same species, noticeable differences exist between individuals of different species within the same site, indicating the coexistence of various woody vegetation phenologies between species in proximity.

The Dahra site figure (Fig. 4a) compares two deciduous species, *A. digitata* and *A. senegal* to *A. raddiana*, a semi-evergreen species. *A. senegal* demonstrates a classic deciduous phenology, with leaf fall and flush reacting directly to rainfall, as evident in NDVI profiles falling shortly after the wet season. *A. digitata* declines at a moderately slower rate and shows distinct pre-rain green-up before trees of other species, resulting in dynamic U-shaped curves. Despite being classified in the same deciduous phenology category, their phenology patterns differ still, and the curve shapes for each species are distinct. *A. raddiana* trees decrease in NDVI more slowly, and all four individuals in the Dahra site appear to increase slightly between approximately days 0 and 60, showing a consistent bump in all curves. This period coincides with the timing of *A. raddiana*'s flowering and pod setting.

The Fakara site samples from each species (Fig. 4b) are made up of two evergreen species, *A. indica* and *C. glutinosum*, and a deciduous species *A. senegal*. Individuals within the same species show slightly more variability in profiles compared to the Dahra site, but overall differences are not particularly large. *A. indica* has a characteristic evergreen profile, and all four individuals exhibit similar shapes. The four *C. glutinosum* trees, which are in the same evergreen category, show more variability, with lower absolute NDVI and a less clear evergreen pattern. *A. senegal* in Fakara also shows more variable phenology between individuals than *A. senegal* in the Dahra site, with profiles decreasing less strongly during the dry season. *A. senegal* trees in Fakara also have a slightly higher NDVI than *C. glutinosum* trees overall, but *C. glutinosum* trees samples were measured with a consistently lower NDVI than other evergreen species.

Four samples of *F. albida* trees from the same Dahra site were visualized by subtracting the corresponding reference mean from the tree maximum NDVI (Fig. 4c). After subtraction, the *F. albida* trees show a remarkable inverse deciduous phenology during the dry season, with NDVI increasing from approximately days 350 to 50 (Fig. 4d).

### 3.3. Intra-species variability across study sites

The average phenology patterns per species in each of the Niakhar, Dahra, Fakara, and Gourma sites exhibit some variability, yet Fig. 5 depicts consistent overall trends for each species across all sites. There is an observable shift in leaf phenology timing that parallels the start and end of the wet season in each respective site. Both site-averaged curves
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** NDVI times series showing the maximum NDVI pixel within the tree crown (blue) and the reference mean (orange) for individual trees. The left panel is the full 5-year time series, the middle panel is the day of year averages, and the right panel is the tree polygon and associated Maxar image in panchromatic. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Comparison of individual tree crowns from three different species at two sites: 4a. Dahra and 4b. Fakara. Each line represents an individual tree, and each color a species. 4c. Comparison of maximum tree crown NDVI of 4 individual *F. albida* trees in Dahra. 4d. The maximum NDVI values with the reference NDVI mean subtracted at the Dahra site for the same trees in 4c to minimize the impact of the herbaceous NDVI during the wet season.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Comparison of aggregated maximum NDVI average splines for all (5a) *A. digitata*, (5b) *A. raddiana*, (5c) *C. glutinosum*, and (5d) *A. nilotica* trees within each of the study sites (where samples were available). The rainfall for each site is shown on the layered bar graph with its corresponding color indicated in the legend.</figcaption></figure>

for *A. digitata* (Fig. 5a) and *A. raddiana* (Fig. 5b) closely resemble those of individual trees of the same species (Figs. 3 and 4), indicating relatively consistent phenology timing among individuals. *A. digitata* trees, when averaged, exhibit a U-shape across all three sampled sites, characteristic of deciduous phenology. Samples in Fakara, where the wet season starts earlier and is shorter than in Niakhar, start leaf shed earlier, and green up earlier. Niakhar *A. digitata* samples show a slower decline in NDVI during the dry season, but green-up before the rains. The Dahra site *A. digitata* samples also show a rapid decline in NDVI at the start of the dry season, but green-up before the rains is not quite as visible as in the other sites. It should however be noted that *A. digitata* trees near villages in all sites are often exploited by local populations and defoliated for consumption.

