One of the constraints on the effective management and assessment of the terrestrial system at sub-field scales (e.g., 1–10’s of meters) lies in the challenge of achieving both high spatial and high temporal retrievals [1
]. Satellite observations have made considerable progress in advancing such a capacity, but are inevitably constrained by optics, bandwidth, or orbital configurations that limit optimal spatio-temporal characteristics. One approach that has recently been proposed to overcome such limitations is the use of so-called constellations of nano-satellites i.e., a large number of small, compact sensor units (~10 kg) that are typically cheap, replaceable, and take advantage of economies of scale [2
]. In recent years, the capacity to survey the entire Earth at very high spatial resolution and high frequency (daily) has approached operational reality, with a number of commercial efforts exploring such an observation strategy.
One such company, Planet Labs (www.planet.com
), operates the largest constellation of satellite systems in orbit, with nano-satellite “Doves” collecting RGB (i.e., red, green, blue) imagery at 3–5 m resolution on a daily scale, based on a full constellation of 150–200 satellites. Through such imagery, the opportunities for change detection and surface characterization are enormous. From an agricultural monitoring perspective for instance, timely and repeatable information on within-field variability in growing conditions has specific utility in precision agriculture [3
] as a means to optimize production efficiencies via more sustainable and spatially explicit management practices [5
]. Potential application of such data span diverse aspects of earth observation and provide numerous opportunities for advancing science and operational outcomes. Such an observational strategy represents a revolution in earth observation.
Traditional single-source satellite missions are costly and physically constrained in terms of spatial resolution and frequency of image capture. For instance, Landsat-8 is the continuation of an impressive 40-year mission heritage that has demonstrated great capacity for land surface characterization and monitoring at high spatial resolution [7
]. The advances that have been made with Landsat observations result partly from the high quality sensor designs, high signal to noise ratios, and impressive absolute radiometric accuracies and reflectance error specifications [8
]. However, the 16-day revisit cycle constitutes a major limitation for applications requiring time-critical information on surface conditions. Improvements in spatio-temporal frequency will be realized by the pair of Sentinel-2 satellites, which will deliver 10–60 m resolution multi-spectral data with a five-day revisit capability [10
]. In addition, Landsat-8 and Sentinel-2 synergies offer opportunities to further enhance the temporal resolution [11
]. Nevertheless, the simultaneous requirements of very high spatial resolution (<10 m) and near-daily frequency are currently only met through targeted acquisition via programmable commercial multi-sensor systems such as WorldView and RapidEye [12
]: and even then on a spatially limited basis.
While Planet’s Doves will provide very high spatial resolution imagery at an unprecedented frequency over the terrestrial surfaces of the Earth, the deliberately inexpensive sensor designs and commercial off the shelf components do not match the signal-to-noise characteristics, radiometric performance, cross-sensor consistency, and spectral enhancements of satellite imagers that comprise more traditional space agency based operational missions. In addition, the lack of at-sensor radiance conversions and atmospheric correction of the RGB imagery will affect the interpretation and time and space consistency of any time-series data. Bands in the visible domain are particularly sensitive to the atmospheric correction process [13
] and failure to account for contaminations from time-varying atmospheric influences may significantly impair the utility of the RGB imagery for reliably inferring actual changes in surface cover conditions.
Broadband imaging in just the RGB domain presents a potential limitation for vegetation monitoring and categorization. Such applications typically require information in the near-infrared (NIR) to represent the reflectance plateau of green vegetation and is the spectral region most responsive to changes in vegetation density [15
]. The Normalized Difference Vegetation Index (NDVI), developed based on pioneering work by Rouse et al. [16
] and Tucker [17
] using bands in the NIR and red domain, i.e., NDVI = (NIR − red)/(NIR + red), has shown to be an effective and widely used indicator of spatio-temporal changes in vegetation growth and distribution [18
], vegetation stress [21
], and vegetation productivity [24
]. However, indices based entirely on the visible range of the spectrum have also demonstrated utility for assessing vegetation growth and development [27
]. The Green-Red Vegetation Index serves as a visible analog to NDVI and has been shown to correlate strongly with that index [17
], while the Visible Atmospherically Resistant Index (VARI) has demonstrated utility for estimating green vegetation fractions [27
]. However, the robustness of these indices for assessing vegetation density remains particularly challenged by confounding factors introduced by the atmospheric medium [30
] and pigment absorption [31
], and significantly reduced sensitivity to changes in vegetation density with increasing leaf area [32
]. Important advances in monitoring of vegetation status and retrieval of plant biochemical, structural, and physiological quantities have been realized with the development of a variety of broadband and narrowband vegetation indices that exploit specific regions and features of the electromagnetic spectrum [33
]. However, a standard index such as the NDVI continues to serve as a very simple and useful satellite observable metric in the remote sensing of vegetation response [36
A key objective of the current study is to exploit the high radiometric quality and near-infrared imaging capability of Landsat-8 to maximize the utility of Planet’s RGB time series imagery for agricultural applications. The capacity of the RGB to reproduce insights provided by traditional Landsat NDVI dynamics was investigated using a data mining approach. An accurate translation of the RGB imagery into maps of Landsat consistent NDVI is seen as a particularly useful step given the recognition of NDVI as a key metric for the state and functioning of vegetation. To do this, associations between the NDVI and RGB data were established on a scene-specific basis using atmospherically corrected Landsat NDVI, in an attempt to minimize cross-sensor inconsistencies (i.e., resulting from many individual Dove cameras) and time and space varying contaminations from atmospheric influences. Coarser resolution (i.e., 250 m) NDVI from the Moderate Resolution Imaging Spectroradiometer (MODIS) were also integrated into the downscaling framework to account for surface variations in NDVI occurring between the Planet and Landsat scene acquisitions. An important objective was to develop an operational framework, building upon an automated processing stream and generic techniques that are capable of translating dense time-series of raw RGB imagery into Landsat consistent and atmospherically corrected estimates of NDVI at 3–5 m resolution.
