3cm) are highly sensitive to changes in near-surface (top 5-10 cm) moisture and have been widely used to retrieve near-surface soil moisture (SM) information. Recent satellite-based sensors at L-band (1.4 GHz) such as the ESA - Soil Moisture Ocean Salinity (SMOS) mission and the upcoming NASA-Soil Moisture Active Passive (SMAP) mission provide unprecedented, global SM product every 2-3 days at spatial resolutions of ~50 km. In addition, the SMAP will provide a SM product at 10 km. The L-band product benefit from decreased sensitivity to the atmosphere and vegetation. The remotely sensed SM represents a very shallow layer in the top soil, and is insufficient, by itself, to provide useful drought information in agricultural terrains. However, it can be used in conjunction with crop growth models in a data assimilation system that merges the SM product with the modeled soil moisture information to provide optimal estimates of RZSM. Moreover, the resolutions of 10-50 km are too coarse for relevance to agricultural drought in heterogeneous landscapes, making downscaling a necessity. The overall goal of this project is to develop a framework that utilizes new SM product from the recent and upcoming missions at 10- 50km with other satellite products to provide RZSM estimates at meaningful spatial scales of 1km for quantifying impacts of agricultural drought on crop yields. computationally-efficient algorithms based upon data descriptors that retain information from higher-order moments are required for optimal downscaling, especially under heterogeneous land surface conditions. Novel transformation methods leveraging physical relationships and recent advances in signal processing techniques can be used to transform information from relevant RS products into SM.' />
Source: UNIVERSITY OF FLORIDA submitted to
REMOTE SENSING OBSERVATIONS FOR MANAGING IMPACTS OF AGRICULTURAL DROUGHT ON CROP YIELDS IN HETEROGENEOUS LANDSCAPES
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
NEW
Funding Source
HATCH
Reporting Frequency
Annual
Accession No.
0232661
Grant No.
(N/A)
Project No.
FLA-ABE-005229
Proposal No.
(N/A)
Multistate No.
(N/A)
Program Code
(N/A)
Project Start Date
Jan 15, 2013
Project End Date
Sep 30, 2017
Grant Year
(N/A)
Project Director
Judge, J.
Recipient Organization
UNIVERSITY OF FLORIDA
G022 MCCARTY HALL
GAINESVILLE,FL 32611
Performing Department
Agricultural and Biological Engineering
Non Technical Summary
Agricultural drought is characterized by reduced soil moisture in the root zone (RZSM). Typically, it follows a meteorological drought characterized by lower than normal/average rainfall. In addition, it is dependent upon agricultural land cover type and the soil's capacity to store water, making the spatial extent and impact variable. Its slow onset results in accumulated affects over a significant period of making it difficult to determine its beginning and end. Such difficulties produce several challenges in monitoring, managing, and adapting to agricultural drought. A moderate drought may result in decrease in crop yield, but severe drought may result in crop failure. Such decreases and failures may result in increased volatility in commodity prices, decreased standard of living, and increased concerns regarding food security. Availability of reliable RZSM estimates is critical at fine spatial resolutions of 1 km and at temporal resolutions of a few days for accurate quantification of drought impacts on crop yields and recommending meaningful management and adaptation strategies. Remote sensing observations, particularly at microwave frequencies < 10 GHz (or wavelengths > 3cm) are highly sensitive to changes in near-surface (top 5-10 cm) moisture and have been widely used to retrieve near-surface soil moisture (SM) information. Recent satellite-based sensors at L-band (1.4 GHz) such as the ESA - Soil Moisture Ocean Salinity (SMOS) mission and the upcoming NASA-Soil Moisture Active Passive (SMAP) mission provide unprecedented, global SM product every 2-3 days at spatial resolutions of ~50 km. In addition, the SMAP will provide a SM product at 10 km. The L-band product benefit from decreased sensitivity to the atmosphere and vegetation. The remotely sensed SM represents a very shallow layer in the top soil, and is insufficient, by itself, to provide useful drought information in agricultural terrains. However, it can be used in conjunction with crop growth models in a data assimilation system that merges the SM product with the modeled soil moisture information to provide optimal estimates of RZSM. Moreover, the resolutions of 10-50 km are too coarse for relevance to agricultural drought in heterogeneous landscapes, making downscaling a necessity. The overall goal of this project is to develop a framework that utilizes new SM product from the recent and upcoming missions at 10- 50km with other satellite products to provide RZSM estimates at meaningful spatial scales of 1km for quantifying impacts of agricultural drought on crop yields. computationally-efficient algorithms based upon data descriptors that retain information from higher-order moments are required for optimal downscaling, especially under heterogeneous land surface conditions. Novel transformation methods leveraging physical relationships and recent advances in signal processing techniques can be used to transform information from relevant RS products into SM.
Animal Health Component
(N/A)
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
1020110205020%
1020210205020%
1110110205010%
1110210205010%
1320110205020%
1320210205020%
Knowledge Area
132 - Weather and Climate; 102 - Soil, Plant, Water, Nutrient Relationships; 111 - Conservation and Efficient Use of Water;

