John D. Bolten

Evaluating the Utility of Remotely Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring

Soil moisture is a fundamental data source used by the United States Department of Agriculture (USDA) International Production Assessment Division (IPAD) to monitor crop growth stage and condition and subsequently, globally forecast agricultural yields. Currently, the USDA IPAD estimates surface and root-zone soil moisture using a two-layer modified Palmer soil moisture model forced by global precipitation and temperature measurements. However, this approach suffers from well-known errors arising from uncertainty in model forcing data and highly simplified model physics. Here, we attempt to correct for these errors by designing and applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA modified Palmer soil moisture model. An assessment of soil moisture analysis products produced from this assimilation has been completed for a five-year (2002 to 2007) period over the North American continent between 23° N-50° N and 128° W-65° W. In particular, a data denial experimental approach is utilized to isolate the added utility of integrating remotely sensed soil moisture by comparing EnKF soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline Palmer model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.

Variation of hydrometeorological conditions along a topographic transect in Northwestern Mexico during the North American monsoon

Abstract Relatively little is currently known about the spatiotemporal variability of land surface conditions during the North American monsoon, in particular for regions of complex topography. As a result, the role played by land–atmosphere interactions in generating convective rainfall over steep terrain and sustaining monsoon conditions is still poorly understood. In this study, the variation of hydrometeorological conditions along a large-scale topographic transect in northwestern Mexico is described. The transect field experiment consisted of daily sampling at 30 sites selected to represent variations in elevation and ecosystem distribution. Simultaneous soil and atmospheric variables were measured during a 2-week period in early August 2004. Transect observations were supplemented by a network of continuous sampling sites used to analyze the regional hydrometeorological conditions prior to and during the field experiment. Results reveal the strong control exerted by topography on the spatial and tem...

Soil moisture retrieval using the passive/active L- and S-band radar/radiometer

In the present study, remote sensing of soil moisture is carried out using the Passive and Active L- and S-band airborne sensor (PALS). The data in this paper were taken from five days of overflights near Chickasha, OK during the 1999 Southern Great Plains (SGP99) experiment. Presently, we analyze the collected data to understand the relationships between the observed signals (radiometer brightness temperature and radar backscatter) and surface parameters (surface soil moisture, temperature, vegetation water content, and roughness). In addition, a radiative transfer model and two radar backscatter models are used to simulate the PALS observations. An integration of observations, regression retrievals, and forward modeling is used to derive the best estimates of soil moisture under varying surface conditions.

Biomass Burning, Land-Cover Change, and the Hydrological Cycle in Northern Sub-Saharan Africa

The Northern Sub-Saharan African (NSSA) region, which accounts for 20%-25%of the global carbon emissions from biomass burning, also suffers from frequent drought episodes and other disruptions to the hydrological cycle whose adverse societal impacts have been widely reported during the last several decades. This paper presents a conceptual framework of the NSSA regional climate system components that may be linked to biomass burning, as well as detailed analyses of a variety of satellite data for 2001-2014 in conjunction with relevant model-assimilated variables. Satellite fire detections in NSSA show that the vast majority (greater than 75%) occurs in the savanna and woody savanna land-cover types. Starting in the 2006-2007 burning season through the end of the analyzed data in 2014, peak burning activity showed a net decrease of 2-7% /yr in different parts of NSSA, especially in the savanna regions. However, fire distribution shows appreciable coincidence with land-cover change. Although there is variable mutual exchange of different land cover types, during 2003-2013, cropland increased at an estimated rate of 0.28% /yr of the total NSSA land area, with most of it (0.18% /yr) coming from savanna.During the last decade, conversion to croplands increased in some areas classified as forests and wetlands, posing a threat to these vital and vulnerable ecosystems. Seasonal peak burning is anti-correlated with annual water-cycle indicators such as precipitation, soil moisture, vegetation greenness, and evapotranspiration, except in humid West Africa (5 deg-10 deg latitude),where this anti-correlation occurs exclusively in the dry season and burning virtually stops when monthly mean precipitation reaches 4 mm/d. These results provide observational evidence of changes in land-cover and hydrological variables that are consistent with feedbacks from biomass burning in NSSA, and encourage more synergistic modeling and observational studies that can elaborate this feedback mechanism.

Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring

AbstractDespite considerable interest in the application of land surface data assimilation systems (LDASs) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model, and a sequential data assimilation filter) against a series of linear models that perform the same function (i.e., have the same basic input/output structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moisture/NDVI correlations obtained using individual LDAS components versus their linear analo...

Flood mapping in the lower Mekong River Basin using daily MODIS observations

ABSTRACT In flat homogenous terrain such as in Cambodia and Vietnam, the monsoon season brings significant and consistent flooding between May and November. To monitor flooding in the Lower Mekong region, the near real-time NASA Flood Extent Product (NASA-FEP) was developed using seasonal normalized difference vegetation index (NDVI) differences from the 250 m resolution Moderate Resolution Imaging Spectroradiometer (MODIS) sensor compared to daily observations. The use of a percentage change interval classification relating to various stages of flooding reduces might be confusing to viewers or potential users, and therefore reducing the product usage. To increase the product usability through simplification, the classification intervals were compared with other commonly used change detection schemes to identify the change classification scheme that best delineates flooded areas. The percentage change method used in the NASA-FEP proved to be helpful in delineating flood boundaries compared to other change detection methods. The results of the accuracy assessments indicate that the −75% NDVI change interval can be reclassified to a descriptive ‘flood’ classification. A binary system was used to simplify the interpretation of the NASA-FEP by removing extraneous information from lower interval change classes.

Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the U.S.