Planet’s flock of Dove satellites has been collecting high spatial resolution imagery in broad red, green, and blue spectral bands since March 2014. Daily image capture on a global scale is anticipated with a full constellation of Dove satellites in the near future. While unique in terms of spatio-temporal resolution, the limited spectral resolution and lack of atmospherically corrected or radiometrically calibrated radiance data may pose a limitation for reliably characterizing the changing conditions of land surface features generally, and vegetation canopies specifically. To address such potential restrictions, Planet’s RGB imagery were translated into Landsat-8 consistent and atmospherically corrected NDVI.
Whenever a NIR retrieval capability is lacking, the translation of the visible (i.e., RGB) imagery into estimates of NDVI is not straightforward. To overcome the RGB data constraint, a data mining approach based on establishing rule-based predictive regression models was shown to be effective at predicting Landsat observed NDVI from information contained in the red and green bands of the Planet imagery. NDVI predictability was best, i.e., characterized by a MAD of 0.014 (9.2%), when a Landsat acquisition within a few days of the Planet acquisition was available to train the rule-based models. The MAD increased to 0.021 (13.7%) when the Landsat NDVI training image was further away (i.e., 11–16 days) from the Planet acquisition and MODIS NDVI images were used to inform on the change in NDVI occurring between the overpasses.
The Planet NDVI predictions were aggregated to the Landsat 30 m resolution in order to facilitate scale consistent pixel-wise comparisons. While efforts were made to ensure precise co-registration of the imagery (via the pixel shifting approach described in Section 2.3.1
), differences between the Planet pixel aggregation and the Landsat-8 cubic convolution resampling technique are likely to affect the pixel comparability, particularly at field boundaries. As a result, the prediction accuracy was also assessed on the best performing majority (i.e., 80%) of the data, which reduced the overall MAD to ~0.006 (~4.5%) and ~0.008 (~7%) (Table 2
) for the Landsat-only and MODIS informed cases, respectively. The retrieval accuracy levels may be impacted by the number of days between the Planet acquisition and the Landsat NDVI image used to validate the Planet NDVI predictions. For example, rapid changes in surface cover conditions between the Landsat and Planet overpasses on DOY 288 and 292, respectively, raised some questions on the validity of the Landsat NDVI image on DOY 288 for evaluating the Planet predictions on DOY 292. Overall, reported prediction accuracies were highest for Planet imagery associated with a Landsat NDVI validation image acquired within two days of the Planet imagery.
The high correlations (r2
> 0.9) reported between the Planet explanatory data (red, green, GRVI) and the Landsat target NDVI, might seem surprising given the lack of NIR information. Dense green vegetation canopies are characterized by a surface reflectance peak in the green wavelength domain and a reflectance minimum in the red domain (absorption maximum by chlorophyll pigments), whereas the typical background soil reflectance signal is characterized by a steady increase over the visible domain [15
]. As a result, the reflectance contrast between green and red reflectances will directly relate to changes in vegetation density. This makes a visible normalized difference index such as the GRVI a particularly good proxy for NDVI, as has been observed in a number of previous studies [17
]. Confusion between the RGB and NDVI signals is most likely over non-vegetated non-soil surfaces (e.g., roads, buildings, water), where the distinctive spectral signatures can be mistaken for vegetation development without the presence of NIR information. Clearly, the link is not universal and will depend on time-specific and spatially varying surface characteristics. The adopted data mining approach is particularly useful at inferring these links on a repeatable scene-specific basis via the definition of multiple rule-based regressions. The rule-based models will not be applied beyond the given Planet scene, thereby avoiding traditional issues associated with the transferability of regression-based predictions [51
]. As such, changes in environmental or surface characteristics are expected to have little effect on the algorithm performance, as the Cubist-based regressions will adapt to any given condition.
