Estimating High Spatial Resolution Clear-Sky Land Surface Longwave Radiation Budget from MODIS and GOES Data

The surface radiation budget (SRB) is important in addressing a variety of scientific and application issues related to climate trends, hydrological and biogeophysical modeling, and agriculture. The three longwave components of SRB are surface downwelling, upwelling, and net longwave radiation (LWDN, LWUP, and LWNT). Existing surface longwave radiation budget (SLRB) datasets have coarse spatial resolution and their accuracy needs to be greatly improved. This study develops new hybrid methods for estimating instantaneous clear-sky high spatial resolution land LWDN and LWUP from the Moderate Resolution Imaging Spectroradiometer (MODIS, 1km) and the Geostationary Operational Environmental Satellites (GOES, 2-10 km) data. The hybrid methods combine extensive radiation transfer (physical) and statistical analysis (statistical) and share the same general framework. LWNT is derived from LWDN and LWUP. This study is the first effort to estimate SLRB using MODIS 1 km data. The new hybrid methods are unique in at least two other aspects. First, the radiation transfer simulation accounted for land surface emissivity effect. Second, the surface pressure effect in LWDN was considered explicitly by incorporating surface elevation in the statistical models. Nonlinear models were developed using the simulated databases to estimate LWDN from MODIS TOA radiance and surface elevation. Artificial Neural Network (ANN) models were developed to estimate LWUP from MODIS TOA radiance. The LWDN and LWUP models can explain more than 93.6% and 99.6% of variations in the simulated databases, respectively. Preliminary study indicates that similar hybrid methods can be developed to estimate LWDN and LWUP from the current GOES-12 Sounder data and the future GOES-R data. The new hybrid methods and alternative methods were evaluated using two years of ground measurements at six validation sites from the Surface Radiation Budget Network (SURFRAD). Validation results indicate the hybrid methods outperform alternative methods. The mean RMSEs of MODIS-derived LWDN, LWUP, and LWNT using the hybrid methods are 16.88, 15.23, and 17.30 W/m2. The RMSEs of GOES-12 Sounder-derived LWDN and LWUP are smaller than 23.70 W/m2. The high spatial resolution MODIS and GOES SLRB derived in this study is more accurate than existing datasets and can be used to support high resolution numerical models

Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX’08 field campaign

Robust spatial information about environmental water use at field scales and daily to seasonal timesteps will benefit many applications in agriculture and water resource management. This information is particularly critical in arid climates where freshwater resources are limited or expensive, and groundwater supplies are being depleted at unsustainable rates to support irrigated agriculture as well as municipal and industrial uses. Gridded evapotranspiration (ET) information at field scales can be obtained periodically using land–surface temperature-based surface energy balance algorithms applied to moderate resolution satellite data from systems like Landsat, which collects thermal-band imagery every 16 days at a resolution of approximately 100 m. The challenge is in finding methods for interpolating between ET snapshots developed at the time of a clear-sky Landsat overpass to provide complete daily time-series over a growing season. This study examines the efficacy of a simple gap-filling algorithm designed for applications in data-sparse regions, which does not require local ground measurements of weather or rainfall, or estimates of soil texture. The algorithm relies on general conservation of the ratio between actual ET and a reference ET, generated from satellite insolation data and standard meteorological fields from a mesoscale model. The algorithm was tested with ET retrievals from the Atmosphere–Land Exchange Inverse (ALEXI) surface energy balance model and associated DisALEXI flux disaggregation technique, which uses Landsat-scale thermal imagery to reduce regional ALEXI maps to a finer spatial resolution. Daily ET at the Landsat scale was compared with lysimeter and eddy covariance flux measurements collected during the Bushland Evapotranspiration and Agricultural Remote sensing EXperiment of 2008 (BEAREX08), conducted in an irrigated agricultural area in the Texas Panhandle under highly advective conditions. The simple gap-filling algorithm performed reasonably at most sites, reproducing observed cumulative ET to within 5–10% over the growing period from emergence to peak biomass in both rainfed and irrigated fields

