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The global expansion of photovoltaic (PV) power plants, especially in ecologically fragile regions like the Gobi Desert, highlights the suitability of such areas for large-scale PV development. The most direct impact of PV development in the Gobi Desert is temperature change that results from the land-use-induced albedo changes; however, the detailed and systemic understanding of the effects of PV expansion on land surface temperature remains limited. This study focuses on the 16 largest PV plants in the Chinese Gobi Desert, utilizing remote sensing data to assess their effects on land surface temperature. Our result showed a cooling effect during the daytime (−0.69 ± 0.10 °C), but a warming effect during the nighttime (0.23 ± 0.05 °C); the overall effect on the daily mean was a cooling effect (−0.22 ± 0.05 °C). Seasonal variations were observed, with the most significant cooling effect in autumn and the weakest in summer. The PV area was the most significant factor which influenced the temperature variation across PV plants. Our findings enrich our understanding of the environmental effects arising from the construction of PV plants and provide vital information for the design and management of increasingly renewable electricity systems globally. [ABSTRACT FROM AUTHOR]

Accurate and consistent observations of diurnal variability of sea surface temperature (SST DV) and its impact on air–sea heat fluxes over large areas for extended periods are challenging due to their short time scale and wide coverage. The hourly gap-free SSTs generated from Japan Aerospace Exploration Agency-Japan Agency for Marine–Earth Science and Technology (JAXA-JAMSTEC) are input to the COARE3.5 bulk flux algorithm to investigate the impact of SST DV on air–sea heat fluxes over the Northwest Pacific Ocean (NWPO). The main results are as follows. (1) The JAXA-JAMSTEC SSTs were found to be in good agreement with the buoy observations on SST DV with a very slight negative bias of −0.007 °C and a root mean square error of 0.018 °C. (2) The case study conducted on 26 June 2020 showed that the fluxes' diurnal amplitudes were about 30–50 W   m − 2 , and evolution was in agreement with SST DV. (3) The average impact of SST DV on heat fluxes was 2.93 W   m − 2 over the subtropical NWPO, decreasing from southeast to northwest and from low to high latitudes, and showing a clear seasonal cycle during 2019–2022. This research highlights the need to consider SST DV for accurate estimation of heat fluxes, which is crucial for climate and atmospheric studies. [ABSTRACT FROM AUTHOR]

Projections of future climate are sensitive to the representation of upper-ocean diurnal variability, including the diurnal cycle of winds. Two different methods suitable for time series with missing data are used here to characterize how observed diurnal winds vary over the year. One is based on diurnal composites of mooring data, and the other is based on harmonic analysis via a least squares fit and is able to isolate annual (i.e., 1 cycle per year) modulation of diurnal variability. Results show that the diurnal amplitude in meridional winds is larger than in zonal winds and peaks in the tropical Pacific, where diurnal variability in zonal winds is overall weaker compared to other basins. Furthermore, the amplitude and phasing of diurnal winds in the tropical oceans are not uniform in time, with overall larger differences through the year in the meridional component of tropical winds. Estimating the annual modulation of the diurnal signal implies resolving both the diurnal energy peak and also the modulation of this peak by the annual cycle. This leads to a recommendation for sampling at least 6 times per day and for a duration of at least 3 years.

The global expansion of photovoltaic (PV) power plants, especially in ecologically fragile regions like the Gobi Desert, highlights the suitability of such areas for large-scale PV development. The most direct impact of PV development in the Gobi Desert is temperature change that results from the land-use-induced albedo changes; however, the detailed and systemic understanding of the effects of PV expansion on land surface temperature remains limited. This study focuses on the 16 largest PV plants in the Chinese Gobi Desert, utilizing remote sensing data to assess their effects on land surface temperature. Our result showed a cooling effect during the daytime (−0.69 ± 0.10 °C), but a warming effect during the nighttime (0.23 ± 0.05 °C); the overall effect on the daily mean was a cooling effect (−0.22 ± 0.05 °C). Seasonal variations were observed, with the most significant cooling effect in autumn and the weakest in summer. The PV area was the most significant factor which influenced the temperature variation across PV plants. Our findings enrich our understanding of the environmental effects arising from the construction of PV plants and provide vital information for the design and management of increasingly renewable electricity systems globally.

