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Article

At Which Overpass Time Do ECOSTRESS Observations Best Align with Crop Health and Water Rights?

by
Benjamin D. Goffin
1,*,
Carlos Calvo Cortés-Monroy
2,
Fernando Neira-Román
2,
Diya D. Gupta
3 and
Venkataraman Lakshmi
1
1
Department of Civil and Environmental Engineering, University of Virginia, Charlottesville, VA 22904, USA
2
Centro de Información de Recursos Naturales, Providencia, Santiago 7501556, Chile
3
College of Arts and Sciences, University of Virginia, Charlottesville, VA 22904, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(17), 3174; https://doi.org/10.3390/rs16173174
Submission received: 31 May 2024 / Revised: 5 August 2024 / Accepted: 17 August 2024 / Published: 28 August 2024
(This article belongs to the Special Issue Advanced in Remote Sensing Approaches for Agricultural Monitoring at Field and Regional Scale)
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Abstract

:
Agroecosystems are facing the adverse effects of climate change. This study explored how the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can give new insight into irrigation allocation and plant health. Leveraging the global coverage and 70-m spatial resolution of the Evaporative Stress Index (ESI) from ECOSTRESS, we processed over 200 overpasses and examined patterns over 3 growing seasons across the Maipo River Basin of Central Chile, which faces exacerbated water stress. We found that ECOSTRESS ESI varies substantially based on the overpass time, with ESI values being systematically higher in the morning and lower in the afternoon. We also compared variations in ESI against spatial patterns in the environment. To that end, we analyzed the vegetation greenness sensed from Landsat 8 and compiled the referential irrigation allocation from Chilean water regulators. Consistently, we found stronger correlations between these variables and ESI in the morning time (than in the afternoon). Based on our findings, we discussed new insights and potential applications of ECOSTRESS ESI in support of improved agricultural monitoring and sustainable water management.

1. Introduction

Agriculture is the primary consumer of water withdrawals globally [1]. Nevertheless, the agricultural sector remains highly sensitive to climate variations [2]. If water is available, crops can use water (i.e., actual evapotranspiration, AET) at the maximum rate of atmospheric demand (i.e., potential evapotranspiration, PET) [3,4]. Once the demand for water from the leaves exceeds the supply from the roots, plants experience water stress and close their stomates to conserve water [5,6]. Because ET fluxes are restricted, the temperature of the canopy increases, causing potential heat-related impacts [7]. When this water deficit extends over time, the crop’s growth and yield are adversely affected [8,9]. In the face of climate change, crops are at a greater risk of facing droughts and limited water availability [10,11].
Therefore, it is becoming increasingly important for food and water security to monitor crop stress from local to global scales with high accuracy [12]. Reduction in AET relative to PET can indicate plant-water stress [13]. The ratio of AET to PET is referred to as the Evaporative Stress Index (ESI) and serves as a key indicator of rapidly evolving drought conditions that pose a threat to agricultural areas [14,15,16,17,18,19,20]. Unlike other drought indices relying on ground observations, ESI can incorporate remotely sensed data from satellites and monitor drought conditions at the global scale. Previously, ESI was limited by the spatial resolution of available geostationary satellites, on the order of 5–10 km [21].
Since its launch in 2018, the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) models plant-water dynamics at a 70-m spatial resolution [22]. Aboard the International Space Station (ISS), ECOSTRESS follows an irregular orbit (i.e., non-sun-synchronous, non-geostationary), overpassing a site every 1–5 days at different revisiting times. This unique vantage point allows ECOSTRESS to sample the diurnal cycle in critical regions [23]. Specifically, ECOSTRESS measures the Thermal Infrared (TIR) radiation emitted from Earth’s surface to calculate the brightness temperature of plants and derive ET. The algorithm for global ET retrieval is based on the Priestley-Taylor Jet Propulsion Lab (PT-JPL) method [24,25,26]. Past studies confirmed the ability of the PT-JPL model to characterize variation in ET across regions and seasonal scales [27,28,29,30,31]. On ECOSTRESS, ET estimates based on PT-JPL also showed high agreement with observations from 82 flux towers around the world [22].
More recent assessments highlighted potential inaccuracies and biases in the PT-JPL ET product, particularly during the summer season or morning time [31,32,33,34]. But to this day, there has been very limited research on the PT-JPL ESI product (one of the core products of the ECOSTRESS mission). A prior study by Pascolini-Campbell et al. (2022) applied ESI from ECOSTRESS (among other variables) to predict fire burn severity in California [35]. ESI was found to be among the leading predictors (in certain vegetation types), but those results were sensitive to the time of day of the ECOSTRESS acquisition. No paper has reviewed how ECOSTRESS overpasses can affect ESI measurements and to what extent they can capture patterns in the environment. To the best of our knowledge, only one preliminary study has indicated that ESI from ECOSTRESS could exhibit significant variability or “noise” between overpasses [36]. It is critical for agricultural monitoring to better understand where these remotely sensed data have discrepancies and why [37]. Therefore, our work aims to:
  • Examine how ESI observations vary based on their acquisition time;
  • Evaluate how closely ESI aligns with crop health and water rights;
  • Assess the potential for ESI to capture sub-optimal crop conditions.