Subject Of Investigation
0210 - Water resources; 0110 - Soil;

Field Of Science
2050 - Hydrology;
Goals / Objectives
The overall goal of this project is to develop a framework that utilizes new soil moisture product from the recent and upcoming satellite missions at 10- 50km with other satellite products to provide root zone estimates at meaningful spatial scales of 1km for quantifying impacts of agricultural drought on crop yields. The primary objectives of this project are (1) to implement an upscaling/downscaling methodology to obtain near-surface soil moisture at 1km (2) to conduct sensitivity studies and quantify errors in soil moisture for simulated data-poor scenarios (3) to obtain root zone soil moisture and crop yields at 1km through data assimilation.
Project Methods
The downscaling methodology will be evaluated using a synthetic experiment developed recently by the PI. The experiment uses coupled hydrology-crop growth models linked with microwave algorithms to generate land surface temperature (LST), leaf area index (LAI), crop yield, SM, and RZSM for two growing seasons of corn and cotton at different spatial scales. Such datasets are excellent to test data-driven methodologies, independent of model physics. The methodology will be implemented in three predominantly agricultural regions in the lower La Plata Basin in South America. The project will use remote sensing (RS) and in situ observations spanning two growing seasons from 1/10 to 4/12, during which ESA-SMOS observations are available to demonstrate the feasibility of the proposed approach. The downscaling methodology is based upon theoretic learning principles. Its implementation has two steps. First, a transformation function is created at fine resolution using in situ and fine-scale remotely sensed observations to probabilistically relate soil moisture to land surface and meteorological conditions. This provides and initial estimate of soil moisture at fine resolution. Second, the coarse scale satellite observations are merged with the initial estimate from step 1 using PRI to obtain an improved estimate of soil moisture. This remotely sensed soil moisture is assimilated using ensemble Kalman Filtering technique into a crop model to provide root zone soil moisture and crop yield.

Progress 10/01/15 to 09/30/16

Outputs
Target Audience:Research and teaching communities from remote sensing, water resources, food and agriculture, big data and computer sciences Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Undergraduate, graduate student, and postdoc training. Professional development during SMAPVEX16-MicroWEX and conferences. How have the results been disseminated to communities of interest?Presentations and publications at professional conferences and refereed and non-refereed publications. What do you plan to do during the next reporting period to accomplish the goals?The next step is to implement the methodology on a larger area - such as the rainfed agricultural regions in the US

Impacts
What was accomplished under these goals? The clustering algorithm downscaling developed in 2015 was implemented with temporal correlations to reduce the amount of data required. In addition, assimilation algorithms were developed and published that utilize downscaled observations. During summer 2016, we participated in the community-wide Soil moisture active passive-Validation experiment (SMAPVEX-16). During the experiment UF ground-based sensors were deployed in Iowa for three months to conduct the Microwave Water and Energy Balance Experiment (SMAPVEX16-MicroWEX). Observations of microwave signatures of corn and soybean along with those of soil and crop conditions provide a rich dataset to understand the spatial and temporal scaling from satellite to field scales.