Climatic impacts of bushland to cropland conversion in Eastern Africa

Bushlands (Acacia-Commiphora) constitute the largest and one of the most threatened ecosystems in East Africa. Although several studies have investigated the climatic impacts of land changes on local and global climate, the main focus has been on forest loss and the impacts of bushland clearing thus remain poorly understood. Measuring the impacts of bushland loss on local climate is challenging given that changes often occur at fragmented and small patches. Here, we apply high-resolution satellite imagery and land surface flux modeling approaches to unveil the impacts of bushland clearing on surface biophysical properties and its associated effects on surface energy balance and land surface temperature. Our results show that bushland clearing leads to an average reduction in evapotranspiration of 0.4 mm day(-1). The changes in surface biophysical properties affected the surface energy balance components with different magnitude. The reduction in latent heat flux was stronger than other surface energy fluxes and resulted in an average net increase in daytime land surface temperature (LST) of up to 1.75 K. These results demonstrate the important impact of bushland-to-cropland conversion on the local climate, as they reveal increases in LST of a magnitude comparable to those caused by forest loss. This finding highlights the necessity of bushland conservation for regulating the land surface temperature in East Africa and, at the same time, warns of the climatic impacts of clearing bushlands for agriculture. (c) 2020 The Authors. Published by Elsevier B.V.Peer reviewe

Evaluation Of CMIP5 Simulated Clouds And TOA Radiation Budgets Using NASA Satellite Observations

A large degree of uncertainty in global climate models (GCMs) can be attributed to the representation of clouds and how they interact with incoming solar and outgoing longwave (Earth emitted) radiation. In this study, the simulated total cloud fraction (CF), cloud water path (CWP), top-of-atmosphere (TOA) radiation budgets and cloud radiative forcings (CRFs) from 28 CMIP5 AMIP models are evaluated and compared to multiple satellite observations from CERES, MODIS, ISCCP, CloudSat, and CALIPSO. The multimodel ensemble mean CF (58.6 %) is, on global average, under estimated by nearly 7 % compared to CERES-MODIS (CM) and ISCCP results, with an even larger negative bias (16.7 %) compared to the CloudSat/CALIPSO result. The CWP bias is similar in comparison to the CF result; the multimodel ensemble mean is under estimated (16.4 gm−2) when compared to CM. The model simulated and CERES EBAF observed TOA reflected shortwave (SW) and outgoing longwave (LW) radiation fluxes, on average, differ by 1.6 and −0.9 Wm−2, respectively, and is contrary to physical theory. The global averaged SW, LW, and net CRFs form CERES EBAF are −47.2, 26.2, and −21.0 Wm−2, respectively, indicating a net cooling effect due to clouds on the TOA radiation budget. Global biases in the SW and LW CRFs from the multimodel ensemble mean are −1.1 and −1.3 Wm−2, respectively, resulting in a greater net cooling effect of 2.4 Wm−2 in the model simulations. A further investigation of cloud properties and CRFs reveals the GCM biases in atmospheric upwelling (15 °S − 15 °N, ocean-only) regimes are much less than their downwelling (15 ° − 45 °N/S, ocean-only) counterparts. Sensitivity studies have shown that the magnitude of SW cloud radiative cooling increases significantly with increasing CF at similar rates ( −1.20 and −1.31 Wm−2 %−1) in both regimes. The LW cloud radiative warming increases with increasing CF but is regime dependent, demonstrated by the different slopes over the upwelling and downwelling regimes (0.81 and 0.22 Wm %−1, respectively). Through a comprehensive error analysis, we found that CF is a primary modulator of warming (or cooling) in the atmosphere. The comparisons and statistical results from this study may provide helpful insight for improving GCM simulations of clouds and TOA radiation budgets in future versions of CMIP

Land Surface Temperature Trend and Its Drivers in East Africa

Land surface temperature (LST) is affected by surface-atmosphere interaction. Yet, the degree to which surface and atmospheric factors impact the magnitude of LST trend is not well established. Here, we used surface energy balance, boosted regression tree model, and satellite observation and reanalysis data to unravel the effects of surface factors (albedo, sensible heat, latent heat, and ground heat) as well as incoming radiation (shortwave and longwave) on LST trends in East Africa (EA). Our result showed that 11% of EA was affected by significant (p <0.05) daytime annual LST trends, which exhibited both cooling of -0.19 K year(-1) (mainly in South Sudan and Sudan) and warming of 0.22 K year(-1) (mainly in Somalia and Kenya). The nighttime LST trends affected a large part of EA (31%) and were dominated by significant warming trend (0.06 K year(-1)). Influenced by contrasting daytime and nighttime LST trends, the diurnal LST range reduced in 15% of EA. The modeling result showed that latent heat flux (32%), incoming longwave radiation (30%), and shortwave radiation (23%) were stronger in explaining daytime LST trend. The effects of surface factors were stronger in both cooling and warming trends, whereas atmospheric factors had stronger control only on surface cooling trends. These results indicate the differential control of surface and atmospheric factors on warming and cooling trends, highlighting the importance of considering both factors for accurate evaluation of the LST trends in the future.Peer reviewe