2. Data and Methods

2.1. Study Area

Our study focused on the Maipo River Basin located in Central Chile (Figure 1a). Per its regulatory delineation, the basin collects water from over 15,200 sq km, from the Andes Mountains to the Pacific Ocean [38]. It encompasses the Región Metropolitana de Santiago, Chile’s most densely populated region. It also extends over parts of the Región de Valparaíso (to the west) and Región del Libertador General Bernardo O’Higgins (to the south). We combined the most recent land registry information available from the three administrative regions studied to build a comprehensive inventory of all the 180,000 hectares of agricultural fields in the basin [39,40,41].
The basin is characterized by a Mediterranean climate, with most of the annual precipitation concentrated during the winter months [42]. Note that this precipitation regime results in low cloud cover for the rest of the year and optimal conditions for remote sensing monitoring. During the growing season, the basin supplies water to around 135,000 ha of irrigated agricultural land [43]. Irrigation is achieved by means of consumptive water-use rights, allowing some farmers to meet the water demand of both annual and perennial crops [44].
However, the basin has experienced long downward trends in precipitation and rising temperatures [11,45]. In addition, a multi-year dry spell (referred to as megadrought) began in 2010 and further reduced runoff and the quantity (and quality) of surface water and groundwater levels across the region [42,46,47]. The decade-long megadrought and changing climate have already impacted socio-economic systems and exacerbated conflicts across water users [48,49,50,51,52,53,54].

2.2. Earth Observations

In this context, we leveraged different Earth observations from satellites to capture crop conditions on the ground. Using NASA’s open-access Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) portal, we downloaded all ECOSTRESS PT-JPL ESI Level 4 data available over our study area [55]. ESI is one of the core products of ECOSTRESS, which evaluates the evaporative stress based on the ratio in Equation (1) below [13]:
ESI = AET PET
We aligned each ECOSTRESS snapshot on a 0.0006° grid (i.e., ~70 m spacing), encompassing approximately 4.1 M pixels over the study area (EPSG 32719). We compiled a total of 316 observations taken at various acquisition time over 242 days, spanning from 21 July 2018 to 19 January 2023 (Figure S1). Due to its irregular orbit aboard the ISS, ECOSTRESS returns to a given location at various times of day with inconsistent intervals. Each ESI value represents an instantaneous measure at the time of ECOSTRESS overpass [56]. We converted the Coordinated Universal Time (UTC) reported for each observation to the local solar time. To account for the potential variations between the different revisit times, we differentiated ECOSTRESS overpasses based on the following time periods:
  • the morning hours from 5:00 a.m. to 9:59 a.m.; and
  • the afternoon hours from 12:00 p.m. to 4:59 p.m.
We also utilized surface reflectance data (Level 2, Collection 2, Tier 1) from the ongoing Landsat 8 mission [57,58]. Image processing and acquisition was completed through Google Earth Engine using the JavaScript Application Programming Interface (API). Pixels affected by cloud cover or cloud shadow were masked to retain only the highest quality information (Figure S2). We used the Red, Green, and Blue (RGB) bands of the Operational Land Imager (OLI) with the prescribed scaling factors to create natural color composites of the study area. We also computed the Normalized Difference Vegetation Index (NDVI), a measure of vegetation greenness already in use by the Chilean government for drought monitoring [59]. NDVI is calculated based on the Red and Near-Infrared (NIR) bands using Equation (2) [60]:
NDVI = NIR   −   Red NIR + Red
We primarily examined ESI (and NDVI) during the growing season, which is defined as the five months from November to March, when crops in the basin are in their most active growing stages and water demand is the highest [43]. To get representative measure of ESI (and NDVI) for a growing season, we took the median value of the ESI (and NDVI) observed during those hours over the 5 months studied. Given the launch of ECOSTRESS in 2018, we considered a total of three growing seasons for which ECOSTRESS data had been released over:
  • 2019 to 2020;
  • 2020 to 2021;
  • 2021 to 2022.

