Educating the Next Generation of Remote Sensing Specialists: Skills and Industry Needs in a Changing World

Figures & data

Figure 1. Distribution of (a) professional respondent’s current employment, and (b) the employment goals of students and early career professionals upon completion of their degree training.

Table 1. Examples of complimentary questions asked of professionals and student and early career professionals.

Professionals	Student and early career professionals
What is your current level of education?	What is your current level of education (completed or in progress)?
What do you think will be the biggest innovations in remote sensing in the next 5–10 years?	What do you think will be the biggest innovations in remote sensing in the next 5–10 years?
What do you consider to be the most valuable learning opportunity for trainees?	If you were to choose a networking opportunity, what would it be?
On a scale of 1 to 5, do you believe that recently graduated professionals at the undergraduate/masters/PhD levels are well-prepared for a successful career in remote sensing?	On a scale of 1 to 5, do you believe that your university career has prepared you for a successful career in remote sensing?
From your experience of recent graduates at the undergraduate/masters/PhD levels, which of the following skills need to be improved upon? (select all that apply)	At the undergraduate/masters/PhD level, in which areas do you think your university education I remote sensing could be improved? (select all that apply)

Figure 2. Perceived areas of significant development over the next 5–10 years by remote sensing professionals versus trainees and early career professionals.

Table 2. The culmination of major issues faced by remote sensing today and insightful comments by respondents.

Issue	Responder comments
Data volume	“The extreme amount of data, platforms and sensors are overloading the ability to process data”
	“the time and effort it takes to process imagery. When working over large areas, processing imagery from multiple sensors, seasons, years, etc. can take too long to warrant doing, but could improve the output product”
	“Keeping pace with petabyte stream of data. We have way more data than we can handle, hence the need for [artificial intelligence] to help
(Lack of) data sharing policies	“lots of unused data either through lock-up (close-data policy of nations…) or inability to access and process all available data fast enough, anywhere”
	“Many useful types of remote sensing are privatized and the data are prohibitively expensive for research applications. Restrictive data sharing agreements and “unshareable” data sources limit our ability to push the science forward”
Expense of remote sensing technology	“Data continuity and the risk of governments facing large debt and deficits are cutting back on EO missions”
Requirement of advanced computing/ programming skills	“Skills required by remote sensing professionals increasingly [require] programming and software development. Big data and cloud computing”
	“Complexity of algorithms and limited (open) access to experimental data/scripts hurts the basic principles of scientific method: repeatability of methodological design. There is a limited means to take published approaches and operationalize them, eight due to copyright or the [investigator] willingness to share data”
	“As the field continues to grow so rapidly…one of the biggest challenges is having students and young workers properly trained with the right skills. Where do they start? Do our universities properly equip students with the skills necessary to thrive in the field of remote sensing? And if not, how do we change our programs to meet the demands of this rapidly changing field?”
	“…we have a lot of Space-based EO data already in our possession and the future is indicating an exponential growth in different sensors and instruments. It is essential that we have the [highly qualified personnel] HQP to develop the algorithms etc. to fully exploit this. The quantity of data and ensuring the “right” distillation of this data into meaningful information for society requires more HQP and more proliferation of “remote sensing” and “earth observation” in school curriculums”
Use of remote sensing by non-experts and in non-traditional areas	“General public lack of understanding of the data”
	“Managing the heterogeneous range of remote sensing capabilities that exist and coordinating innovations/advancements across fields”
Application of remote sensing with technical understanding, but lacking fundamental understanding of the Earth system	“Think cooking: too many ingredients or too few and the result is not good, but just the right amount and all is good. Remote sensing is, to a degree, falling into the trap that adding more data will make a better “meal”. The trick is to understand what is the best data to get the right results, which relies on understanding the underlying biophysics of the application”
	“…the biggest challenge to the remote sensing community is an over-emphasis on the technical aspects of [remote sensing] applications, with insufficient attention paid to institutional issues, application issues, true utility and ethics. We can do so much now, but the core question for me is what SHOULD we do? How can [remote sensing] truly contribute to solving the knotty problems (including mitigating the effects of climate change)?”
	“I think that remote sensing technology has two big challenges: 1. Determination, integration, and analyses of socio-ecological and socio-economic parameters using remote sensing data, I mean that Remote Sensors are very good to study structures, functions, and changes of natural and artificial landscapes, but the analysis and detection of the dynamics of human factors that affect the systems need a lot of improvement. 2. Real-time detection of change. For example, we always detect deforestation after it happens. We need more preventative remote sensing”
	“A tendency to lack fundamental understanding of physical/ecological parameters underlying the attributes and phenomena we aim to map and track through remote sensing. For example, a jump to wide-area-mapping and parameterisation of forest attributes without a forest ecology understanding or local knowledge of forest conditions. We need more field training and training of topical areas in which remote sensing is applied”
	“Too many people in the field see themselves as “remote sensing specialists” but they have little or no background in either the physics or in the user disciplines such as forestry, agriculture, and the like. Such specialists tend to make mistakes and give us all a bad name”

Figure 3. Word cloud of respondent answers to the question: What are the biggest challenges facing remote sensing today?

Figure 4. Weighted importance of baseline to distinguishing competencies and skills determined from survey respondents and adapted from Markow et al. (2019).

Figure 5. Baseline competencies as required skills for remote sensing scientists and division of industry-required skills adapted from Ryerson (2014).

Table 3. Matching between respondent identification of desirable skills of remote sensing trainees and early career professionals vs. generalized business skills considered common and more desired from Bloomberg (2015).

Desirable skills of remote sensing trainees	Generalized less common, more desired skills
Writing and oral presentation skills	Communication skills
Applied activities hands-on or experiential learning	Ability to work collaboratively/industry-related work experience
Understanding of remote sensing applications	Creative problem solving/adaptability
Understanding of topical areas (e.g., forestry, agriculture)	Creative problem solving
Field validation	Adaptability/strategic thinking
Understanding of state-of-the-art methods/algorithms	Strategic thinking
Outreach activities	Communication skills

Figure 6. Magnitude and proportion of respondents’ views on the importance of remote sensing topics and skills by (a) undergraduate respondents compared with professionals; and (b) graduate respondents compared with professionals. The size of the bubbles indicates greater differences between trainees and professionals.

Figure 7. Weighting of importance of beyond university experiences from professionals versus trainees.

Markow, W., Hughes, D., and Bundy, A. 2019. “The new foundational skills of the digital economy.” Burning Glass Technologies. Accessed February 18, 2021. https://www.burning-glass.com/wp-content/uploads/New_Foundational_Skills.pdf

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Ryerson, R.A. 2014. “So, you want to be employed? Some advice for graduate students in remote sensing.” Paper presented at the IEEE International Geoscience and Remote Sensing Symposium, Quebec City, Canada, July 13–18.

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Bloomberg. 2015. “Job skills companies want but can’t get.” Bloomberg. https://www.bloomberg.com/graphics/2015-job-skills-report/

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