Utility of thermal image sharpening for monitoring field-scale evapotranspiration over rainfed and irrigated agricultural regions

The utility of a thermal image sharpening algorithm (TsHARP) in providing fine resolution land surface temperature data to a Two-Source-Model for mapping evapotranspiration (ET) was examined over two agricultural regions in the U.S. One site is in a rainfed corn and soybean production region in central Iowa. The other lies within the Texas High Plains, an irrigated agricultural area. It is concluded that in the absence of fine (sub-field scale) resolution thermal data, TsHARP provides an important tool for monitoring ET over rainfed agricultural areas. In contrast, over irrigated regions, TsHARP applied to kilometer-resolution thermal imagery is unable to provide accurate fine resolution land surface temperature due to significant sub-pixel moisture variations that are not captured in the sharpening procedure. Consequently, reliable estimation of ET and crop stress requires thermal imagery acquired at high spatial resolution, resolving the dominant length-scales of moisture variability present within the landscape

Utility of thermal sharpening over Texas high plains irrigated agricultural fields

Irrigated crop production in the Texas high plains (THP) is dependent on water extracted from the Ogallala Aquifer, an area suffering from sever water shortage. Water management in this area is therefore highly important. Thermal satellite imagery at high temporal (~daily) and high spatial (~100 m) resolutions could provide important surface boundary conditions for vegetation stress and water use monitoring, mainly through energy balance models such as DisALEXI. At present, however, no satellite platform collects such high spatiotemporal resolution data. The objective of this study is to examine the utility of an image sharpening technique (TsHARP) for retrieving land surface temperature at high spatial resolution (down to 60 m) from moderate spatial resolution (1 km) imagery, which is typically available at higher (~daily) temporal frequency. A simulated sharpening experiment was applied to Landsat 7 imagery collected over the THP in September 2002 to examine its utility over both agricultural and natural vegetation cover. The Landsat thermal image was aggregated to 960 m resolution and then sharpened to its native resolution of 60 m and to various intermediate resolutions. The algorithm did not provide any measurable improvement in estimating high-resolution temperature distributions over natural land cover. In contrast, TsHARP was shown to retrieve high-resolution temperature information with good accuracy over much of the agricultural area within the scene. However, in recently irrigated fields, TsHARP could not reproduce the temperature patterns. Therefore we conclude that TsHARP is not an adequate substitute for 100-m-scale observations afforded by the current Landsat platforms. Should the thermal imager be removed from follow-on Landsat platforms, we will lose valuable capacity to monitor water use at the field scale, particularly in many agricultural regions where the typical field size is ~100 X 100 m. In this scenario, sharpened thermal imagery from instruments like MODIS or VIIRS would be the suboptimal alternative

Utility of thermal sharpening over Texas high plains irrigated agricultural fields

Irrigated crop production in the Texas high plains (THP) is dependent on water extracted from the Ogallala Aquifer, an area suffering from sever water shortage. Water management in this area is therefore highly important. Thermal satellite imagery at high temporal (~daily) and high spatial (~100 m) resolutions could provide important surface boundary conditions for vegetation stress and water use monitoring, mainly through energy balance models such as DisALEXI. At present, however, no satellite platform collects such high spatiotemporal resolution data. The objective of this study is to examine the utility of an image sharpening technique (TsHARP) for retrieving land surface temperature at high spatial resolution (down to 60 m) from moderate spatial resolution (1 km) imagery, which is typically available at higher (~daily) temporal frequency. A simulated sharpening experiment was applied to Landsat 7 imagery collected over the THP in September 2002 to examine its utility over both agricultural and natural vegetation cover. The Landsat thermal image was aggregated to 960 m resolution and then sharpened to its native resolution of 60 m and to various intermediate resolutions. The algorithm did not provide any measurable improvement in estimating high-resolution temperature distributions over natural land cover. In contrast, TsHARP was shown to retrieve high-resolution temperature information with good accuracy over much of the agricultural area within the scene. However, in recently irrigated fields, TsHARP could not reproduce the temperature patterns. Therefore we conclude that TsHARP is not an adequate substitute for 100-m-scale observations afforded by the current Landsat platforms. Should the thermal imager be removed from follow-on Landsat platforms, we will lose valuable capacity to monitor water use at the field scale, particularly in many agricultural regions where the typical field size is ~100 X 100 m. In this scenario, sharpened thermal imagery from instruments like MODIS or VIIRS would be the suboptimal alternative