2.3. Selection of Maize Site at the Parcel Level

First, we examined these remotely sensed data at discrete locations with consistent crop cover. Maize (Zea mays) is a particularly relevant crop to study since it is the most produced cereal grain in the basin (with an estimated 14,100 ha in 2020) and is expected to require more irrigation due to climate change [61]. Maize is also well suited for remote sensing monitoring since it develops over the course of approximately 170 days, allowing for many satellite overpasses [62]. Based on that, we selected twelve maize fields across the Maipo River Valley (Table 1). Six of the parcels (labeled LO-101 through LO-106) were located near the cities of Melipilla and María Pinto, while the other six (labeled HI-101 through HI-106) were selected at a relatively higher elevation in the vicinity of Talagante and Paine, thus encompassing a range of topographic and climatic conditions (Figure 1b). The elevation at each location was obtained from NASA’s Shuttle Radar Topography Mission [63]. To minimize the effects of outside-pixel contamination, each parcel under study was about 10 hectares or more [39,40,41]. The planting dates of maize at each field were determined based on the least-squares fit of NDVI against FAO crop development curves [62]. We calculated the total rainfall and average daytime land surface temperature for each site using the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS), respectively [64,65]. To get a representative value of ESI at one location, we computed the median of a 3 × 3 grid around the middle of the field. With that, we examined ESI as a function of the overpass time of ECOSTRESS.

2.4. Compilation of Water Allocations at the Regional Scale

Beyond a dozen maize fields, we also examined variations in ECOSTRESS observations over the irrigated land of the Maipo River Basin (Figure 1c). We analyzed how ESI varies across areas that are allocated various amounts of irrigation water. Previous studies compared remotely sensed data with information on water use available in various parts of Chile [66,67,68]. We conducted an extensive compilation of selected surface- and ground-water allocations for 41 specific agricultural areas in the Maipo River Basin (Table 2). In total, these irrigated areas constituted around 126,000 hectares (i.e., over 70% of the 180,000 hectares of all agricultural land in the Maipo River Basin). The selected areas also made up for a combined 190 m3/s of surface- and ground-water withdrawal allocations of the roughly 237 m3/s used for agriculture in the whole basin [69]. Additional irrigated land exists in the Maipo River Basin, among which several areas hold “eventual” (i.e., intermittent) water-use rights. Due to the extended drought conditions, these surface water allocations were negligible during the period studied (before 2023), and therefore not included in this study.
Agricultural systems are known to experience various degrees of temperature-related damage based on the irrigation and transpiration allowed [7]. Given that each of the 41 entities has different stakeholders and water practices, it made sense to investigate how ESI varied across these geographic entities as a function of agricultural areas, total allocated waters, and crop greenness. To get a representative measure of ESI (and NDVI) in every spatial entity, we took the median value of the ESI (and NDVI) pixels contained within each of these irrigated areas. We also considered the spatial variability in ESI within each area by calculating the Relative Standard Deviation (RSD) across ESI pixels.
We evaluated the level of correlation between ESI (at various times of day) and crop greenness, total allocated waters, and agricultural areas. To characterize the monotonic association between two variables, we calculated Kendall’s τ and Spearman’s ρ coefficients [88,89,90]. Unlike Pearson’s correlation, these two tests are non-parametric and robust to potential outliers, non-normally distributed data, and non-linear relationships [91]. A coefficient of 1 indicates a perfect association of ranks. Lastly, we explored variations in the distribution of ESI between crop productions. Specifically, we considered the 4 major crop types in the basin (by irrigated surface area), namely grapevine (~16,200 ha), walnut (~16,100 ha), maize (~12,200 ha), and avocado (~5700 ha).

3. Results

3.1. Temporal Patterns in ESI at Selected Maize Fields

For different sites across the Maipo River Basin, we found that valid observations of ECOSTRESS were distributed across local times between 5:00 a.m. and 7:00 p.m. (Figure 2). The number of observations taken each hour fluctuated throughout the day and from year to year. We found that ESI at these selected fields ranged generally from around 0.1 to 1.0 based on the time of day and growing season. The higher ESI values (i.e., less stress) were produced around 5:00 a.m. and ESI reached a consistent low (i.e., high stress) around 2:00 p.m., regardless of the site or season. Transient conditions can be observed in the later parts of the morning and the afternoon. Fluctuations in ESI were also noted between seasons with some higher ESI values during the 2020–2021 growing cycle, particularly in the afternoon. The curve of ESI as a function of acquisition time is noticeably flatter at higher elevations (e.g., HI-104 through 106) in part due to relatively higher ESI in the afternoon hours.