Accurate and consistent observations of diurnal variability of sea surface temperature (SST DV) and its impact on air–sea heat fluxes over large areas for extended periods are challenging due to their short time scale and wide coverage. The hourly gap-free SSTs generated from Japan Aerospace Exploration Agency-Japan Agency for Marine–Earth Science and Technology (JAXA-JAMSTEC) are input to the COARE3.5 bulk flux algorithm to investigate the impact of SST DV on air–sea heat fluxes over the Northwest Pacific Ocean (NWPO). The main results are as follows. (1) The JAXA-JAMSTEC SSTs were found to be in good agreement with the buoy observations on SST DV with a very slight negative bias of −0.007 °C and a root mean square error of 0.018 °C. (2) The case study conducted on 26 June 2020 showed that the fluxes’ diurnal amplitudes were about 30–50 W m−2, and evolution was in agreement with SST DV. (3) The average impact of SST DV on heat fluxes was 2.93 W m−2 over the subtropical NWPO, decreasing from southeast to northwest and from low to high latitudes, and showing a clear seasonal cycle during 2019–2022. This research highlights the need to consider SST DV for accurate estimation of heat fluxes, which is crucial for climate and atmospheric studies.

This study developed a new atmospheric correction algorithm, GeoNEX-AC, that is independent from the traditional use of spectral band ratios but dedicated to exploiting information from the diurnal variability in the hypertemporal geostationary observations. The algorithm starts by evaluating smooth segments of the diurnal time series of the top-of-atmosphere (TOA) reflectance to identify clear-sky and snow-free observations. It then attempts to retrieve the Ross-Thick–Li-Sparse (RTLS) surface bi-directional reflectance distribution function (BRDF) parameters and the daily mean atmospheric optical depth (AOD) with an atmospheric radiative transfer model (RTM) to optimally simulate the observed diurnal variability in the clear-sky TOA reflectance. Once the initial RTLS parameters are retrieved after the algorithm’s burn-in period, they serve as the prior information to estimate the AOD levels for the following days and update the surface BRDF information with the new clear-sky observations. This process is iterated through the full time span of the observations, skipping only totally cloudy days or when surface snow is detected. We tested the algorithm over various Aerosol Robotic Network (AERONET) sites and the retrieved results well agree with the ground-based measurements. This study demonstrates that the high-frequency diurnal geostationary observations contain unique information that can help to address the atmospheric correction problem from new directions.

Projections of future climate are sensitive to the representation of upper-ocean diurnal variability, including the diurnal cycle of winds. Two different methods suitable for time series with missing data are used here to characterize how observed diurnal winds vary over the year. One is based on diurnal composites of mooring data, and the other is based on harmonic analysis via a least squares fit and is able to isolate annual (i.e., 1 cycle per year) modulation of diurnal variability. Results show that the diurnal amplitude in meridional winds is larger than in zonal winds and peaks in the tropical Pacific, where diurnal variability in zonal winds is overall weaker compared to other basins. Furthermore, the amplitude and phasing of diurnal winds in the tropical oceans are not uniform in time, with overall larger differences through the year in the meridional component of tropical winds. Estimating the annual modulation of the diurnal signal implies resolving both the diurnal energy peak and also the modulation of this peak by the annual cycle. This leads to a recommendation for sampling at least 6 times per day and for a duration of at least 3 years.