*A. raddiana* (Fig. 5b) profiles show the same distinctive bump observed in the individual tree crown profiles between days 0 and 60, coinciding with the flowering and pod-setting period for their respective rainfall patterns. This feature is not observed in other species from the

same sites. All profiles across sites display semi-evergreen phenology, with a very short period without leaves before rapidly experiencing green-up. Pre-rain green-up is not as clearly evident in this species, but it may be difficult to observe if it occurs during a very brief period before the rains.

*C. glutinosum* (5c) demonstrates a relatively wider range of absolute NDVI values and more diverse behavior during the dry season across sites. Niakhar's *C. glutinosum* trees exhibit a distinct evergreen phenology with higher absolute NDVI and a less pronounced decrease during the dry season. In contrast, Gourma and Fakara show lower absolute NDVI values with a more noticeable decrease. Observations in the field at the Dahra and Niakhar sites are consistent with this behavior, where *C. glutinosum* trees varied in leaf shedding and density during January. Additionally, all trees appear to show some pre-rain green-up except for *C. glutinosum*.

*A. nilotica* (5d) trees display more variable behavior. In Dahra and Fakara, they defoliate at a slower rate with classic semi-evergreen
phenology that reaches a minimum at the end of the dry season. Some samples in small depressions that flood during the wet season retain soil moisture longer throughout the year, which may play a role in individual phenology patterns.

### 3.4. Variability in phenology types within sites

Trees aggregated by phenology type generally show surprisingly similar patterns, except for inverse deciduous trees in Gourma, which were made up of *F. albida* samples on generally vegetation-less backgrounds (Fig. 6). The distinctions between species aggregated by phenology type for each site are much less pronounced than initially expected. Evergreen and inverse deciduous trees consistently exhibit higher NDVI than the other groups in all sites during the dry season, while deciduous species are generally the first to decrease. Interestingly in Niakhar, Dahra, and Fakara, deciduous trees green-up slightly earlier than other types.

However, there is much more apparent variability in the averaged curves grouped by species in each site (as shown in Fig. 5) than among those grouped by phenology. In addition, when we plot the phenology curves of five individual trees from the same site, each representing different species of varying phenology types (Fig. 7), there is more apparent variability between neighboring trees than between the averaged phenology types. This implies that species plasticity and species-specific timing may be causing overlaps in phenological categories.

### 3.5. Minimum dry season NDVI date by phenology group and species

Tree samples aggregated by phenology group (Table 3) across all sites show that deciduous trees consistently lose their leaves earliest, which is coherent with expectations. Inverse deciduous trees consistently show the latest minimum NDVI day of year, falling at the tail end of the dry season period that was analyzed. Semi-evergreens also tend to reach their minimum NDVI towards the end of the dry season, but they are irregularly alternated with evergreen species. Both groups reach their minimum at the end of the dry season, but no clear pattern emerges, making it difficult to delimit the boundary between the two categories. Aggregation by phenology group, in the end, resulted in the averaging out of important information in the variability of evergreen and semi-evergreen behavior.

Tree samples aggregated by species (Table 4) give more insights into the boundary between semi-evergreen and evergreen. The clear

evergreen signals of *A. indica*, *T. indica*, and *D. mespiliformis* have significantly higher minimum NDVI values, indicating the presence of a high density of green leaves. However, many other evergreen species are inconsistent and have a low minimum NDVI value comparable to semi-evergreen or deciduous species. Although other phenology groups are consistent with expectations, characterizing species as evergreen is difficult using only the minimum NDVI value and day of year during the dry season.

### 3.6. Characterization of average leaf phenology of woody vegetation along a latitudinal transect

The phenology of woody vegetation progressively changes along the latitudinal transect with each degree increment (Fig. 8), and it is highly correlated with the rainfall gradient (as shown in Fig. 8d's bar graphs).