3.2. Spatial Patterns in ESI throughout the Agricultural Land

ESI varied substantially across the irrigated land of the Maipo River Basin, regardless of the time of day or growing season (Figure 3). However, a stark contrast in ESI can be seen across the acquisition times, with ESI up to four times higher in the morning hours than in the afternoon. The all-hours ESI showed attenuated details compared to the specific hourly groupings. In addition, we found contrasting spatial patterns in ESI across the different acquisition times. For instance, morning ESI appeared to increase in a westward direction (i.e., light blue to purple) while ESI in the later hours of the day increased in the eastward direction (i.e., red to green). Comparing the growing seasons, we observed important shifts from year to year with afternoon ESI increasing on average by 21% from the 2019–2020 cycle to the next one (Figure 3d,e). However, the inter-seasonal shifts in morning ESI were not as pronounced, varying on average by only 1% over the same timeframe (Figure 3a,b).

3.3. Spatial Correlation of ESI with Crop Health

When comparing different agricultural areas across the Maipo River Basin, we found some positive correlations between their ECOSTRESS ESI and Landsat 8 OLI NDVI (Figure 4). This means that some agricultural areas with higher ESI exhibited higher NDVI, and vice-versa. In addition, ESI appeared generally lower (i.e., higher stress) during the 2019–2020 growing cycle than in the following years, and this shift was reflected in lower NDVI over the same time period. The correlation between plant-water stress and crop health was consistently significant (p < 0.001) for the morning overpasses with Spearman’s correlation coefficients ranging from 0.544 to 0.829 (Figure 4a–c). The relationship between the two variables also followed a weaker monotonic trend during some other times of day (Figure 4f,g,i).

3.4. Correlation of ESI with Water Rights

In exploring the link between crop-water stress and water rights, we found some positive correlations between ESI and the amounts of allocated water (Figure 5). Ranks in morning ESI were best aligned with those of documented surface- and ground-water allocations (p < 0.001 and ρ between 0.436 and 0.556). Based on this relationship, some areas such as Toma del Toro (ID#5) and Colina (ID#9), with relatively low allocated water, showed lower ESI. It should also be noted that the spatial variability in morning ESI was a fraction of the coefficient of variation obtained at other times of day (Figure S3). In addition, the spatial variability in the later hours of the day exhibited a significant correlation (p < 0.05) with the size of the agricultural areas, suggesting that dispersion around the mean might be further amplified across larger sample areas during the afternoon.

3.5. Variations in Crop Productions

When further examining the distribution of ESI, we found that it also varied across irrigated parcels based on crop types (Figure 6). The dispersion of ESI for each crop type is substantially larger for the afternoon and all hours combined, than it is in the morning time. Focusing on morning ESI only, we also observed that the stress imposed on avocado parcels is generally lower (i.e., higher ESI) than it is for grapevines (Figure 6a). Going further, morning ESI also appeared to shift down (i.e., further exacerbated) in the growing cycle 2021–2022 in comparison to the prior year (i.e., on average −5.0% for grapevine, −5.1% for walnut, −3.8% for maize, −3.9% for avocado).

4. Discussion

4.1. Need for ECOSTRESS ESI

A rapid scale-up in the application of satellite data is needed to give new insight into drought conditions and limited water resources, specifically in the Andes region [92]. Previously, fluctuations in NDVI served as an indicator of crop coefficients and productivity [59,62]. However, there can be a lag in the greenness response of crops to rapid-onset drought events. NDVI can also be affected by other factors such as late frost, pests, or flooding. It is critical for drought indices to capture land-atmosphere interactions. The Palmer Drought Severity Index (PDSI) includes temperature (one of the many drivers of ET) but does not account for humidity or incoming radiation [4,93]. Other common indices of droughts attempt to model water availability based on precipitation, soil moisture, and/or ET [8,94,95,96,97,98]. Generally, drought indices fail to capture fine-scale heterogeneity [3,6]. Common indicators like the U.S. Drought Monitor are coarse in size (with a resolution of 0.025 degree) and lack the granularity necessary to support decision-making [99]. In contrast, ECOSTRESS measures TIR radiation at a 70-m spatial resolution to model evaporative stress relying on the principle that well-irrigated fields are cooler than water-stressed crops.

4.2. Limited Understanding of ECOSTRESS ESI

In general, our understanding of atmospheric drying (over the course of a day) and soil drying (over the course of a season) and their combined effects on ET remains limited, especially at the field scale [9]. Current ET retrievals based on PT-JPL showed considerable overestimation across heterogeneous landscapes and complex terrain [32,100]. As for the overpass time, some studies reported that the PT-JPL model performed particularly well in the early morning [31], produced lower biases later in the day [34], or yielded no significant differences at different revisit times [101]. Overall, there have been very few prior applications of any ECOSTRESS PT-JPL data to Mediterranean climates [22,34,35] or South America [22]. In fact, only one paper previously used the ESI variable of ECOSTRESS and differentiated observations before and after 12:00 p.m. [35].