The standard 16-day revisit cycle of Landsat-8 in addition to cloud cover issues reduces the chance of near-coincident Landsat and Planet acquisitions over many regions. In those situations, effective predictions of NDVI at the Planet acquisition DOY will rely on the ability to properly describe variations in NDVI at the Landsat scale over the acquisition timespan. Day-specific (linearly interpolated) MODIS NDVI images (250 m) computed from eight-day products of NIR and red reflectances were found to be useful for this purpose. The 250 m MODIS resolution may not always be sufficient for distinguishing the change in NDVI of individual land cover types in heterogeneous landscapes. However, uncertainties associated with the mixed pixel issue were reduced by searching for the “purest” (i.e., least mixed) MODIS pixel within the neighborhood of the given Landsat pixel based on pixel-wise time-series correlations of coincident MODIS and Landsat NDVI images collected within 45 days of the Planet acquisition (Section 2.3.2
). Uncertainties are naturally associated with describing the change in NDVI occurring between the DOYs of the optimal pixel observations in the eight-day periods, which may vary from 1 to 16 days. A linear rate of change in NDVI between DOYs (as adopted here) may not always be a good approximation for extended timespans. Supplementing the data mining approach with eight-day Aqua MODIS products (i.e., MYD09Q1) or daily surface reflectance products (i.e., MOD/MYD09GQ) during optimal acquisition conditions is likely to help in establishing a higher frequency account of observed NDVI dynamics at the 250 m scale. Overall, the approach relies on high quality retrievals for ensuring time and space consistent time-series records of NDVI, and reliably describing relative variations in land cover specific NDVI occurring over the acquisition timespan.
Reducing the time interval between the Planet acquisition DOY and the NDVI training image is key to achieving good NDVI predictabilities. Unfortunately, extended timespans between overpasses are likely to be an issue in regions with more frequent cloud coverage. While the framework has been demonstrated using Landsat-8, it can be easily extended to high spatial resolution multi-spectral sensors such as RapidEye (5 m) and Sentinel-2 (10 m) using their respective red and near-infrared bands for providing NDVI target inputs to the Cubist regression modeling. Synergistic use of multi-sensor NDVI data streams, atmospherically corrected and normalized to a common standard, would significantly increase the chance of usable clear-sky NDVI training images close to the Planet scene acquisition.
The developed approach uses generic techniques, adaptable scene-specific model regressions, and automated processing streams to ensure transferability to any region of interest. The approach involves the acquisition and atmospheric correction of the Landsat NDVI imagery, which is accomplished in a fully automated manner using extendable atmospheric correction techniques [37
]. Careful removal of atmospheric influences in the target NDVI images is particularly pertinent over desert agricultural systems due to high aerosol loadings, a heterogeneous surface reflectance field, and bright desert soils [37
], and represent a critical step in the construction of high quality NDVI time-series from Planet RGB imagery. Contamination from space and time varying atmospheric effects, inherent variations in the spectral and radiometric characteristics of individual Planet RGB sensors, and differences in the orbit and acquisition characteristics of individual Doves can easily confound the time-series signal relating to the actual change in surface cover conditions. A key objective of ongoing work is to evaluate the robustness of the framework over other regions with contrasting environmental and surface conditions.
The framework developed here exploits the high absolute radiometric accuracy and reflectance error specification of the Landsat-8 push-broom sensor [8
] to correct the Planet time-series imagery into estimates of NDVI, with a comparable level of accuracy and consistency over time and space domains. This is expected to significantly enhance the utility of Planet’s dense time-series of imagery for near real-time monitoring of the state and condition of vegetation canopies. Capability will be further enhanced with the addition of near-infrared sensors on-board the Dove platforms in the near future. While this will enable Planet NDVI estimation directly from the imagery, the data mining approach remains useful in producing Landsat consistent and atmospherically corrected NDVI estimates at the scale and time of the Planet acquisition. Obviously, the approach outlined here may also be applied directly to the Planet spectral band (RGB) data in order to retrieve Landsat consistent surface reflectance data in associated visible wavelength bands, which could further advance the utility of Planet’s imagery in agricultural applications.
The expanding flock of Planet’s Dove satellites surveys the globe with frequent RGB imagery capture at high spatial resolution (~3 m). A daily frequency of global coverage is foreseen in the near future with a full constellation of Dove satellites. In order to enhance the use of this unique capacity for earth observation, the Planet RGB imagery was translated into Landsat-consistent estimates of atmospherically corrected NDVI via a set of scene-specific rule-based predictive models. The adopted data mining technique was shown to be effective at reproducing observed features and magnitudes in Landsat NDVI from information contained in the red and green bands of the Planet imagery. Predictabilities were optimal when a near-coincident Landsat NDVI image was available to train the regression models: but accuracy levels remained high with larger time intervals between acquisitions and when MODIS 250 m data were used to inform on the change in NDVI occurring between the Landsat and Planet acquisitions.
The developed framework builds upon an automated processing stream and generic techniques, which makes it extendable, scalable, and transferable to other regions of interest. The approach offers significant potential for translating dense time-series of Planet RGB imagery, captured by a multitude of Dove satellites during variable acquisition conditions, into consistent and atmospherically corrected estimates of an important metric of vegetation density and health. Apart from being a useful metric of vegetation health globally, this newly developed earth observation resource serves as a high resolution tool for precision agriculture with the potential to significantly enhance the ability to identify within-field variability in crop growing conditions on a timely and repeatable basis, which is likely to help farmers optimize agricultural production and manage resources more sustainably.