Fig. 8c shows the minimum NDVI date for the phenology curves along the transect, as well as the day of year for the start of the rainy season (4% of the yearly cumulative rainfall) as a function of latitude. As shown in Fig. 8d, green-up tends to start just after NDVI reaches a minimum, therefore this date can serve as an approximation for green-up. There is a clear linear relationship between the minimum NDVI day of year and latitude. A change between the 12.8° and 13.7° latitude is apparent, where there is a noticeable shift in curve shape from a long deciduous pattern to a more pronounced deciduous shape together with the shortening of the dry season and lengthening of the growing season moving southward. This latitude corresponds also to the change from the Sahelian (semi-arid) bioclimate to the Sudanian (sub-humid) bioclimate.

Furthermore, there is an increasing presence of pre-rain green-up in the more southern latitudes, shown by the gradually increasing time gaps between the minimum NDVI day of year and the start of the rainy season day of year moving southward.

The Maxar image clips show the progressive changes in vegetation composition and density with each latitudinal increment, further reflected in the NDVI data. The Sudanian 9.9° latitude, where woodlands are more abundant, has a largely different amount of tree cover compared to the Sahelian 16.6° latitude, which is mostly made up of dry savanna grasslands, where woody vegetation is more scattered. However, despite the longer dry season in the more northern latitudes, it is remarkable that woody vegetation remains green for as long as it does, with average phenology curves showing an overall semi-evergreen pattern that does not reach a minimum until the tail end of the dry

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** All species aggregated by phenology type according to our classification for each site—(6a) Dahra, (6b) Fakara, (6c) Niakhar, and (6d) Gourma. Only the dry season is shown instead of the full year.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** The phenology curves of five individual tree crowns of different species from a variety of phenology groups in the Fakara site. Only the dry season is shown instead of the full year.</figcaption></figure>

<table><caption>**Table 3**
Samples in all sites aggregated by phenology group showing overall statistics.</caption><thead><tr><th>Phenology group</th><th>Average min day</th><th>Min day standard deviation</th><th>Average min NDVI value</th><th>Min NDVI standard deviation</th><th>Sample count</th></tr></thead><tbody><tr><td>Deciduous</td><td>116.78</td><td>28.574</td><td>0.198</td><td>0.029</td><td>88</td></tr><tr><td>Evergreen</td><td>138.04</td><td>19.131</td><td>0.209</td><td>0.046</td><td>135</td></tr><tr><td>Semi</td><td>142.02</td><td>16.368</td><td>0.197</td><td>0.032</td><td>107</td></tr><tr><td>Inverse</td><td>154.76</td><td>17.178</td><td>0.208</td><td>0.025</td><td>66</td></tr></tbody></table>

season. Moving southwards, the dry season increasingly shrinks in duration, and the phenology of woody vegetation has more of a bi-seasonality, with a shorter senescence and green-up period, though the period without leaves becomes increasingly smaller.

The overall absolute NDVI also gradually increases with decreasing latitude (Table 5), as there is increasing tree cover moving southwards. However, in the more northern latitudes, there is a higher proportion of bright soil compared to relatively sparse tree crown cover, and trees are generally smaller and less dense. It is most likely that the 3-m Planet-Scope data NDVI values are reduced by the higher chance of mixing of tree-background reflectance moving northwards. Also notable is the gradual appearance of pre-rain green-up in the more southern Sudanian latitudes, where the phenology curve increases noticeably before the start of the main wet season. The effect becomes particularly observable

at 11.8° latitude until 9.9° latitude.

## 4. Discussion

The results over the study sites show the potential of Planet data in capturing individual tree leaf phenology and analyzing phenological functional types. In this respect, PlanetScope can achieve on the scale of the individual tree, but with the same spatial reach, what has been done previously with more established datasets such as AVHRR, SPOT-VGT, and MODIS at the plot scale to describe leaf phenology (Brandt et al., 2016; Butt et al., 2011; Heumann et al., 2007; Tong et al., 2020, among others). The daily revisit time has been shown to be critical for such studies, making PlanetScope more suited than Sentinel-2 or Landsat time series, which also have limitations due to lower spatial resolution (Cheng et al., 2020).