4.3. New Insights into ECOSTRESS ESI

To the best of our knowledge, our study is the first to examine how instantaneous values of ESI varies as a function of the ECOSTRESS acquisition time. We found substantial variations in crop-water stress based on the revisit time over multiple fields and throughout several growing seasons. We also used a more conservative grouping of ECOSTRESS overpasses by differentiating ESI observations between early morning (i.e., 5:00 a.m. to 9:59 a.m.) and early afternoon (i.e., 12:00 p.m. to 16:59 p.m.). Despite a consistent megadrought, we also observed some shifts in ECOSTRESS ESI between the seasons studied. It is worth noting that these inter-seasonal changes looked substantially different across various times of day. A decrease in afternoon ESI (e.g., lower stress) between the 2019–2020 and 2020–2021 cycles could potentially be explained by lower seasonal temperatures (Table 1). To gain confidence in the ESI snapshots from different times of day, we examined how well ECOSTRESS observations agreed with patterns across irrigated land. We found that morning ESI systematically aligned best with crop health and water rights and exhibited lower spatial variability (even across large agricultural areas). Our results held consistent, whether we used Kendall’s τ or Spearman’s ρ coefficients (Tables S1 and S2). These findings contradict previous studies that found greater discrepancies in ECOSTRESS PT-JPL products during the morning hours [32,33,34]. However, we should note that our study focuses on relatively larger study areas (i.e., >900 ha.) and longer timescales (i.e., median over a season), which might not be as sensitive to shifts in micro-meteorological conditions.

4.4. Societal Implications

Our research showed how agricultural parcels with the same crop faced more or less stress over the course of a season, and how various crops also exhibited different stress responses from year to year. However, one of our most actionable findings identified irrigated areas, which appeared to be disproportionately affected by lower water allocations. As such, ECOSTRESS ESI proved helpful in highlighting areas with sub-optimal crop conditions. This kind of knowledge could help guide much-needed adaptation actions as irrigated crops are faced with reduced water availability in the future. Some of these measures might include early sowing or double cropping, the production of drought-resistant crops, the adoption of irrigation technologies, near-real-time monitoring tools, and the redesign of subsidy programs [44,102,103,104,105,106]. This type of information at the community level is even more relevant in the context of smallholder and subsistence agriculture [107]. Therefore, the unprecedented spatial resolution and diurnal sampling provided by ECOSTRESS ESI could offer additional insights to support sustainable water management (SDG #6) and reduce irrigated agriculture’s vulnerability (SDG #2) and inequalities (SDG #10) in the midst of climate extremes (#SDG #13).

4.5. Future Work

Accessing information on referential water-use rights was critical to our analysis but proved difficult. In line with the recommendations of others, we suggest that these data be compiled by authorities at the national level and be made publicly available [92]. While documented values of water allocation were used in this analysis, it should be noted that referential water-use amounts were not necessarily utilized at their full volumetric rate during the timeframe studied. This is because water availability varies from year to year. Therefore, the installation of flow meters and the reporting of water withdrawals across the basin would provide a better measure of consumptive water use [52].
In addition, our research was possible because the study area was not severely impacted by cloud cover and had frequent ECOSTRESS observations with nominal quality or higher (Figures S2 and S3). Moving forward, ECOSTRESS will require additional testing, especially in complex terrain and humid regions with missing data [31,33]. It is also important to continue exploring the relationship between TIR and optical signal, and to what extent the former goes beyond the latter. ECOSTRESS serves as a precursor for upcoming TIR missions [108]. Gaining insights into where these remotely sensed data have discrepancies and why is critical to improving agricultural monitoring [37]. In the future, one could hope to monitor agricultural outputs and crop yield using this information from space [18,19]. But for data from ECOSTRESS or other missions to be used operationally, it should also be made available online on a near-real-time basis.