Real-time citrus variety detection in orchards based on complex scenarios of improved YOLOv7

Variety detection provides technical support for selecting XinHui citrus for use in the production of XinHui dried tangerine peel. Simultaneously, the mutual occlusion between tree leaves and fruits is one of the challenges in object detection. In order to improve screening efficiency, this paper introduces a YOLO(You Only Look Once)v7-BiGS(BiFormer&GSConv) citrus variety detection method capable of identifying different citrus varieties efficiently. In the YOLOv7-BiGS network model, initially, the BiFormer attention mechanism in the backbone of the YOLOv7-based network strengthens the model’s ability to extract citrus’ features. In addition, the introduction of the lightweight GSConv convolution in place of the original convolution within the ELAN of the head component effectively streamlines model complexity while maintaining performance integrity. To environment challenge validate the effectiveness of the method, the proposed YOLOv7-BiGS was compared with YOLOv5, YOLOv7, and YOLOv8. In the comparison of YOLOv7-BiGS with YOLOv5, YOLOv7, and YOLOv8, the experimental results show that the precision, mAP and recell of YOLOv7-BiGS are 91%, 93.7% and 87.3% respectively. Notably, compared to baseline methods, the proposed approach exhibited significant enhancements in precision, mAP, and recall by 5.8%, 4.8%, and 5.2%, respectively. To evaluate the efficacy of the YOLOv7-BiGS in addressing challenges posed by complex environmental conditions, we collected occluded images of Xinhui citrus fruits from the Xinhui orchard base for model detection. This research aims to fulfill performance criteria for citrus variety identification, offering vital technical backing for variety detection endeavors

A new maturity recognition algorithm for Xinhui citrus based on improved YOLOv8

Current object detection algorithms lack accuracy in detecting citrus maturity color, and feature extraction needs improvement. In automated harvesting, accurate maturity detection reduces waste caused by incorrect evaluations. To address this issue, this study proposes an improved YOLOv8-based method for detecting Xinhui citrus maturity. GhostConv was introduced to replace the ordinary convolution in the Head of YOLOv8, reducing the number of parameters in the model and enhancing detection accuracy. The CARAFE (Content-Aware Reassembly of Features) upsampling operator was used to replace the conventional upsampling operation, retaining more details through feature reorganization and expansion. Additionally, the MCA (Multidimensional Collaborative Attention) mechanism was introduced to focus on capturing the local feature interactions between feature mapping channels, enabling the model to more accurately extract detailed features, thus further improving the accuracy of citrus color identification. Experimental results show that the precision, recall, and average precision of the improved YOLOv8 on the test set are 88.6%, 93.1%, and 93.4%, respectively. Compared to the original model, the improved YOLOv8 achieved increases of 16.5%, 20.2%, and 14.7%, respectively, and the parameter volume was reduced by 0.57%. This paper aims to improve the model for detecting Xinhui citrus maturity in complex orchards, supporting automated fruit-picking systems

Protein tyrosine phosphatase Meg2 dephosphorylates signal transducer and activator of transcription 3 and suppresses tumor growth in breast cancer

INTRODUCTION: Signal transducer and activator of transcription 3 (STAT3) is over-activated or phosphorylated in breast cancers. The hyper-phosphorylation of STAT3 was attributed to either up-regulated phosphorylation by several tyrosine-kinases or down-regulated activity of phosphatases. Although several factors have been identified to phosphorylate STAT3, it remains unclear how STAT3 is dephosphorylated by PTPMeg2. The aim of this study was to determine the role of PTPMeg2 as a phosphatase in regulation of the activity of STAT3 in breast cancers. METHODS: Immunoprecipitation assays were used to study the interaction of STAT3 with PTPMeg2. A series of biochemistry experiments were performed to evaluate the role of PTPMeg2 in the dephosphorylation of STAT3. Two breast cancer cell lines MCF7 (PTPMeg2 was depleted as it was endogenously high) and MDA-MB-231 (PTPMeg2 was overexpressed as it was endogenously low) were used to compare the level of phosphorylated STAT3 and the tumor growth ability in vitro and in vivo. Samples from breast carcinoma (n = 73) were subjected to a pair-wise Pearson correlation analysis for the correlation of levels of PTPMeg2 and phosphorylated STAT3. RESULTS: PTPMeg2 directly interacts with STAT3 and mediates its dephosphorylation in the cytoplasm. Over-expression of PTPMeg2 decreased tyrosine phosphorylation of STAT3 while depletion of PTPMeg2 increased its phosphorylation. The decreased tyrosine phosphorylation of STAT3 is coupled with suppression of STAT3 transcriptional activity and reduced tumor growth in vitro and in vivo. Levels of PTPMeg2 and phosphorylated STAT3 were inversely correlated in breast cancer tissues (P = 0.004). CONCLUSIONS: PTPMeg2 is an important phosphatase for the dephosphorylation of STAT3 and plays a critical role in breast cancer development