Satellite-based estimates of net radiation and modeling the role of topography and vegetation on inter-annual hydro-climatology

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 229-260).The Fourth Assessment Report of the Intergovernmental Panel on Climate Change acknowledged that the lack of relevant observations in various regions of the world is a crucial gap in understanding and modeling impacts of climate change related to hydrologic cycle. The Surface Radiation Budget (SRB) is an important component in the study of land surface processes. Existing SRB retrieval algorithms generally suffer from two major shortcomings: difficulty in dealing with cloudy sky conditions and reliance on study-site specific ancillary ground data. In this work, a framework of estimating net radiation from the MODerateresolution Imaging Spectroradiometer (MODIS) data is presented that is applicable under all-sky conditions, while solely relying on satellite data. The results from the proposed methodology are compared against several ground measurements within the United States for the entire 2006. Finally, monthly radiation maps for the Continental United States are produced. Modeling, similar to observations, is critical to the Earth Sciences and the second part of this work focuses on the impact of incorporating vegetation dynamics and topography in modeling hydro-climatology over large river basins. Land and atmosphere are coupled with each other through the exchange of heat, momentum and water at the boundary. This work involves coupling of a physically-based, fully distributed ecohydrology model with a numerical atmospheric model, using high performance computing. The ability of the ecohydrology model (in an offline mode) to accurately resolve hydro-climatic signatures and vegetation dynamics is first examined. The ecohydrology model is applied in a highly instrumented catchment, Walnut Gulch Experimental Watershed (WGEW) in Arizona, for a period of 11-years (1997-2007). The ecohydrology model is able to capture the behavior of several key hydrologic variables and vegetation dynamics within the WGEW. A series of three synthetic experiments are conducted with a coupled land-atmosphere model. The anomalies of various simulated quantities between the synthetic experiments are examined within the rainfall-soil moisture feedback hypothesis proposed by Elathir [1998]. The results from the experiments highlight the need to explicitly account for vegetation dynamics and topography within a numerical atmospheric model. The thesis concludes with a discussion of contributions, and future directions for this work.by Gautam Bisht.Ph.D

A physics-constrained machine learning method for mapping gapless land surface temperature

More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless LST estimation, which have their respective advantages and disadvantages. In this paper, a physics-constrained ML model, which combines the strengths in the mechanism model and ML model, is proposed to generate gapless LST with physical meanings and high accuracy. The hybrid model employs ML as the primary architecture, under which the input variable physical constraints are incorporated to enhance the interpretability and extrapolation ability of the model. Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model. Physical constraints (PCs) are coupled by further incorporating key Community Land Model (CLM) forcing data (cause) and CLM simulation data (effect) as inputs into the LGBM model. This integration forms the PC-LGBM model, which incorporates surface energy balance (SEB) constraints underlying the data in CLM-LST modeling within a biophysical framework. Compared with a pure physical method and pure ML methods, the PC-LGBM model improves the prediction accuracy and physical interpretability of LST. It also demonstrates a good extrapolation ability for the responses to extreme weather cases, suggesting that the PC-LGBM model enables not only empirical learning from data but also rationally derived from theory. The proposed method represents an innovative way to map accurate and physically interpretable gapless LST, and could provide insights to accelerate knowledge discovery in land surface processes and data mining in geographical parameter estimation

Landsat and local land surface temperatures in a heterogeneous terrain compared to MODIS values

Land Surface Temperature (LST) as provided by remote sensing onboard satellites is a key parameter for a number of applications in Earth System studies, such as numerical modelling or regional estimation of surface energy and water fluxes. In the case of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra or Aqua, pixels have resolutions near 1 km2 , LST values being an average of the real subpixel variability of LST, which can be significant for heterogeneous terrain. Here, we use Landsat 7 LST decametre-scale fields to evaluate the temporal and spatial variability at the kilometre scale and compare the resulting average values to those provided by MODIS for the same observation time, for the very heterogeneous Campus of the University of the Balearic Islands (Mallorca, Western Mediterranean), with an area of about 1 km2 , for a period between 2014 and 2016. Variations of LST between 10 and 20 K are often found at the sub-kilometre scale. In addition, MODIS values are compared to the ground truth for one point in the Campus, as obtained from a four-component net radiometer, and a bias of 3.2 K was found in addition to a Root Mean Square Error (RMSE) of 4.2 K. An indication of a more elaborated local measurement strategy in the Campus is given, using an array of radiometers distributed in the area

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research