1. Introduction
Concerns about climate change, energy supply, growing greenhouse gas emissions, and rising fossil fuel price have driven a significant effort in searching for clean and renewable conventional fuel alternatives in recent years [
1]. Bioenergy is one of the most important resources available to help meet humanity’s energy demands and replace fossil fuels, and interest in it has grown sharply in recent years [
2]. Biomass is defined as any organic substance that has absorbed sunlight and stored it as chemical energy, such as wood, wood waste, straw, manure, sugar cane, and many other agricultural byproducts [
3]. Therefore, energy from biomass is a very flexible energy source that can be rapidly scaled to meet the demand, and as a result, it is a perfect complement to weather-dependent renewable sources such as wind and solar energy.
Considering the European Union’s most significant aim of reducing greenhouse gas emissions by a quarter and increasing energy efficiency and the use of renewable energy [
4], Romanian entrepreneurs have started to test and implement bioenergy projects, most of which are based on the provision of fuels from short-rotation crops, including willow (
Salix spp.). Willow short-rotation crops (SRCs) have been tested and used to source renewable raw materials for bioenergy production since 2008 [
5]. Currently, they are an important renewable energy source that is mostly turned into thermal energy utilizing biomass processing technology; willow SRCs have contributed to the European Union’s renewable energy objectives since they are a cost-effective choice for locations with large untapped biomass production potential [
6].
Monitoring crop condition, phenology, and changes in land cover and land use and allocating agricultural land to these crops [
7,
8] provide critical information for the development and implementation of sustainable energy management policies and the reduction in greenhouse gas emissions. While systematic and open-source remote sensing provides a wealth of data and imagery with high spatial and temporal resolutions on land-use change and bioenergy use [
9], what happens to the land once it is allocated to these activities differs by region [
10]. This has opened up the possibility of conducting complete bioenergy and biomass production studies over a longer time frame using multi-source remote sensing data. In addition, there is no worldwide product that gives information useful for the local classification of land use for bioenergy purposes.
There are several ways for estimating and evaluating the condition and the area of various land cover types. These approaches range from agricultural national census to other forms of remote sensing and GIS techniques. For area estimates, the most often-used approach is wall-to-wall or sample-based mapping [
11,
12,
13,
14]. However, landscape classification might be problematic nowadays because the average plot size has been decreasing. Also, acquiring multi-source feature sets with high quality is difficult due to diverse imaging techniques and spatiotemporal resolution [
15,
16]. In Southeast Asia, for example, the average size of agricultural fields declined from 2.5 hectares to 1 hectare between 1950 and 2000 due to farm fragmentation caused by population expansion [
17]. To overcome these challenges, a combination of medium spatial resolution satellite images, such as those provided by the European Space Agency (ESA) Sentinel-2 (with a spatial resolution of 10 m), with high spatial resolution satellite images, such as those provided by PlanetScope satellites (with a higher spatial resolution of 3 m), can be used. This combination, which uses various spectral bands, increases the capacity to discern land cover types and agricultural crops and allows the human eye to distinguish characteristics better. As a result, the object-based classification algorithms used provide a precise representation of land surface classification [
18].
The calculation and use of vegetation indices (VIs) generated from remote sensing imagery is a typical strategy in examining the condition and health of vegetation covers for both agricultural crops and forests [
19]. In particular, VIs have been developed to assess a wide range of environmental and biological events [
20,
21] and may be used to forecast plants’ biophysical characteristics [
22]. Climate change, land-use change, and natural disturbances such as wildfires and insect outbreaks can all have an impact on vegetation greening and browning patterns [
23,
24,
25], while VIs estimated from the red and near-infrared (NIR) bands are still commonly used in this context [
26]. The normalized difference vegetation index (NDVI) [
27] is commonly used to quantify vegetation changes and to study the effects of environmental events. Several studies have used the NDVI to characterize vegetation phenology [
28,
29] and to classify land cover [
30,
31], while the NDVI has been used in a wide range of applications in ecology, economics, agriculture, drought monitoring, and in characterizing climatic effects on plants [
32,
33].
As a result of the development of all of these instruments and indicators, researchers and governmental and international organizations and institutions have used these data to study adverse environmental occurrences, vegetation conditions, and even the performance of agricultural products [
34]. Several years of work in this field have demonstrated that the use of satellite data, the combination of spectral bands in the identification of land uses and land covers, and the use of vegetation indices (such as the NDVI), have great potential in determining how external and environmental factors affect vegetation [
35]. These technologies have the potential to detect various agricultural products, tree plantations and their health status, and material shortages induced by weather.
Despite the fact that the cultivation of willow (
Salix spp.) for bioenergy purposes has been performed in Romania for the last 15 years (since 2008), there are no publicly available aggregated statistics on their condition, location, and size. Partly, this comes from the fact that such crops were typically established on small-sized areas, in dispersed locations [
36], at a well-sustained pace, making it difficult to keep track of them, monitor their condition, and formulate and implement governmental bioenergy policies. The situation is similar to other European countries, for which the characterization of such crops typically relies on several data sources [
37]. As a result, the main goal of this study was to check if the combined use of medium- and high-resolution satellite images can help in detecting and classifying the agricultural plots under willow SRC production. On the other hand, field observations and time series extracted from satellite images may give evidence of variations in terrestrial vegetation activity by detecting greening and browning cycles in plants throughout time. As a second goal, this study looked at the NDVI trend changes in planted willow plots for more detailed monitoring of vegetation conditions. Consequently, the following were the objectives of this research: (1) detecting changes in the willow SRC planting areas, and (2) tracking their growth and health status.