Large scale estimation of evapotranspiration

Evapotranspiration is an essential component of the energy and water bud­get, but its estimation depends on available data sources and the environment of an area. Remote sensing techniques, combined with routine meteorological data, are used to estimate evapotranspiration over central Australia through the development and application of a number of models, ranging from physically based instantaneous models to a daily simulation model. The proposed models are evaluated using aircraft observations over two dis­tinct vegetation regimes in south-western Australia. Among the three physically based instantaneous models, single-source models using an excess resistance term empirically determined performed better than a two-source model which does not require such a parameterization. The mean absolute difference between measured and estimated values of the sensible heat flux is below 17 wm-2 in comparison to approximately 40 Wm-2 for evapotranspiration. Estimates of evapotranspiration depend on the closure of the surface energy balance and incorporate all residual errors in this closure. All models perform better over the agricultural vegetation than over the native vegetation. As these physically based models only provide instantaneous estimates of evapotranspiration at satellite overpass, a coupled one dimensional soil-canopy­atmosphere model and a simple budget water balance model have been used to simulate the daily evapotranspiration. Comparison of these results with the air-craft observations shows that the coupled model provides a good estimate of sur­face heat fluxes over the agricultural area with mean absolute differences between measured and estimate values being approximately 25 wm-2 for both sensible heat flux and evapotranspiration. Over the native vegetation, the mean absolute difference between measured and observed fluxes increased to 49 and 47 wm-2, respectively, for the sensible heat and evapotranspiration. This increase results from the inability of a simple water balance model to incorporate the effects of the underlying aquifer on deep rooted native vegetation, particularly during the dry summer season. It also highlights the sensitivity of the one dimensional soil-canopy-atmosphere model to the specification of soil moisture. Since the model simulation of surface temperature is also very sensitive to the soil moisture, a comparison between model simulation of surface temperature and satellite derived surface temperature was used to adjust parameters of a water balance model resulting in better estimates of soil moisture and consequently improved predictions of evapotranspiration. These models have been applied to estimating evapotranspiration in central Australia, using limited routine meteorological data and the NOAA-14 AVHRR overpass. Minimizing the difference between model predicted surface temperature and satellite derived temperature to adjust the estimated soil moisture, both the instantaneous physically based model and the simulation yielded consistent re­sults for 8 representative clear sky days during 1996-1997. These results highlight the sensitivity of surface temperature to soil moisture and suggest that radiomet­ric surface temperature can be used to adjust simple water balance estimates of soil moisture providing a simple and effective means of estimating large scale evapotranspiration in remote arid regions

Research on the stability of non‐equigap grey control model under multiple transformations

PurposeThe purpose of this paper is to deal with the ill‐conditioned problem for the non‐equigap GM(1,1) control model by using the method of multiple transformations.Design/methodology/approachOwing to noises and interferences from both inside and outside of the system, many control systems contain unequal intervals and sharp variation which may result in undesirable systems instability. In order to ensure the stability and efficiency of grey forecasting control model, the data transformation for a raw series is an important and useful method for enhancing accuracy and improving ill‐condition of the non‐equigap GM(1,1) model.FindingsThis paper discusses the quantitative relations between the multiple transformation and the parameters of the non‐equigap GM(1,1) model in detail, and studies the effect of the multiple transformation on the condition number of the non‐equigap GM(1,1) model.Research limitations/implicationsAccessibility and availability of data are the main limitations based on which model will be applied.Practical implicationsChoosing an appropriate multiple of transformation cannot only eliminate dimension, lessen computation and maintain high accuracy, but also largely reduce the condition number of the model and improve the ill‐condition of non‐equigap GM(1,1) model effectively.Originality/valueThis paper seems to be the first to discuss the stability problems for the non‐equigap GM(1,1) model.</jats:sec