add accepted ms notice to firstpage header

manuscript/manuscript.tex

@@ -16,6 +16,13 @@
 % \SetWatermarkScale{5}
 \usepackage{float}
 
+\usepackage{fancyhdr}
+\pagestyle{fancy}
+\fancyhf{}% clear default for head and foot
+\fancypagestyle{firstpage}{%  
+  \lhead{This is an Accepted Manuscript of an article published by the Society of Photo-Optical Instrumentation Engineers in the Journal of Applied Remote Sensing.}
+}
+
 \title{Geographically aware estimates of remotely sensed water properties for Chesapeake Bay}
 \author{Jemma Stachelek$^{1}$, Sofia Avendaño$^{1,}$$^{2}$, Jon Schwenk$^{1}$  \\
         \small $^{1}$Los Alamos National Laboratory, Division of Earth and Environmental Sciences, Los Alamos, NM 87545, USA \\
@@ -25,6 +32,7 @@
 
 \begin{document}
 \maketitle
+\thispagestyle{firstpage}
 
 \begin{abstract}
     \noindent Remotely sensed water properties are important for a variety of applications, including validation of Earth Systems models (ESMs), habitat suitability models, and sea level rise projections. For the validation of next-generation, high or multi-resolution (30-60 km) ESMs in particular, the usefulness of operational forecasting products and directly-observing satellite-based sensors for validation is limited due to their temporal availability and spatial resolution of $<$ 1 year (in some cases) and $>$ 30 $km^2$ respectively. To address this validation data gap, we developed a data-driven model to produce high-resolution ($<$ 1 $km^2$) estimates of temperature, salinity, and turbidity over decadal time scales as required by next-generation ESMs. Our model fits daily MODIS Aqua reflectance data to surface observations ($<$ 1 m depth) from 2000-2021 in Chesapeake Bay, USA. The resulting models have similar error statistics as prior efforts of this type for salinity (RMSE: 2.3) and temperature (RMSE: 1.8 C). However, unlike prior efforts our model is designed as a pipeline meaning that it has the advantage of producing predictions of water properties in future time periods as additional MODIS data becomes available. We also include novel  “geographically-aware” predictive features insofar as they capture geographic variation in the influence of flow and surface water exchange in upstream coastal watersheds.