This paper presents an intercomparative study of 12 operationally produced large-scale datasets describing soil moisture, evapotranspiration (ET), and/or vegetation characteristics within agricultural regions of the contiguous United States (CONUS). These datasets have been developed using a variety of techniques, including, hydrologic modeling, satellite-based retrievals, data assimilation, and survey/in-field data collection. The objectives are to assess the relative utility of each dataset for monitoring crop yield variability, to quantitatively assess their capacity for predicting end-of-season corn and soybean yields, and to examine the evolution of the yield-index correlations during the growing season. This analysis is unique both with regards to the number and variety of examined yield predictor datasets and the detailed assessment of the water availability timing on the end-of-season crop production during the growing season. Correlation results indicate that over CONUS, at state-level soil moisture and ET indices can provide better information for forecasting corn and soybean yields than vegetation-based indices such as normalized difference vegetation index. The strength of correlation with corn and soybean yields strongly depends on the interannual variability in yield measured at a given location. In this case study, some of the remotely derived datasets examined provide skill comparable to that of in-situ field survey-based data—further demonstrating the utility of these remote sensing-based approaches for estimating crop yield.

Microwave remote sensing: a perspective from the last few field experiments

There have been numerous field experiments which have tested the effectiveness of microwave remote sensing, both active and passive under varied land surface conditions. The Southern Great Plains Experiment 1999 (SGP99) was held in Chickasha Oklahoma where winter wheat and rangeland was the predominant land surface type whereas at the Soil Moisture Experiment 2002 (SMEX02) in Walnut River watershed in Ames Iowa it was a mixture of corn, and soyabeans. In the SMEX03 (Soil Moisture Experiment in 2003) in Little River Watershed, the land surface was a mixture of peanuts, vegetables, cotton and pasture and for SMEX04 (Soil Moisture Experiment in 2004) in Walnut Gulch, Arizona, the land surface cover is primarily brush and grass covered rangeland vegetation. Given that microwaves have low sensitivity to soil moisture in the presence of vegetation, these field experiments offer an opportunity to examine observations of sensitivity in the presence of varied (and varying with time) vegetation densities. In addition, in each of these experiments, there were different instruments on aircrafts and satellite sensors that were deployed. In SGP99, we had observations from the PALS (Passive Active L and S band Radar and Radiometer), PSR (Polarimetric Scanning Radiometer) from the C130 aircraft and the TMI (TRMM Microwave Imager) and SSM/I (Special Sensor Microwave Imager) from space. In SMEX02, we had PALS, PSR, AIRSAR (Airborne Synthetic Aperture Radar) from the aircraft platforms and in SMEX03 PSR only. Satellite sensors in SMEX02 and SMEX03 included AMSR (Advanced Microwave Scanning Radiometer), TMI, and SSM/I. In the recently concluded SMEX04, PSR was used from the aircraft and AMSR, TMI and SSM/I satellite observations were available. We will use these sensors and observations in the microwave channel in conjunction with ground observations of vegetation characteristics and soil moisture to study the sensitivity of microwaves to soil moisture under varied land surface conditions.

A MODIS-based automated flood monitoring system for southeast asia

Abstract Flood disasters in Southeast Asia result in significant loss of life and economic damage. Remote sensing information systems designed to spatially and temporally monitor floods can help governments and international agencies formulate effective disaster response strategies during a flood and ultimately alleviate impacts to population, infrastructure, and agriculture. Recent destructive flood events in the Lower Mekong River Basin occurred in 2000, 2011, 2013, and 2016 ( http://ffw.mrcmekong.org/historical_rec.htm , April 24, 2017). The large spatial distribution of flooded areas and lack of proper gauge data in the region makes accurate monitoring and assessment of impacts of floods difficult. Here, we discuss the utility of applying satellite-based Earth observations for improving flood inundation monitoring over the flood-prone Lower Mekong River Basin. We present a methodology for determining near real-time surface water extent associated with current and historic flood events by training surface water classifiers from 8-day, 250-m Moderate-resolution Imaging Spectroradiometer (MODIS) data spanning the length of the MODIS satellite record. The Normalized Difference Vegetation Index (NDVI) signature of permanent water bodies (MOD44W; Carroll et al., 2009 ) is used to train surface water classifiers which are applied to a time period of interest. From this, an operational nowcast flood detection component is produced using twice daily imagery acquired at 3-h latency which performs image compositing routines to minimize cloud cover. Case studies and accuracy assessments against radar-based observations for historic flood events are presented. The customizable system has been transferred to regional organizations and near real-time derived surface water products are made available through a web interface platform. Results highlight the potential of near real-time observation and impact assessment systems to serve as effective decision support tools for governments, international agencies, and disaster responders.

Optical and Physical Methods for Mapping Flooding with Satellite Imagery

Flood and surface water mapping is becoming increasingly necessary, as extreme flooding events worldwide can damage crop yields and contributing to billions of dollars economic damages as well as social effects including fatalities and destroyed communities. Utilizing earth observing satellite data to map standing water from space is indispensable to flood mapping for disaster response, mitigation, prevention and warning. Researchers have demonstrated countless methods and modifications of those methods to help increase knowledge of areas at risk and areas that are flooded using remote sensing data. This chapter will review methods for mapping floods and open water using spectral formulas and statistical methods commenting on false color composite techniques with optical data, physical models using radar and ancillary data. Methods will be demonstrated over the Lower Mekong Basin to demonstrate visual impacts of the differences over the same study area. The increase in the quantity and variety of flood mapping techniques using satellite data has allowed broader and less-technical audiences to be able to benefit from flood products and may help to mitigate pervasive economic and social damages caused by flooding.