Airborne remote sensing technologies have been widely applied in field crop phenotyping. However, the quality of current remote sensing data suffers from significant diurnal variances. The severity of the diurnal issue has been reported in various plant phenotyping studies over the last four decades, but there are limited studies on the modeling of the diurnal changing patterns that allow people to precisely predict the level of diurnal impacts. In order to comprehensively investigate the diurnal variability, it is necessary to collect time series field images with very high sampling frequencies, which has been difficult. In 2019, Purdue agricultural (Ag) engineers deployed their first field visible to near infrared (VNIR) hyperspectral gantry platform, which is capable of repetitively imaging the same field plots every 2.5 min. A total of 8631 hyperspectral images of the same field were collected for two genotypes of corn plants from the vegetative stage V4 to the reproductive stage R1 in the 2019 growing season. The analysis of these images showed that although the diurnal variability is very significant for almost all the image-derived phenotyping features, the diurnal changes follow stable patterns. This makes it possible to predict the imaging drifts by modeling the changing patterns. This paper reports detailed diurnal changing patterns for several selected plant phenotyping features such as Normalized Difference Vegetation Index (NDVI), Relative Water Content (RWC), and single spectrum bands. For example, NDVI showed a repeatable V-shaped diurnal pattern, which linearly drops by 0.012 per hour before the highest sun angle and increases thereafter by 0.010 per hour. The different diurnal changing patterns in different nitrogen stress treatments, genotypes and leaf stages were also compared and discussed. With the modeling results of this work, Ag remote sensing users will be able to more precisely estimate the deviation/change of crop feature predictions caused by the specific imaging time of the day. This will help people to more confidently decide on the acceptable imaging time window during a day. It can also be used to calibrate/compensate the remote sensing result against the time effect.

Short-term (sub-diurnal) biological and biogeochemical processes cannot be fully captured by the current suite of polar-orbiting satellite ocean color sensors, as their temporal resolution is limited to potentially one clear image per day. Geostationary sensors, such as the Geostationary Ocean Color Imager (GOCI) from the Republic of Korea, allow the study of these short-term processes because their orbit permit the collection of multiple images throughout each day for any area within the sensor’s field of regard. Assessing the capability to detect sub-diurnal changes in in-water properties caused by physical and biogeochemical processes characteristic of open ocean and coastal ocean ecosystems, however, requires an understanding of the uncertainties introduced by the instrument and/or geophysical retrieval algorithms. This work presents a study of the uncertainties during the daytime period for an ocean region with characteristically low-productivity with the assumption that only small and undetectable changes occur in the in-water properties due to biogeochemical processes during the daytime period. The complete GOCI mission data were processed using NASA’s SeaDAS/l2gen package. The assumption of homogeneity of the study region was tested using three-day sequences and diurnal statistics. This assumption was found to hold based on the minimal diurnal and day-to-day variability in GOCI data products. Relative differences with respect to the midday value were calculated for each hourly observation of the day in order to investigate what time of the day the variability is greater. Also, the influence of the solar zenith angle in the retrieval of remote sensing reflectances and derived products was examined. Finally, we determined that the uncertainties in water-leaving “remote-sensing„ reflectance (Rrs) for the 412, 443, 490, 555, 660 and 680 nm bands on GOCI are 8.05 × 10−4, 5.49 × 10−4, 4.48 × 10−4, 2.51 × 10−4, 8.83 × 10−5, and 1.36 × 10−4 sr−1, respectively, and 1.09 × 10−2 mg m−3 for the chlorophyll-a concentration (Chl-a), 2.09 × 10−3 m−1 for the absorption coefficient of chromophoric dissolved organic matter at 412 nm (ag (412)), and 3.7 mg m−3 for particulate organic carbon (POC). These Rrs values can be considered the threshold values for detectable changes of the in-water properties due to biological, physical or biogeochemical processes from GOCI.

This study developed a new atmospheric correction algorithm, GeoNEX-AC, that is independent from the traditional use of spectral band ratios but dedicated to exploiting information from the diurnal variability in the hypertemporal geostationary observations. The algorithm starts by evaluating smooth segments of the diurnal time series of the top-of-atmosphere (TOA) reflectance to identify clear-sky and snow-free observations. It then attempts to retrieve the Ross-Thick–Li-Sparse (RTLS) surface bi-directional reflectance distribution function (BRDF) parameters and the daily mean atmospheric optical depth (AOD) with an atmospheric radiative transfer model (RTM) to optimally simulate the observed diurnal variability in the clear-sky TOA reflectance. Once the initial RTLS parameters are retrieved after the algorithm's burn-in period, they serve as the prior information to estimate the AOD levels for the following days and update the surface BRDF information with the new clear-sky observations. This process is iterated through the full time span of the observations, skipping only totally cloudy days or when surface snow is detected. We tested the algorithm over various Aerosol Robotic Network (AERONET) sites and the retrieved results well agree with the ground-based measurements. This study demonstrates that the high-frequency diurnal geostationary observations contain unique information that can help to address the atmospheric correction problem from new directions. [ABSTRACT FROM AUTHOR]