### 4.1. Limitations and sources of noise

#### 4.1.1. Radiometric inter-calibration of Dove sensors

Because there are 130+ Dove sensors currently in orbit, radiometric differences between the satellites potentially contribute to noise affecting the time series since inter-calibration for multiple sensors requires great care (Brown et al., 2006). Radiometric inter-calibration issues for the Dove Classic satellites in the past have been noted by other studies (Frazier and Hemingway, 2021; Latte and Lejeune, 2020;

<table><caption>**Table 4**
Samples in all sites aggregated by species.</caption><thead><tr><th>Species</th><th>Average min day</th><th>Min day standard deviation</th><th>Average min NDVI value</th><th>Min NDVI standard deviation</th><th>Sample count</th><th>Phenology group</th></tr></thead><tbody><tr><td>A. digitata</td><td>97.6</td><td>22.3</td><td>0.213</td><td>0.017</td><td>27</td><td>deciduous</td></tr><tr><td>C. pinnata</td><td>117.3</td><td>21.5</td><td>0.197</td><td>0.007</td><td>7</td><td>deciduous</td></tr><tr><td>A. ehrenbergiana</td><td>119.6</td><td>56.4</td><td>0.131</td><td>0.024</td><td>5</td><td>deciduous</td></tr><tr><td>A. senegal</td><td>124.2</td><td>11.2</td><td>0.194</td><td>0.024</td><td>13</td><td>deciduous</td></tr><tr><td>A. seyal</td><td>126.7</td><td>18.6</td><td>0.196</td><td>0.040</td><td>13</td><td>deciduous</td></tr><tr><td>P. reticulatum</td><td>127.9</td><td>10.3</td><td>0.197</td><td>0.016</td><td>8</td><td>evergreen</td></tr><tr><td>S. birrea</td><td>128.8</td><td>27.8</td><td>0.197</td><td>0.017</td><td>23</td><td>deciduous</td></tr><tr><td>Z. mauritania</td><td>131.7</td><td>5.9</td><td>0.170</td><td>0.036</td><td>6</td><td>semi-deciduous</td></tr><tr><td>B. senegalensis</td><td>133.5</td><td>1.5</td><td>0.181</td><td>0.003</td><td>2</td><td>evergreen</td></tr><tr><td>C. glutinosum</td><td>135.3</td><td>17.4</td><td>0.188</td><td>0.023</td><td>50</td><td>evergreen</td></tr><tr><td>A. indica</td><td>136.4</td><td>11.5</td><td>0.299</td><td>0.029</td><td>17</td><td>evergreen</td></tr><tr><td>B. aegyptiaca</td><td>137.7</td><td>16.4</td><td>0.195</td><td>0.032</td><td>42</td><td>evergreen</td></tr><tr><td>A. raddiana</td><td>139.8</td><td>11.4</td><td>0.193</td><td>0.034</td><td>52</td><td>semi-deciduous</td></tr><tr><td>A. nilotica</td><td>145.6</td><td>21.4</td><td>0.202</td><td>0.027</td><td>30</td><td>semi-deciduous</td></tr><tr><td>A. leiocarpus</td><td>145.7</td><td>18.3</td><td>0.209</td><td>0.020</td><td>19</td><td>semi-deciduous</td></tr><tr><td>D. mespiliformis</td><td>151.8</td><td>34.1</td><td>0.232</td><td>0.031</td><td>9</td><td>evergreen</td></tr><tr><td>F. albida</td><td>154.8</td><td>17.2</td><td>0.208</td><td>0.025</td><td>66</td><td>inverse deciduous</td></tr><tr><td>T. indica</td><td>155.8</td><td>22.6</td><td>0.237</td><td>0.017</td><td>6</td><td>evergreen</td></tr><tr><td>M. crassifolia</td><td>177.0</td><td>0.0</td><td>0.141</td><td>0.000</td><td>1</td><td>evergreen</td></tr></tbody></table>
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

12.8°

11.8°

10.9°

9.9°

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /><figcaption>**Fig. 8.** a. Maxar image clips of each area sampled by latitude for the 9.9° to 16.6° transect. 8b. Average NDVI plotted together for the sampled 500 trees above 30 m² (where enough samples were available), starting on day 220. 8c. Average minimum NDVI day of year and average start of season (SOS) rainfall as a function of latitude. 8d. The same sampled trees for each latitude against average rainfall of each transect site for the 2018–2022 period.</figcaption></figure>

