*2.1. Satellite-Based Hyperspectral Imaging*

Compared with a large number of satellite-based multispectral sensors (e.g., Landsat, SPOT, WorldView, QuickBird, Sentinel-2), there are significantly fewer hyperspectral sensors. EO-1 Hyperion, PROBA-CHRIS, and TianGong-1 [40] are a few examples of the available satellite hyperspectral sensors [20]. EO-1 Hyperion is the most widely used satellite-based hyperspectral sensor for agriculture (e.g., more than 40 publications). It collects data in the visible, near-infrared, and shortwave infrared ranges with a spectral resolution of 10 nm and a spatial resolution of 30 m. More sensor specifications of EO-1 Hyperion are given in Table 2. The sensor was in operation from 2000 to 2017, which corresponds to the period having more publications using satellite-based hyperspectral imaging (e.g., 2006 to 2020 in Figure 2). The use of Hyperion data has been reported in a

variety of agricultural studies for monitoring different crop and soil properties, including detecting crop disease [41,42], estimating crop properties (e.g., chlorophyll, LAI, biomass) [43–45], assessing crop residues [46,47], classifying crop types [48], and investigating soil features [49,50]. A few featured ones include Wu et al. [45], who estimated vegetation chlorophyll content and LAI in a mixed agricultural field using Hyperion data and evaluated spectral bands that are sensitive to these vegetation properties. Camacho Velasco et al. [48] used Hyperion hyperspectral imagery and different classification algorithms (e.g., spectral angle mapper and adaptive coherence estimator) for identifying five types of crops (e.g., oil palm, rubber, grass for grazing, citrus, and sugar cane) in Colombia. Gomez et al. [49] predicted soil organic carbon (SOC) using both spectroradiometer data and a Hyperion hyperspectral image, and they found that using Hyperion data resulted in a lower accuracy compared with results derived from spectroradiometer data.

**Figure 2.** Number of publications that used different hyperspectral imaging platforms over time.

Studies have also been conducted to compare the performances of Hyperion hyperspectral imagery with multispectral imagery for estimating crop properties or classifying crop types. For instance, Mariotto et al. [15] compared Hyperion hyperspectral imagery with Landsat multispectral imagery for the estimation of crop productivity and the classification of crop types. The authors reported better performances of using hyperspectral imagery than using Landsat imagery for both research purposes. Similarly, Bostan et al. [51] compared Hyperion hyperspectral imagery with Landsat multispectral imagery for crop classification and also found that higher classification accuracy can be achieved by using hyperspectral imagery.


**2.**Specifications of commonly used hyperspectral sensors [11,20,52–56].

**Table** 

175

#### *Remote Sens.* **2020** , *12*, 2659

PROBA-CHRIS is another commonly used satellite-based hyperspectral sensor that was launched in 2001. Specific studies, such as Verger et al. [57], utilized PROBA-CHRIS data for retrieving LAI, the fraction of vegetation cover (fCover), and the fraction of absorbed photosynthetically active radiation (FAPAR) in an agricultural field. Antony et al. [58] identified three growth stages of wheat using multi-angle PROBA-CHRIS images and found the optimal view angles for the identification. Casa et al. [59] evaluated the performance of airborne Multispectral Infrared Visible Imaging Spectrometer (MIVIS) data and spaceborne PROBA-CHRIS data for investigating soil texture, and they found that these two data have similar performances, although the PROBA-CHRIS data have a lower spatial resolution.

There are a few other satellite-based hyperspectral sensors that have not been commonly used in an agricultural environment. For instance, Hyperspectral Imager (HySI) is a hyperspectral sensor equipped on the Indian Microsatellite-1 (IMS-1) launched in 2008 [60]. It collects spectral signals in the range of 400–950 nm with a spatial resolution of 550 m at nadir [61]. HySI imagery has been used to map different agricultural features, such as soil moisture and soil salinity [62]. It has also been used for crop classification [63]. However, this data has not been widely used in precision farming, which is probably due to the low spatial resolution and limited data availability. The Hyperspectral Imager for the Coastal Ocean (HICO) is another spaceborne hyperspectral sensor that takes images with a spectral range from 380 to 960 nm at a spatial resolution of 90 m [64]. This sensor was mainly designed to sample the coastal ocean and operated from 2009 to 2015.