Publications

  • Type: Conference Papers and Presentations Status: Other Year Published: 2016 Citation: Liu, P.W., M.P. Clarizia, J. Judge, A. Camps, C. Ruf, and T. Bongiovanni, "Satellite-based GNSS-R observations from TDS-1 for soil moisture studies in agricultural vegetation landscapes", Oral presentation at the Agu Fall Meeting, San Francisco, CA, 2015
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Camps, A., H. Park, M. Pablos, G. Foti, C. Gommenginger, P.-W. Liu, and J. Judge, Sensitivity of GNSS-R Spaceborne Observations to Soil Moisture and Vegetation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9, Issue 10, PP. 4730-4742, 2016.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Liu, P.-W., J. Judge, R. DeRoo, A.W. England, and T. Bongiovanni, Uncertainty in Soil Moisture Retrievals Using the SMAP Combined Active-Passive Algorithm for Growing Sweet Corn, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9, Issue 7, PP. 3326-3339, 2016.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Liu, P.-W., J. Judge, R. DeRoo, A.W. England, T. Bongiovanni, and A. Luke, Dominant Backscattering Mechanisms at L-band during Dynamic Soil Moisture Conditions for Sandy Soils, Remote Sensing of Environment, Vol. 178, PP. 104-112, 2016.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Liu, P.-W., T. Bongiovanni, A. Monsivais-Huertero, J. Judge, S. Steele-Dunne, R. Bindlish and T.J. Jackson, Assimilation of Active and Passive Microwave Observations for Improved Estimates of Soil Moisture and Crop Growth, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9, Issue.4, PP. 1357-1369, 2016
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: Monsivais-Huertero, A., J. Judge, S. Steele-Dunne, and P.-W. Liu, Impact of Bias Correction Methods on Estimation of Soil Moisture When Assimilating Active and Passive Microwave Observations, IEEE Transactions of Geoscience and Remote Sensing, Vol. 54, No. 1, PP. 262-278, 2016.
  • Type: Journal Articles Status: Accepted Year Published: 2016 Citation: Steele-Dunne, S., H. McNairn, A. Monsivais-Huertero, J. Judge , P.-W. Liu, and K. Papathanassiou, Radar Remote Sensing of Agricultural Canopies: A Review, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Accepted, 2016.
  • Type: Journal Articles Status: Published Year Published: 2016 Citation: S. Chakrabarti, J. Judge, A. Rangarajan, and S. Ranka. Disaggregation of remotely sensed soil moisture in heterogeneous landscapes using holistic structure based models. IEEE Trans. Geosci. Remote Sensing, 54(3), 2016.
  • Type: Journal Articles Status: Accepted Year Published: 2016 Citation: S. Chakrabarti, J. Judge, , T. Bongiovanni, A. Rangarajan, and S. Ranka. Utilizing self regularized regressive models to downscale microwave brightness temperatures for agricultural land covers in the SMAPVEX-12 region. IEEE J. Sel. Topics Appl. Earth Observ, Accepted, 2016


Progress 10/01/14 to 09/30/15

Outputs
Target Audience:Research communities from computer science, big data, food and agriculture, weather prediction,and water resources management. Changes/Problems:The PRI method was computationally too intensive, required significant training dataset, and was conducted on each pixel. We are investigating novel ways to address these issues. What opportunities for training and professional development has the project provided?Graduate student and postdoc training. Professional development during conferences and other research meetings. How have the results been disseminated to communities of interest?Presentations and publications at the professional conferences; refereed and non-refereed publications. What do you plan to do during the next reporting period to accomplish the goals?Even though the primary objectives have been accomplished, we are now investigating downscaling the brightness temperatures observed by the satellite-based sensors, to avoid errors in soil moisture retrieval.

Impacts
What was accomplished under these goals? Our previous method, PRI was computationally intensive. To reduce computational needs, we clustered the pixels using Cauchy-Schwartz distance approach and downscaled usinga kernel-based regression method instead of downscaling on each pixel. This method also requires less training (for example in data poor regions) than the PRI method.We have successfully downscaled soil moisture to 1km. The preliminary work was presented at the IEEE-IGARSS conference in Milan.