Martins et al., 2022). (Huang and Roy, 2021) for example, studied radiometric continuity between different generations of Dove sensors and found as much as a 20 % difference in atmospherically corrected bands between PlanetScope-0 and PlanetScope-1 sensors, and they attribute this to slightly different spectral response functions and bandwidths between the two generations of sensors. Though it is a slight difference, the study however assumes sensors of the same generation

are inter-calibrated with one another, which may not be the case. This goes to show the challenges associated with radiometric intercalibration. We did not, however, find any satellites that were systematically above or below the average signal.

Besides radiometric inter-calibration, uncertainties in NDVI values may also be due to aerosols, haze, or cloud presence in images that were not effectively filtered by the MODIS data. The disparity in overpass
<table><caption>**Table 5**
Average minimum NDVI value and day of year for 500 tree samples in a latitudinal transect.</caption><thead><tr><th>Latitude</th><th>Average min day</th><th>Min day standard deviation</th><th>Average min NDVI</th><th>Min NDVI standard deviation</th></tr></thead><tbody><tr><td>16.6</td><td>169.89</td><td>8.25</td><td>0.141</td><td>0.016</td></tr><tr><td>15.6</td><td>137.85</td><td>17.94</td><td>0.156</td><td>0.016</td></tr><tr><td>14.7</td><td>125.66</td><td>13.28</td><td>0.184</td><td>0.018</td></tr><tr><td>13.7</td><td>132.26</td><td>28.53</td><td>0.195</td><td>0.046</td></tr><tr><td>12.8</td><td>90.72</td><td>7.46</td><td>0.222</td><td>0.026</td></tr><tr><td>11.8</td><td>63.41</td><td>11.59</td><td>0.287</td><td>0.050</td></tr><tr><td>10.9</td><td>50.63</td><td>6.93</td><td>0.240</td><td>0.051</td></tr><tr><td>9.9</td><td>31.82</td><td>4.70</td><td>0.327</td><td>0.062</td></tr></tbody></table>

times between MODIS and PlanetScope sensors, as well as differences in spatial resolution, could have contributed to additional noise.

### 4.1.2. Crown size and canopy characteristics

Small crown sizes and crowns with gaps introduce noise, as Planet-Scope's 3-m pixels may be considered coarse resolution relative to individual small tree crowns in dryland areas. Crowns below 25 m² likely consist of pixels mixed with background and tree crowns, causing a reduction in NDVI values during the dry season and an increase during the wet season when herbaceous vegetation is green (Brandt et al., 2020). Geolocation changes, even minor shifts on the order of half a pixel, are also more likely to impact smaller tree crowns than larger ones. As a result, most larger trees are well observed with PlanetScope whereas smaller tree crowns, less dense tree crowns, and shrubs are not. This poses a significant limitation in the study area, as there can be many tree crowns and bushes with an area below 25 m² (e.g. Brandt et al. (2020), Fig. 3a and d). This may overlook certain species that generally have smaller tree crowns.

The chosen threshold for tree crown area in the study overlooks details in the phenology of species with typically smaller crowns, such as *Maerua crassifolia* and *Boscia senegalensis*. These species have small tree crowns with an evergreen phenology that renews leaves in the dry season rather than the wet season (Hiernaux et al., 1994). However, capturing such subtle changes among evergreen trees using PlanetScope data proves challenging, leading to the decision to use the four more general phenology groups. Higher resolution sensors or field studies could potentially allow for more detailed subdivisions of categories into more specific phenological behaviors, such as Hiernaux et al. (1994), who used four deciduous and two evergreen groupings.

Furthermore, individualized plant physiological characteristics, such as canopy architecture, and leaf size, shape, thickness, waxiness, distribution, orientation, and age (Chavana-Bryant et al., 2017), as well as elements such as shadowing and soil background (Franklin et al., 1991) impact leaf and overall canopy spectral properties. For example, *B. aegyptiaca* leaves are small and spiny, and the canopy is full but still quite see-through, which has an impact on the NDVI signal measured by the sensor. These are details that are not always identifiable using only remotely sensed imagery, but they could explain the variability in NDVI between individuals and species, especially the low NDVI of some evergreen species.