5. Conclusions

ECOSTRESS measures TIR radiation at a 70-m spatial resolution (about 50 times finer than MODIS). Its unique vantage point onboard the ISS provides observations of agricultural productions at different revisit times for critical regions, such as the Maipo River Basin. Current understanding of the effects of overpass time on PT-JPL retrievals is limited, especially for the ESI product. To the best of our knowledge, this study is the first to examine how ESI varies as a function of the ECOSTRESS overpass time. Throughout our analyses, we found substantial variations in ECOSTRESS ESI at different times of the day. Based on that, we recommend differentiating ECOSTRESS observations into two groups for early morning (i.e., 5:00 a.m. to 9:59 a.m.) and early afternoon (i.e., 12:00 p.m. to 16:59 p.m.). In parts of our study area, morning ESI values were up to four times higher (i.e., lower stress) than in the afternoon. In addition, we found contrasting spatial patterns in ESI based on the overpass time. Therefore, we analyzed how well the different ECOSTRESS snapshots agreed with patterns in the environment. To that end, we created an extensive compilation of surface- and ground-water allocations across a combined 126,000 hectares of irrigated land in the Maipo River Basin. We identified a positive correlation (p < 0.001) between the ranks in morning ESI and those in allocated water, meaning that areas with higher water allocations tended to exhibit higher morning ESI (i.e., lower stress). We also found that year after year, morning ESI was best aligned with Landsat 8 OLI NDVI (a proxy for crop health), yielding correlation coefficients between 0.544 and 0.829. Beyond these monotonic associations, ECOSTRESS ESI proved helpful in detecting shifts in evaporative stress and areas with sub-optimal crop conditions. This type of information could inform future decisions for sustainable water withdrawals and crop selection in different parts of the basin and beyond. More broadly, our work gave new insights into the diurnal sampling provided by ECOSTRESS and the effects of overpass time on TIR measurements, in support of future agricultural monitoring.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs16173174/s1, Figure S1: Number of ESI observations from ECOSTRESS; Figure S2: Number of NDVI observations from Landsat 8; Figure S3: Trends in the relative standard deviation of ESI against the size of agricultural areas; Table S1: Correlation coefficients between ESI and NDVI; Table S2: Correlation coefficients between ESI and Water-Use Rights.

Author Contributions

Conceptualization, B.D.G., C.C.C.-M. and F.N.-R.; methodology, B.D.G. and C.C.C.-M.; software, B.D.G. and C.C.C.-M.; validation, B.D.G.; formal analysis, B.D.G.; data curation, B.D.G. and C.C.C.-M.; writing—original draft preparation, B.D.G.; writing—review and editing, B.D.G., C.C.C.-M., F.N.-R., D.D.G. and V.L.; visualization, B.D.G.; supervision, V.L.; project administration, B.D.G.; funding acquisition, B.D.G., D.D.G. and V.L. All authors have read and agreed to the published version of the manuscript.

Funding

This project was made possible by an award from the Center for Global Health Equity at the University of Virginia. This work was also supported by funding from the University of Virginia School of Engineering and Applied Science and the Jefferson Scholars Foundation.

Data Availability Statement

The ECOSTRESS products discussed in this study are publicly available via the NASA Earthdata Search and the AppEEARS platforms at https://lpdaac.usgs.gov/products/eco4esiptjplv001/ (accessed on 11 June 2024). Surface reflectance data from Landsat 8 OLI (Level 2, Collection 2, Tier 1) can be acquired using the Google Earth Engine Data Catalog at https://earthengine.google.com/ (accessed on 23 July 2024). Our compilation of water-use rights in the Maipo River Basin is also made accessible online at https://github.com/ccalvocm/research/blob/main/data/arDAA.gpkg (accessed on 26 August 2024).