Publications

  • Type: Conference Papers and Presentations Status: Other Year Published: 2015 Citation: Chakrabarti, S., P.W. Liu, T. Bongiovanni, J. Judge, R. Bindlish, A. Rangarajan, S. Ranka, T. Jackson,  A combined hierarchical downscaling and assimilation framework for soil moisture and crop yield from remotely sensed brightness temperatures, Oral presentation at the IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy, 2015.
  • Type: Conference Papers and Presentations Status: Awaiting Publication Year Published: 2014 Citation: Liu, P. W., J. Judge, A. Monsivais-Huertero, S. Steele-Dunne, T. Bongiovanni, R. Bindlish, and T. Jackson, Assimilation of synchronous and asynchronous active/passive microwave observations at different spatial scales for improved soil moisture and crop growth, Oral presentation at the AGU Fall Meeting, San Francisco, CA, 2014.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Chakrabarti, S., T. Bongiovanni, J. Judge, K. Nagarajan, and J. Principe,  Downscaling satellite-based soil moisture in heterogeneous regions using high resolution remote sensing products and information theory, IEEE Trans. Geosci and Remote Sens, 53(1), pp 85-101, 2015.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Emmerik, T. V., S. Steele-Dunne, J. Judge, and N. V. Giesen,  Impact of diurnal variation in vegetation water content on radar backscatter from maize during water stress, IEEE Trans. Geosci and Remote Sensn, 53(7), pp 3855-3869, 2015.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2015 Citation: Chakrabarti, S., J. Judge, A. Rangarajan, and S. Ranka, Downscaling microwave brightness temperatures using self regularized regressive models, In Proceedings of IEEE International Geoscience and Remote Sensing Symposium, pp 210-214, 2015.
  • Type: Other Status: Published Year Published: 2015 Citation: Bongiovanni TE, P.W. Liu, K. Nagarajan, D. Preston, P. Rush, T. H.M. van Emmerik, R. Terwillegeru, A. Monsivais-Huerterop, J. Judge, S. Steele-Dunne, R. DeRoo, R. Akbar, E. Baar, K.M. Wallace, and A.W. England. Field observations during the eleventh Microwave, Water and Energy Balance Experiment (MicroWEX-11): from April 25 through December 6, 2012, UF/IFAS, EDIS Web site, http://edis.ifas.ufl.edu/AE514, 2015.
  • Type: Other Status: Published Year Published: 2015 Citation: Bongiovanni TE, P.W. Liu, K. Nagarajan, D. Preston, P. Rush, X. Duan, G. Chen, R. Terwilleger, A. Monsivais-Huertero, J. Judge, R. DeRoo, R. Akbar, M. Morris, O. Williams, L. Marksu, C. Cardozou, M. Moghaddam, and A.W. England. Field observations during the tenth Microwave, Water and Energy Balance Experiment (MicroWEX-10): from March 1, 2011 through January 2, 2012, UF/IFAS, EDIS Web site, http://edis.ifas.ufl.edu/AE512, 2015.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2015 Citation: Judge, J., Integrating RS observations in crop models: Downscaling and data assimilation, Joint GEOGLAM/AgMIP workshop, Maryland, USA, 2015.


Progress 10/01/13 to 09/30/14

Outputs
Target Audience:Potential collaborators from Cargill and INPE in Brazil; peers in the research community Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Graduate student and postdoctoral training; professional meetings and development at conferences. How have the results been disseminated to communities of interest?journal articles; oral and poster presentations at professional meetings What do you plan to do during the next reporting period to accomplish the goals?The PRI-based method is computationally intensive and require significant training datasetsto be operational. New machine learning algorithms need to be tested.

Impacts
What was accomplished under these goals? The Principle of Relevant Information (PRI) -based downscaling method was implemented in the LaPlata region in Brazil in a 400KmX 400km agricultural area. The downscaled soil moisture was assimilated into a crop growth model to improve the crop yield estimates. The improved estimates were much closer to the observed statistics in the region.