### 4.1.3. Adjacency

The PlanetScope surface reflectance algorithm does not account for the adjacency effect ("Planet Surface Reflection Version 2.0," 2023), which is the scattering of radiation in the atmosphere that may cause the reflectance of some of the areas surrounding the subject to be mixed in with the reflectance of the target, especially in heterogenous landscapes (Lyapustin and Kaufman, 2001). Houborg and McCabe (2017) studied Landsat 8 and Planet Labs RapidEye imagery and the impact of adjacency on NDVI values of agricultural areas in the desert of Saudi Arabia. The authors found that reflectance from the dry, desert background

lowered NDVI in the humid agricultural areas by up to 50% due to the adjacency effect.

In the context of the Sahel, the contrast between tree crowns and the background soil brightness lowers tree NDVI values during the dry season, which is less the case during peak rainy season since the contrast in background and tree canopy reflectance is low. Though the significance of the adjacency effect on results is beyond the scope of this study, it is considered a likely source of error especially when the background (herbaceous) is changing rapidly.

### 4.2. Species plasticity

Identifying and categorizing species by pre-determined phenology groups proves challenging, as tree species tend to show a degree of plasticity and display species-specific phenology patterns. Plasticity refers to the inter- and intra-species phenological variability as determined by several local environmental factors. The results demonstrate that the phenological types of tree species may overlap, making some of the initially described categories less definitive than anticipated. This is especially true when phenology is studied over multiple sites that have different environmental characteristics, as in this study.

The overlap between some phenology types or some species suggests that plasticity is relatively widespread, as it has been noted for some species that are ubiquitous in many different parts of the Sahel. For example, Seghieri and Simier (2002) found during field surveys of *G. senegalensis* at various sites under differing environmental conditions in southwest Niger that the typically evergreen species avoids water loss during times of limited availability by behaving as a deciduous species. Other examples of species that display plastic phenological behavior are *B. aegyptiaca* (Seghieri et al., 2012b), *T. indica* (Bourou and Van Damme, 2011), and *A. raddiana* (Do et al., 2008).

Plasticity has two implications for further study of the phenology of individual trees in the Sahel. Firstly, species identification by phenology is probably restricted to local studies or some specific species (*F. albida* for instance (Lu et al., 2022)). Secondly, it is important to monitor tree phenology within the context of climate change to understand how phenological patterns will adapt to environmental changes including climate.

### 4.3. The ambiguity of phenological groups

In the Sahel, moisture availability primarily controls leaf phenology event timing and deciduous and evergreen phenology are often discussed in the literature as two distinct survival strategies that optimize water use in trees. (Seghieri et al., 2012a; Givnish, 2002; Archibald and Scholes, 2007). However, understanding how water influences phenology is challenging because Sahelian woody vegetation phenology does not follow precipitation patterns exactly, as evidenced by the occurrence of pre-rain green-up in many species and greenness extending in the dry season. Instead, the survival of woody vegetation in the Sahel is governed by morphological and physiological traits that determine water storage, conservation, and access during the dry periods (Archibald and Scholes, 2007; Borchert, 1994; Seghieri et al., 2012a; Singh and Kushwaha, 2005). Seghieri et al. (2012a) describe deciduous and evergreen phenologies as two opposite ends of a spectrum of survival strategies. Our results agree with this perspective, with distinct profiles of deciduous or evergreen species at either end of the spectrum. Many species classified as 'deciduous' indeed behave as expected and consistently have the earliest minimum NDVI date, indicating earlier leaf fall. Certain tree species on the opposite end of the spectrum, like *A. indica*, *T. indica*, and *D. mespiliformis*, unmistakably show an evergreen phenology in the results. However, a more nuanced division exists among species that fall more in the middle of the spectrum. Semi-evergreen and evergreen species exhibit a less distinct pattern, indicating a potential overlap between the two groups.