In recent years, several spaceborne hyperspectral sensors have been launched or scheduled for launching in the next few years. For instance, the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS), a hyperspectral sensor mounted on the International Space Station, was launched in 2018 [65]. This sensor acquires images in the range from 400 to 1000 nm with a spectral resolution of 2.5 nm and a spatial resolution of 30 m. The Hyperspectral Imager Suite (HISUI) is a Japanese hyperspectral sensor that is also onboard the International Space Station [66]. It was launched in 2019 and collects data in the range from 400 to 2500 nm with a spatial resolution of 20 m and a temporal resolution of 2 to 60 days [20]. Hyperspectral Precursor and Application Mission (PRISMA) is an Italian hyperspectral mission with the sensor launched in March 2019. Its spectral resolution is 12 nm in the range of 400-2500 nm (~250 bands in visible to shortwave infrared). Its hyperspectral imagery has a spatial resolution of 30 and 5 m for the panchromatic band [67]. The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that is still in the development and production phase [68]. The EnMAP sensor will collect data from the visible to the shortwave infrared range with a spatial resolution of 30 m. It is planned to be launched in 2020. The Spaceborne Hyperspectral Applicative Land and Ocean Mission (SHALOM) is a joint mission by Israeli and Italian space agencies, and the satellite is scheduled to be launched in 2022 [69]. This sensor will collect hyperspectral images with a spatial resolution of 10 m in the spectral range of 400–2500 nm and panchromatic images with a spatial resolution of 2.5 m [70]. HyspIRI is another hyperspectral mission that is also at the study stage [71]. This sensor will collect data in the 380 to 2500 nm range with an interval of 10 nm and a spatial resolution of 60 m.

Although the actual PRISMA, EnMAP, and HyspIRI data are not yet available, researchers have simulated the images using other data and tested the performance of the simulated images for investigating different vegetation and soil features. For instance, Malec et al. [72], Siegmann et al. [73], and Locherer et al. [74] simulated EnMAP imagery using different airborne or spaceborne images and applied the simulated images for investigating different crop and soil properties. Bachmann et al. [75] produced an image using the EnMAP's end-to-end simulation tool and examined the uncertainties associated with spectral and radiometric calibration. Castaldi et al. [76] simulated data of four current (EO-1 ALI and Hyperion, Landsat 8 Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI)) and three forthcoming (EnMAP, PRISMA, and HyspIRI) sensors using a soil spectral library and compared their performance for estimating soil properties. Castaldi et al. [77] used PRISMA data that were simulated with lab-measured spectral data for estimating clay content and attempted to reduce the influence of soil moisture on the estimation of clay.

Previous studies have confirmed the good performance of satellite-based hyperspectral sensors for studying agricultural features; however, several factors could potentially affect the broad applications of these data in precision farming, including the spatial resolution, temporal resolution, and data quality. The detection and monitoring of many agricultural features, such as crop disease, pest infestation, and nutrient status, require high spatial and temporal resolution. Most of the satellite-based hyperspectral sensors have medium spatial resolutions, such as 17 or 36 m for PROBA-CHRIS; 30 m for Hyperion, PRISMA, and EnMAP, DESIS; and 60 m for HyspIRI. Previous studies have indicated that such spatial resolutions are not sufficient for precision farming applications [20,49]. To overcome such limitations, researchers have attempted to pansharpen hyperspectral images, aiming to improve spatial resolution [73,78–80]. Loncan et al. [81] also reviewed different pansharpening methods for generating high-spatial resolution hyperspectral images.

Temporal resolution is another factor that could potentially limit the applications of satellite-based hyperspectral images to precision agriculture. Most of the satellite-based sensors have a long revisit cycle (e.g., typically around two weeks), and thus early signals of crop stress (e.g., disease and pest) may be missed. This limitation can be further aggravated by unfavorable weather conditions (e.g., cloud contamination). Lastly, low data quality is also an issue that can affect the performance of satellite-based hyperspectral imaging for investigating agricultural features. A low signal-to-noise ratio is a well-known issue of Hyperion data (e.g., in the shortwave infrared (SWIR) range), which has affected the accuracy of retrieving different agricultural features [20]. For instance, Asner and Heidebrecht [82], Gomez et al. [49], and Weng et al. [83] found that the low signal-to-noise ratio influenced the accuracies of estimating non-photosynthetic vegetation and soil cover, soil organic matter, and soil salinity, respectively. Future satellite-based hyperspectral missions are expected to solve the data quality issue.