By using this research approach in Eastern Europe and Central Romania as an example, we propose a simple, cost-effective, strategic, and transparent technique for mapping small crops of willows in areas dominated by a mix of complex peri-urban agriculture and forest environments. Understanding the distribution of planted willow plots and land covers in the study area provides a good overview of the development of willow SRC production in the entire region using the Google Earth Engine (GEE) platform and Sentinel-2 time-series images from 2017 to 2022, as cultivation patterns are generally similar. However, the approach may be used in other regions of the world where changes in landscape and land use/cover are frequent.
4. Discussion
Mapping agricultural use is important for identifying crops, analyzing crops and cropping systems’ spatial distribution, and recording land cover trends in different areas. As the world’s climate changes, this becomes increasingly important [
66]. In this context, it is critical to develop proper procedures for accelerating ssuch investigations. The simultaneous use of Google Maps and PlanetScope satellite images, as well as Sentinel-2 images and multispectral instruments with 13 bands for image classification, to prepare ground-truth samples was an appropriate strategy for identifying and detecting willow crops in the study area, with high accuracy (more than 98%) demonstrated for all six maps created between 2017 and 2022. In fact, the rate of accuracy achieved in this study exceeds the average rates obtained with hyperspace images using object-based classification algorithms [
77,
78]. Kpienbaareh et al. [
79] found that using Sentinel-2 and PlanetScope data to produce maps of agricultural regions in Sub-Saharan Africa improved the accuracy of land cover maps by more than 85%. Mercie et al. [
63] and Gašparović and Jogun [
80] reported similar findings and indicated that production maps are very accurate when Sentinel-2 is integrated with PlanetScope for vegetation mapping and monitoring. Nomura and Mitchard’s [
47] research also used Sentinel-2 data to classify a complex mosaic of different land uses in a forest ecosystem, as well as using WorldView-3 and UAV images to create ground-truth samples. The classification of 13 Sentinel-2 bands using the random forest classification approach yielded an overall accuracy of more than 95%. Due to a lack of data and information regarding the willow-farmed land in Romania, as well as the relatively small ownership of such agricultural systems, the use of such techniques in the research area may provide a good opportunity for future studies.
The NDVI was used to evaluate the health of willow crops. The application of the NDVI has aided remote sensing applications since it is connected to the state of a wide variety of plant properties. Remote sensing has transformed how humans see, use, and manage Earth’s resources [
81]. The same is true for how the NDVI is associated with vegetation characteristics (e.g., health, patterns, and status). Coops and Stone [
82] and McVeagh et al. [
83] have shown that for local-scale vegetation management, the NDVI is employed as a direct measure of vegetation health and growth. The findings of this study indicated that the value for this index in 2022 was much lower than in previous years (i.e., 2017 to 2021). Since the NDVI mostly varies due to changes in environmental conditions (precipitation and temperature) [
69], meteorological data on temperature and precipitation were used to assess the cause of this index’s decline in 2022. The plant growth season in Europe begins at the end of May and lasts until the end of October. Precipitation data analysis found that the rainfall in June had decreased substantially in 2022. The decrease in rainfall of this month appears to have resulted in a decrease in mass growth for a plant like a willow, which is a hydrophile. According to these findings, NDVI index data derived from Sentinel-2 satellite images offer a high potential for precise monitoring of agricultural stages based on the crop calendar. Liberacki et al. [
84] studied willow demands for water during their vegetation season in western Poland and found that this species needed 402 to 408 mm of water on average throughout the growth season. In this regard, June’s 0.85 cm (=8.5 mm) of precipitation was too low to support plant development.
The analysis of average temperature changes from 2017 to 2022, on the other hand, shows that the average temperature has risen in recent years. This increase in temperature across the growing seasons creates short-term droughts, which have a direct impact on crop growth [
85]. Hao et al. [
86] reported that the NDVI reacted more strongly at higher temperatures. According to global forecasts, temperatures will continue to increase and precipitation will decrease, requiring preventative actions in Romania to deal with the negative implications of water shortages during the growing season [
87]. Willows do not require strict cultivation conditions, according to researchers [
88]. Mirck and Volk [
89] report that willows can resist irrigation with water containing 1625 mg of chlorine. In addition, they reported that willows are only modestly salt tolerant. Based on these findings, it is possible to conclude that the willow can adapt to some extent. It should be noted that proper watering of willows is required for their best growth. According to Gage and Cooper [
90], one of the decisive factors for willow development in mountain and coastal communities in the United States of America is the availability of water in the soil.
Decisions on what crops can be used in place of fossil fuels should be made in such a way that they do not considerably increase water demand [
91]. Research is increasingly being conducted to simulate agricultural water demands and the amount of irrigation water required under various climate change scenarios [
92,
93]. According to previous estimations, climate change has an impact on the amount of water required for irrigation. Climate change will also increase the need for irrigation for several crops [
94].