The existence of different sub-species can account for additional
phenological variation between individuals, as is the case with *A. nilotica*, *A. raddiana*, and *C. glutinosum*, though this study did not identify sub-species. In each study site, however, most individuals of the same species showed a minimal amount of phenological variation. Instead, results showed that variation among species within the same phenology group was much more evident than between individuals in the same site. This points to the difficulty of categorizing individual species into distinct groups, as phenology groups are on a spectrum rather than clearly separated. Individuals of different species may shed their leaves successively within a time frame that still places them within the same phenology group, but when aggregated, the subtle transitions between phenology timing of species are lost. This explains the surprising lack of variability displayed between phenology groups that was observed.

The idea that many species elude clear-cut phenology classifications is even further supported by the inconsistent placing of species in phenology groups by different studies. For example, *C. glutinosum* was classified as a deciduous species by De Bie et al. (1998), Seghieri et al. (2012a) classify *A. seyal* as a semi-evergreen species, and Lykke et al. (2004) consider *P. reticulatum* and *D. mespilioformis* evergreen species. This highlights the need for more species-level studies to obtain a better understanding of the phenological heterogeneity of Sahelian woody vegetation.

### 4.4. The effect of site and regional climatic conditions

Comparing species between different sites highlights the significant impact of climatic controls on the phenology of a given species at each site. While many species exhibit similar phenological patterns, their timing varies in conjunction with the onset of the wet season specific to each site. For instance, *A. digitata* trees in the Fakara region experience earlier senescence than those in Dahra, and finally Niakhar. This illustrates the intricate relationship between the timing of phenological events and the climatic conditions prevailing in the species' native habitats where they have adapted. Though it may seem straightforward to link phenological timing exclusively to precipitation, its direct role in phenological cues may be more nuanced (Adole et al., 2019). Precipitation is just one of the factors among many that have been hypothesized to trigger phenological events such as temperature, photoperiodicity, and atmosphere humidity (Adole et al., 2019; Archibald and Scholes, 2007; Breman and Kessler, 1995; Ryan et al., 2017; Seghieri et al., 2012a, 2012b).

Supporting this idea, a study by (Bamba et al., 2019) observed that *A. digitata* individuals from 27 sites across West and East Africa demonstrated remarkably synchronized leaf phenology events with the climatic patterns of their respective origins, despite being cultivated under uniform conditions. According to Bamba et al. (2019), this finding indicates a certain level of genetic adaptation among individuals of the same species to their specific climatic environments.

### 4.5. The unique dry season vegetation activity in Sahelian trees

The findings from the transect analysis show that on average Sahelian trees retain their leaves for a long period after the onset of the dry season, which is unexpected for an area with minimal rainfall. Indeed, moving northward, the dry season increases duration gradually, and precipitation during the wet period becomes more unpredictable with increasing variability in quantity and distribution (De Bie et al., 1998). The general patterns of woody phenology along the rainfall gradient observed in this study agree with Breman and Kessler (1995), who suggests that the proportion of evergreen and semi-evergreen phenology types increases moving northward. From 16.6° latitude to 12.8° latitude, the aggregated phenology curves resemble an overall semi-evergreen phenology, while south of this latitude, aggregated phenology curves start to resemble increasingly deciduous patterns with pre-rain green-up.

To cope with the longer dry periods and water stress, evergreen and many semi-evergreen trees in the Sahel have been shown to tap water in the deep soil layers, where herbaceous vegetation does not have access, or they possess features like scleromorphic leaves to minimize water loss (Breman and Kessler, 1995; De Bie et al., 1998; Do et al., 2005). These species are more drought-resistant as opposed to deciduous species, which are more sensitive to rainfall variability. Several studies have documented cases where woody species biodiversity declined significantly with changes in rainfall patterns (like the Sahelian drought of the 1970s and 1980s), with mesic deciduous species being replaced with more resilient xeric evergreens and semi-evergreen species (Brandt et al., 2014; Hiernaux et al., 2009; Kusserow, 2017; Herrmann and Tappan, 2013).

In contrast, Sudanian trees, on average, tend to drop leaves early, which may be an adaptation to fire (Innes, 1972; Breman and Kessler, 1995), and to flush leaves early to fully benefit from the first rains. Some species start to sprout immediately after a fire event, while some species even depend on fire cues for seed germination and reproduction (Bond and Van Wilgen, 2012; Dayamba, 2010; De Bie et al., 1998; Innes, 1972).