Acknowledgments

The authors would like to recognize the staff members of Chile’s Centro de Información de Recursos Naturales (CIREN) for their guidance and feedback.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Our study focuses on the Maipo River Basin located in (a) Central Chile and characterized by (b) a complex terrain and river network, and (c) extensive areas with surface- and ground-water allocations.
Figure 1. Our study focuses on the Maipo River Basin located in (a) Central Chile and characterized by (b) a complex terrain and river network, and (c) extensive areas with surface- and ground-water allocations.
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Figure 2. Variations in Evaporative Stress Index (ESI) from ECOSTRESS over the hours of the day for a dozen maize fields across the Maipo River Basin. Values of ESI approaching 1 (e.g., early morning hours) indicate a close match between available water and atmospheric demand, resulting in minimal plant stress. Note that no maize was grown at LO-103 in 2021–2022.
Figure 2. Variations in Evaporative Stress Index (ESI) from ECOSTRESS over the hours of the day for a dozen maize fields across the Maipo River Basin. Values of ESI approaching 1 (e.g., early morning hours) indicate a close match between available water and atmospheric demand, resulting in minimal plant stress. Note that no maize was grown at LO-103 in 2021–2022.
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Figure 3. Variations in Evaporative Stress Index (ESI) from ECOSTRESS throughout the 126,000 ha of irrigated agricultural land of the Maipo River Basin across various times of day (vertically) and growing seasons (horizontally). Values of ESI approaching 1 (blue) indicate a close match between available water and atmospheric demand, whereas an ESI of 0 (red) represents maximum plant stress.
Figure 3. Variations in Evaporative Stress Index (ESI) from ECOSTRESS throughout the 126,000 ha of irrigated agricultural land of the Maipo River Basin across various times of day (vertically) and growing seasons (horizontally). Values of ESI approaching 1 (blue) indicate a close match between available water and atmospheric demand, whereas an ESI of 0 (red) represents maximum plant stress.
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Figure 4. Trends in Evaporative Stress Index (ESI) from ECOSTRESS (y-axis) against Normalized Difference Vegetation Index (NDVI) from Landsat 8 (x-axis) of agricultural areas with different water allocations in the Maipo River Basin across various times of day (vertically) and growing seasons (horizontally). The positive correlation between the two variables is illustrated by a regression line (dashed) and its 95% confidence interval (shaded). Spearman’s ρ coefficient is shown as a measure of the monotonic association between two variables. Significance levels are indicated based on the thresholds of p < 0.05 (*), p < 0.01 (**), and p < 0.001 (***).
Figure 4. Trends in Evaporative Stress Index (ESI) from ECOSTRESS (y-axis) against Normalized Difference Vegetation Index (NDVI) from Landsat 8 (x-axis) of agricultural areas with different water allocations in the Maipo River Basin across various times of day (vertically) and growing seasons (horizontally). The positive correlation between the two variables is illustrated by a regression line (dashed) and its 95% confidence interval (shaded). Spearman’s ρ coefficient is shown as a measure of the monotonic association between two variables. Significance levels are indicated based on the thresholds of p < 0.05 (*), p < 0.01 (**), and p < 0.001 (***).
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Figure 5. Trends in Evaporative Stress Index (ESI) from ECOSTRESS (y-axis) against total allocated water (x-axis) of agricultural areas with different water allocations in the Maipo River Basin across various times of day (vertically) and growing seasons (horizontally). The positive correlation between the two variables is illustrated by a regression line (dashed) and its 95% confidence interval (shaded). Spearman’s ρ coefficient is shown as a measure of the monotonic association between two variables. Significance levels are indicated based on the thresholds of p < 0.05 (*), p < 0.01 (**), and p < 0.001 (***).
Figure 5. Trends in Evaporative Stress Index (ESI) from ECOSTRESS (y-axis) against total allocated water (x-axis) of agricultural areas with different water allocations in the Maipo River Basin across various times of day (vertically) and growing seasons (horizontally). The positive correlation between the two variables is illustrated by a regression line (dashed) and its 95% confidence interval (shaded). Spearman’s ρ coefficient is shown as a measure of the monotonic association between two variables. Significance levels are indicated based on the thresholds of p < 0.05 (*), p < 0.01 (**), and p < 0.001 (***).
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Figure 6. Comparison of the distributions in Evaporative Stress Index (ESI) from ECOSTRESS for the 4 major crop types in the Maipo River Basin (irrigated land only) across different times of day and growing seasons.
Figure 6. Comparison of the distributions in Evaporative Stress Index (ESI) from ECOSTRESS for the 4 major crop types in the Maipo River Basin (irrigated land only) across different times of day and growing seasons.
