Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales

Anderson, Martha C.; Yang, Yang; Xue, Jie; Knipper, Kyle; Yang, Yun; Gao, Feng; Hain, C.; Kustas, William P.; Cawse‐Nicholson, Kerry; Hulley, Glynn; Fisher, Joshua B.; Alfieri, Joseph G.; Meyers, Tilden P.; Prueger, John H.; Baldocchi, Dennis; Rey‐Sánchez, Camilo

doi:10.1016/j.rse.2020.112189

Cited by 101 publications

(79 citation statements)

References 69 publications

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“…It means that multi-spectral data saturate over high yields and cannot capture yield variability. Other variables at such 3 to 30 m scale, such as soil moisture and evapotranspiration (ET) [38][39][40], irrigation vs. rainfed, should be considered, as previous studies showed that field-scale yield variability for corn and soybean in the US is mainly attributed to soil differences in the field and the water holding capacity [41].…”

Section: Discussionmentioning

confidence: 99%

Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery

et al. 2021

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Crop yield monitoring is an important component in agricultural assessment. Multi-spectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 μm), red-edge (0.726 μm), and near-infrared (NIR − 0.865 μm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.

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Section: Discussionmentioning

confidence: 99%

Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery

et al. 2021

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“…The ABI also has the ability to measure the Enhanced Vegetation Index (EVI) which is beneficial in areas (and periods) of dense vegetation cover where, unlike NDVI, EVI does not saturate and in open canopy areas because of a correction factor applied for canopy background (Huete et al, 2002;Zhou et al, 2014). The near-infrared reflectance of vegetation (NIRv) is strongly correlated to the amount of incoming PAR absorbed by, plants and therefore, photosynthesis at half-hourly to annual timescales and has shown stronger relationships with photosynthesis compared to NDVI (Badgley et al, 2017(Badgley et al, , 2019Baldocchi et al, 2020;Wu et al, 2020) and can also, in principle, be measured by GOES (Table 1).…”

Section: Vegetation Greennessmentioning

confidence: 99%

“…In comparison with other sources of insolation data, geostationary-based insolation could significantly improve ET retrievals, particularly in areas of frequent cloud cover where reanalysis estimates may not accurately capture the timing and spatial extents of clouds (Anderson et al, 2019;Wonsook et al, 2020). ALEXI-based ET estimates are produced routinely at 4 km resolution for the United States (Anderson et al, 2020). Also, daily 2 km resolution ALEXI-based ET estimates have been generated from ABI observations as part of the GOES ET and Drought (GET-D) product system (Fang et al, 2019).…”

Section: Land Surface Temperaturementioning

confidence: 99%

Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites

Khan¹,

Stoy

Douglas

et al. 2021

Biogeosciences

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Abstract. Environmental science is increasingly reliant on remotely sensed observations of the Earth's surface and atmosphere. Observations from polar-orbiting satellites have long supported investigations on land cover change, ecosystem productivity, hydrology, climate, the impacts of disturbance, and more and are critical for extrapolating (upscaling) ground-based measurements to larger areas. However, the limited temporal frequency at which polar-orbiting satellites observe the Earth limits our understanding of rapidly evolving ecosystem processes, especially in areas with frequent cloud cover. Geostationary satellites have observed the Earth's surface and atmosphere at high temporal frequency for decades, and their imagers now have spectral resolutions in the visible and near-infrared regions that are comparable to commonly used polar-orbiting sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), or Landsat. These advances extend applications of geostationary Earth observations from weather monitoring to multiple disciplines in ecology and environmental science. We review a number of existing applications that use data from geostationary platforms and present upcoming opportunities for observing key ecosystem properties using high-frequency observations from the Advanced Baseline Imagers (ABI) on the Geostationary Operational Environmental Satellites (GOES), which routinely observe the Western Hemisphere every 5–15 min. Many of the existing applications in environmental science from ABI are focused on estimating land surface temperature, solar radiation, evapotranspiration, and biomass burning emissions along with detecting rapid drought development and wildfire. Ongoing work in estimating vegetation properties and phenology from other geostationary platforms demonstrates the potential to expand ABI observations to estimate vegetation greenness, moisture, and productivity at a high temporal frequency across the Western Hemisphere. Finally, we present emerging opportunities to address the relatively coarse resolution of ABI observations through multisensor fusion to resolve landscape heterogeneity and to leverage observations from ABI to study the carbon cycle and ecosystem function at unprecedented temporal frequency.

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“…For example, assimilation of biomass and ET observations may lead to improved yield estimates as they are closely related to yield formation. Recently, dry matter productivity (DMP) and ET products have been developed by Copernicus Global Land Service (Swinnen et al, 2015) and the NASA ECOSTRESS mission (Fisher et al, 2020;Anderson et al, 2021) at fine resolution, which may be incorporated into the assimilation framework. Alternatively, the AquaCrop model may be coupled to a land surface model to facilitate the assimilation of more variables.…”

Section: Potential Improvement In Future Studiesmentioning

confidence: 99%

Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model

Chibarabada

Ziliani

et al. 2021

Agricultural Water Management

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Parameter calibration is normally required prior to crop model simulation, which can be a time-consuming and data-intensive task. Meanwhile, the growth stages of different hybrids/cultivars of the same crop often show some similarities, which implies that phenological parameters calibrated for one hybrid/cultivar may be useful for the simulation of another. In this study, a data assimilation framework is proposed to reduce the requirement for parameter calibration for maize simulation using AquaCrop. The phenological parameters were uniformly scaled from previous research performed in a different location for a different maize hybrid, and other parameters were taken from default settings in the model documentation. To constrain simulation uncertainties, soil moisture and canopy cover observations were assimilated both separately and jointly in order to update model states. The methodology was tested across a rain-fed field in Nebraska for 6 growing seasons. The results suggested that the under-calibrated model with uniformly scaled phenological parameters captured the temporal dynamics of crop growth, but may lead to large estimation bias. Data assimilation effectively improved model performance, and the joint assimilation outperformed single-variable assimilation. When soil moisture and canopy cover were jointly assimilated, the overall yield estimates (RMSE = 1.24 t/ha, nRMSE = 11.48%, R 2 = 0.695) were improved over the no-assimilation case (RMSE = 2.01 t/ha, nRMSE = 18.61%, R 2 = 0.338). Sensitivity analyses suggested that the improvement was still evident with temporally sparse soil moisture observations and a small ensemble size. Further testing using observations within 90 days after planting demonstrated that the method was able to predict yield around 3 months before harvest (RMSE = 1.7 t/ha, nRMSE = 15.74%). This study indicated that maize yield can be estimated and predicted accurately by monitoring the soil moisture and canopy status, which has potential for regional applications using remote sensing data.

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Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales

Cited by 101 publications

References 69 publications

Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery

Assessing within-Field Corn and Soybean Yield Variability from WorldView-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery

Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites

Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model

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