Publications

  • Type: Conference Papers and Presentations Status: Other Year Published: 2014 Citation: Liu, P. W., A. Monsivais-Huertero, R. DeRoo, A. England, J. Judge, T. Bongiovanni, Impacts of soil moisture distribution and vegetation parameters on active and passive signatures at L-band for bare soil and growing corn, IEEE International Geoscience and Remote Sensing Symposium, Quebec City, Canada, 2014.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2014 Citation: Liu, P. W., T. Bongiovanni A. Monsivais-Huertero, J. Judge, S. Steele-Dunne, R. Bindlish, T. Jackson, Integration of microwave observations from the SMOS and Aquarius with crop model for data assimilation in agricultural region, IEEE International Geoscience and Remote Sensing Symposium, Quebec City, Canada, 2014
  • Type: Conference Papers and Presentations Status: Other Year Published: 2013 Citation: Liu, PW, T. Bongiovanni, A. Monsivais-Huertero, R. Bindlish, and J. Judge, Impacts of different assimilation methodologies on crop yield using active and passive microwave data at L-band, AGU Fall Meeting, San Francisco, CA, 2013
  • Type: Conference Papers and Presentations Status: Other Year Published: 2013 Citation: Chakrabarti, S., T. Bongiovanni, J. Judge, and JC Principe, and C. Fraisse, Assimilation of downscaled SMOS soil moisture for quantifying drought impacts on crop yield in agricultural regions in Brazil, AGU Fall Meeting, San Francisco, CA, 2013.
  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Nagarajan, K., PW Liu, R. DeRoo, J. Judge, R. Akbar, P. Rush, S. Feagle, D. Preston, and R. Terwilleger, Automated L-band radar system for sensing soil moisture at high temporal resolutions, IEEE Geosci and Remote Sensing Letters, 11(2), 2014.
  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Chakrabarti, S., T. Bongiovanni, J. Judge, L. Zotarelli, and C. Bayer, Assimilation of SMOS soil moisture for quantifying drought impacts on crop yield in agricultural regions, IEEE J. Selected Topics in Applied Earth Obs. and Remote Sensing, 7(9), pp 3867-3879, 2014


Progress 01/15/13 to 09/30/13

Outputs
Target Audience: Nothing Reported Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Graduate student training, and presentations in conferences. How have the results been disseminated to communities of interest?Conference presentations and proceedings; journal articles What do you plan to do during the next reporting period to accomplish the goals?Validate the PRI method using the synthetic dataset and implement it in the LaPlata Basin in Brazil.

Impacts
What was accomplished under these goals? During the report period 1/13- 9/13, a comprehensive dataset was generated to allow validation of the scaling algorithm; a Principle of Relevant Information (PRI)-based downscaling algorithm was developed to downscale soil moisture from 25km to 1km.

Publications

  • Type: Journal Articles Status: Published Year Published: 2013 Citation: Nagarajan, K. and J. Judge,  Spatial scaling and variability of soil moisture over heterogeneous land covers and dynamic vegetation conditions, IEEE Geosci. Remote Sens., 10(4), pp 880-884, 2013
  • Type: Conference Papers and Presentations Status: Other Year Published: 2013 Citation: Monsivais-Huertero, A., J. Judge, and PW Liug, A comparison of bias correction methods to improve soil moisture estimates when assimilating microwave active/passive observations, IEEE International Geoscience and Remote Sensing Symposium, Melbourne, Australia, 2013.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2013 Citation: Monsivais-Huertero, A., PW Liug, and J. Judge, Comparison of coherent and incoherent L-band radar signatures with field observations for growing corn, IEEE International Geoscience and Remote Sensing Symposium, Melbourne, Australia, 2013.
  • Type: Journal Articles Status: Published Year Published: 2013 Citation: Liu, P-W, RD DeRoo, AW England, and J. Judge, Impact of moisture distribution within the sensing depth on L- and C-band emission in sandy soils, IEEE J. Selected Topics in Applied Earth Obs. and Remote Sensing, 6(2) 887-899, 2013.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2013 Citation: Liu, P.W., J. Judge, R.DeRoo, A.W. England, and A. Luke, Utilizing complementarity of active/passive microwave observations at L-band for soil moisture studies in sandy soils, In Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 4, pp 743-746, 2013.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2013 Citation: Judge J., T. Bongiovanni, S. Chakrabarti, and J. Principe, Using ITL concepts to downscale SMOS soil moisture for agricultural applications, IEEE International Geoscience and Remote Sensing Symposium, Melbourne, Australia, 2013
  • Type: Conference Papers and Presentations Status: Other Year Published: 2013 Citation: Liu, P.W., Monsivais-Huertero, and J. Judge, Assimilation of satellite-based active and passive microwave observations for agricultural fields, IEEE International Geoscience and Remote Sensing Symposium, Melbourne, Australia, 2013