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Table 1. Characteristics of a dozen maize fields selected for study.
Table 1. Characteristics of a dozen maize fields selected for study.
Site IDLat.Lon.Elev.
(m.)		Area
(ha.)		Planting DatePrecipitation *1
(mm)		Temperature *1
(°C)
2019202020212019
–
2020		2020
–
2021		2021
–
2022		2019
–
2020		2020
–
2021		2021
–
2022
LO-101−33.6885−71.326215982.02 Oct.6 Oct.20 Oct.19121338.835.936.3
LO-102−33.6818−71.295015761.79 Oct.28 Sep.10 Sep.21123136.633.934.2
LO-103−33.6850−71.2780170130.913 Oct.10 Oct.-- *21612-- *235.933.4-- *2
LO-104−33.5180−71.208515824.24 Nov.20 Oct.21 Oct.771333.932.434.0
LO-105−33.5090−71.206015740.75 Nov.20 Oct.20 Oct.1271635.335.036.5
LO-106−33.4785−71.093717355.615 Oct.15 Sep.24 Aug.13122435.234.033.6
HI-101−33.6735−70.01552789.731 Oct.21 Nov.7 Nov.9393733.629.731.2
HI-102−33.6733−70.972830811.44 Oct.25 Oct.21 Oct.1871036.133.634.1
HI-103−33.6883−70.955930911.026 Sep.29 Sep.25 Sep.1851235.132.533.1
HI-104−33.8263−70.848735714.425 Sep.27 Sep.5 Dec.17115935.232.529.8
HI-105−33.8518−70.840235526.23 Oct.15 Oct.1 Oct.18131135.532.233.7
HI-106−33.8591−70.774537959.420 Sep.30 Sep.29 Sep.18121234.432.032.6
*1 Total precipitation amount and mean land surface temperature calculated for the growing cycle specific to each field. *2 Maize not planted at this site during this season.
Table 2. Major irrigated areas of the Maipo River Basin with documented surface- and ground-water allocations.
Table 2. Major irrigated areas of the Maipo River Basin with documented surface- and ground-water allocations.
IDPrimary Water SourceIrrigation
Channel		Agri-
Cultural Area
(ha.)		Surface Water Right (m3/s)Ground-
Water Right *1 (m3/s)		Total Allocated Water
Rate
(L/s/ha)		Depth
(mm/yr)
1Estero Cholqui o ChocalanWodehouse [70]14732.300.041.595010
2Estero PuangueCancha de Piedra [70]10840.270.240.471472
3Estero PuangueSan Diego [71]16031.980.001.243896
4Estero PuangueSanta Emilia o Rulano [71]9900.880.070.973045
5Estero PuangueToma del Toro [71]9100.060.030.10315
6Rio AngosturaHospital [72]9950.580.050.642005
7Rio AngosturaUnificado Aguila Norte Aguila Sur [73]9000.580.050.702212
8Rio ClarilloClarillo [71]10360.500.100.571813
9Rio ColinaColina [74]56910.551.010.27864
10Rio Maipo 1st SectionComun Asociacion Canales del Maipo [74]223535.000.623.4410,839
11Rio Maipo 1st SectionDerivado El Carmen Uno [74]10,9125.711.070.511596
12Rio Maipo 1st SectionEyzaguirre [75]177111.470.076.52 *2 20,554
13Rio Maipo 1st SectionFernandino [76]53312.900.330.611909
14Rio Maipo 1st SectionHuidobro [77]848316.070.581.966191
15Rio Maipo 1st SectionLa Isla [78]25771.500.050.601900
16Rio Maipo 1st SectionLo Herrera [75]22882.000.120.932918
17Rio Maipo 1st SectionLonquen Isla [75]24430.610.880.611919
18Rio Maipo 1st SectionPaine [76]26601.900.340.842655
19Rio Maipo 1st SectionQuinta [76]49133.380.640.822582
20Rio Maipo 1st SectionSanta Rita [76]13870.560.040.431350
21Rio Maipo 1st SectionSanta Rita Uno [76]23325.340.062.327304
22Rio Maipo 1st SectionViluco [76]39922.750.490.812561
23Rio Maipo 2nd SectionSan Antonio de Naltahua [75]24042.460.221.113515
24Rio Maipo 3rd SectionCarmen Alto [70]32048.000.162.558027
25Rio Maipo 3rd SectionChocalan [79]22235.000.062.287177
26Rio Maipo 3rd SectionCholqui [79]24072.000.060.862699
27Rio Maipo 3rd SectionCodigua [79]18714.800.002.578091
28Rio Maipo 3rd SectionCulipran [79]31195.000.031.615085
29Rio Maipo 3rd SectionHualemu [70]12342.000.040.511608
30Rio Maipo 3rd SectionHuechun [70]28034.200.041.514770
31Rio Maipo 3rd SectionIsla De Huechun [75]11652.290.011.986229
32Rio Maipo 3rd SectionPuangue [70]29463.600.001.223858
33Rio Maipo 3rd SectionSan Jose [75]63403.26 *30.040.521640
34Rio MapochoCastillo [80]18233.430.161.976203
35Rio MapochoChinihue [81]17832.700.021.524804
36Rio MapochoEl Paico [75]10681.600.001.504725
37Rio MapochoEsperanza Bajo [75]13331.800.011.364275
38Rio MapochoLas Mercedes [82]11,77710.207.281.484680
39Rio MapochoMallarauco [83]85528.690.291.053311
40Rio MapochoSan Miguel [75]17674.000.102.327317
41Rio PeucoChada Tronco [84]21421.930.211.003143
*1 Subsurface water rights were obtained from several publications by Dirección General de Aguas [85,86,87]. *2 This estimated rate appeared to be an outlier and was not included in further analysis. *3 This reference number varied based on the source and was not included in further analysis.
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Goffin, B.D.; Cortés-Monroy, C.C.; Neira-Román, F.; Gupta, D.D.; Lakshmi, V. At Which Overpass Time Do ECOSTRESS Observations Best Align with Crop Health and Water Rights? Remote Sens. 2024, 16, 3174. https://doi.org/10.3390/rs16173174

AMA Style

Goffin BD, Cortés-Monroy CC, Neira-Román F, Gupta DD, Lakshmi V. At Which Overpass Time Do ECOSTRESS Observations Best Align with Crop Health and Water Rights? Remote Sensing. 2024; 16(17):3174. https://doi.org/10.3390/rs16173174

Chicago/Turabian Style

Goffin, Benjamin D., Carlos Calvo Cortés-Monroy, Fernando Neira-Román, Diya D. Gupta, and Venkataraman Lakshmi. 2024. "At Which Overpass Time Do ECOSTRESS Observations Best Align with Crop Health and Water Rights?" Remote Sensing 16, no. 17: 3174. https://doi.org/10.3390/rs16173174

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