### 4.6. Evidence of pre-rain green-up

Our results confirm the prevalence of pre-rain green-up in trees, though strictly speaking, we observe a pre-grass (annual grasses, not perennials) leafing rather than a pre-rain leafing for most trees since the tree leafing does not occur long before greening of herbaceous. This leaves room for more detailed studies. Our results showed a pre-rain green-up period of two to three weeks before herbaceous vegetation green up at least, most strikingly in woodland phenology in the lower latitudes of the transect. The higher latitudes seem to show some pre-rain green-up, but not to the extent of the lower latitudes. This is in agreement with findings by Adole et al. (2019), who found that pre-rain green-up was quite widespread throughout the African continent, with large lag times in Sudano-Saharan woodlands towards the south, and very short lag times in Sahelian grasslands to the north.

### 4.7. Future perspectives

Despite the adaptive capacity of woody vegetation to survive in semi-arid and arid environments, the uncertainty associated with climate change raises questions about the limits of species adaptability. This carries significant implications for global biodiversity, ecosystem resilience, and effective conservation and management strategies in the face of a changing climate (Duputié et al., 2015; Savolainen et al., 2007). Annual rainfall is expected to increase in most of West Africa (Monerie et al., 2020). Temperature is increasing rapidly, especially in the early and late dry season (Guichard et al., 2020), which is expected to be followed by increased air dryness. Our results on Sahelian phenology suggest that tree function during the dry season is particularly important to monitor.

## 5. Conclusion

This study demonstrates the capability of PlanetScope NDVI time series to characterize the leaf phenology of individual trees in the Sahel. The findings showed that PlanetScope NDVI time series effectively captured the dry season phenology of individual trees in the Sahel. Different leaf phenology types and variability among species and sites were also assessed. Species showed a minimal degree of variability within the same site, but different species showed markedly different phenology profiles in the same site. Aggregations by species per site showed that species have a similar phenology profile pattern in all sites, though profiles shift according to the timing of the wet season.

Trees aggregated by the four phenology types (evergreen, deciduous, semi-deciduous, and inverse deciduous) showed an unexpected lack of
variability compared to the variability between species. The study also showed ambiguity between phenology functional types, with certain species having distinctly deciduous or evergreen profiles. Others had intermediate characteristics that were difficult to classify, particularly many evergreen species with weaker signals due to smaller crown size or scleromorphic leaf features.

The average woody vegetation phenology was analyzed for eight sample sites along the north to south rainfall gradient, which showed the progressive changes in overall woody plant dynamics along the latitudinal rainfall gradient. Trees in the northernmost Sahel exhibited an overall semi-evergreen pattern, whereas those in the Sahelo-Sudanian region displayed a more deciduous phenology with early leaf shedding and pronounced pre-rain green-up.

The methodology presented in this study shows the possibility of using PlanetScope data for large-scale detection and mapping of phenology, which could have significant implications for a range of different fields of study, such as resource management, hydrology, climate science, and ecology.

## CRediT authorship contribution statement

**Yasmin Fitts:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Compton Tucker:** Supervision, Resources, Project administration, Investigation, Funding acquisition. **Pierre Hiernaux:** Writing – review & editing, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Yves Auda:** Writing – review & editing, Methodology, Investigation, Formal analysis. **Laurent Kergoat:** Writing – review & editing, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

The authors do not have permission to share data.

## Acknowledgments

Field work was carried out by the AMMA-CATCH observatory in Mali, Senegal, and Niger (http://www.amma-catch.org), and analysis was carried out at both NASA Goddard Space Flight Center in Greenbelt, Maryland, and the Géosciences environnement Toulouse Laboratory in Toulouse, France. Work was funded by Compton Tucker under the Hydrosphere, Biosphere, and Geophysics (HBG) support services contract with Science Systems and Applications, Inc. We would like to thank the reviewers for their helpful suggestions for the manuscript. The research project was paid for NASA's Commercial Satellite Data Acquisition Program with award number 880292.04.02.01.01.

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