Airborne remote sensing technologies have been widely applied in field crop phenotyping. However, the quality of current remote sensing data suffers from significant diurnal variances. The severity of the diurnal issue has been reported in various plant phenotyping studies over the last four decades, but there are limited studies on the modeling of the diurnal changing patterns that allow people to precisely predict the level of diurnal impacts. In order to comprehensively investigate the diurnal variability, it is necessary to collect time series field images with very high sampling frequencies, which has been difficult. In 2019, Purdue agricultural (Ag) engineers deployed their first field visible to near infrared (VNIR) hyperspectral gantry platform, which is capable of repetitively imaging the same field plots every 2.5 min. A total of 8631 hyperspectral images of the same field were collected for two genotypes of corn plants from the vegetative stage V4 to the reproductive stage R1 in the 2019 growing season. The analysis of these images showed that although the diurnal variability is very significant for almost all the image-derived phenotyping features, the diurnal changes follow stable patterns. This makes it possible to predict the imaging drifts by modeling the changing patterns. This paper reports detailed diurnal changing patterns for several selected plant phenotyping features such as Normalized Difference Vegetation Index (NDVI), Relative Water Content (RWC), and single spectrum bands. For example, NDVI showed a repeatable V-shaped diurnal pattern, which linearly drops by 0.012 per hour before the highest sun angle and increases thereafter by 0.010 per hour. The different diurnal changing patterns in different nitrogen stress treatments, genotypes and leaf stages were also compared and discussed. With the modeling results of this work, Ag remote sensing users will be able to more precisely estimate the deviation/change of crop feature predictions caused by the specific imaging time of the day. This will help people to more confidently decide on the acceptable imaging time window during a day. It can also be used to calibrate/compensate the remote sensing result against the time effect. [ABSTRACT FROM AUTHOR]

Short-term (sub-diurnal) biological and biogeochemical processes cannot be fully captured by the current suite of polar-orbiting satellite ocean color sensors, as their temporal resolution is limited to potentially one clear image per day. Geostationary sensors, such as the Geostationary Ocean Color Imager (GOCI) from the Republic of Korea, allow the study of these short-term processes because their orbit permit the collection of multiple images throughout each day for any area within the sensor's field of regard. Assessing the capability to detect sub-diurnal changes in in-water properties caused by physical and biogeochemical processes characteristic of open ocean and coastal ocean ecosystems, however, requires an understanding of the uncertainties introduced by the instrument and/or geophysical retrieval algorithms. This work presents a study of the uncertainties during the daytime period for an ocean region with characteristically low-productivity with the assumption that only small and undetectable changes occur in the in-water properties due to biogeochemical processes during the daytime period. The complete GOCI mission data were processed using NASA's SeaDAS/l2gen package. The assumption of homogeneity of the study region was tested using three-day sequences and diurnal statistics. This assumption was found to hold based on the minimal diurnal and day-to-day variability in GOCI data products. Relative differences with respect to the midday value were calculated for each hourly observation of the day in order to investigate what time of the day the variability is greater. Also, the influence of the solar zenith angle in the retrieval of remote sensing reflectances and derived products was examined. Finally, we determined that the uncertainties in water-leaving "remote-sensing" reflectance (Rrs) for the 412, 443, 490, 555, 660 and 680 nm bands on GOCI are 8.05 × 10−4, 5.49 × 10−4, 4.48 × 10−4, 2.51 × 10−4, 8.83 × 10−5, and 1.36 × 10−4 sr−1, respectively, and 1.09 × 10−2 mg m−3 for the chlorophyll-a concentration (Chl-a), 2.09 × 10−3 m−1 for the absorption coefficient of chromophoric dissolved organic matter at 412 nm (ag (412)), and 3.7 mg m−3 for particulate organic carbon (POC). These Rrs values can be considered the threshold values for detectable changes of the in-water properties due to biological, physical or biogeochemical processes from GOCI. [ABSTRACT FROM AUTHOR]