Utilizing Collocated Crop Growth Model Simulations to Train Agronomic Satellite Retrieval Algorithms

Due to its worldwide coverage and high revisit time, satellite-based remote sensing provides the ability to monitor in-season crop state variables and yields globally. In this study, we presented a novel approach to training agronomic satellite retrieval algorithms by utilizing collocated crop growth model simulations and solar-reflective satellite measurements. Specifically, we showed that bidirectional long short-term memory networks (BLSTMs) can be trained to predict the in-season state variables and yields of Agricultural Production Systems sIMulator (APSIM) maize crop growth model simulations from collocated Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m satellite measurements over the United States Corn Belt at a regional scale. We evaluated the performance of the BLSTMs through both k-fold cross validation and comparison to regional scale ground-truth yields and phenology. Using k-fold cross validation, we showed that three distinct in-season maize state variables (leaf area index, aboveground biomass, and specific leaf area) can be retrieved with cross-validated R2 values ranging from 0.4 to 0.8 for significant portions of the season. Several other plant, soil, and phenological in-season state variables were also evaluated in the study for their retrievability via k-fold cross validation. In addition, by comparing to survey-based United State Department of Agriculture (USDA) ground truth data, we showed that the BLSTMs are able to predict actual county-level yields with R2 values between 0.45 and 0.6 and actual state-level phenological dates (emergence, silking, and maturity) with R2 values between 0.75 and 0.85. We believe that a potential application of this methodology is to develop satellite products to monitor in-season field-scale crop growth on a global scale by reproducing the methodology with field-scale crop growth model simulations (utilizing farmer recorded field-scale agromanagement data) and collocated high-resolution satellite data (fused with moderate-resolution satellite data)

Improving Retrievals of Crop Vegetation Parameters from Remote Sensing Data

Agricultural systems are difficult to model because crop growth is driven by the strongly nonlinear interaction of Genotype x Environment x Management (G x E x M) factors. Due to the nonlinearity in the interaction of these factors, the amount of data necessary to develop and utilize models to accurately predict the performance of agricultural systems at an operational scale is large. Satellite remote sensing provides the potential to vastly increase the amount of data available for modelling agricultural systems as a result of its high revisit time and spatial coverage. Unfortunately, there have been significant difficulties in deploying remote sensing for many agricultural modelling applications because of the uncertainty involved in the retrievals. In this dissertation, we show that collecting farmer-provided agro-managment information has the potential to reduce the uncertainty in the retrieval products obtained from remote sensing observations. Specifically, both field-scale and regional-scale analysis are used to show that secondary factor variability is a very significant cause of uncertainty in both crop growth modelling and agricultural remote sensing that needs to be addressed through increased data collection. In order to address this need for increased data availability, a method is developed that allows geolocated crop growth model simulations to be used to train satellite-based crop state variable retrievals, which is then validated at regional scale. The method developed provides a general robust methodology to create a large-scale platform that would allow farmers to share data with government agencies and universities to improve crop state variable retrievals and crop growth modelling and provide farmers, government, industry, and researchers with insights and predictive capability into crop growth at both field and regional scales

Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models

Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G X E X M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it diffcult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use effciency (LUECanopy). The simulations are used to both explore the sensitivity of the satellite-estimated genotype X management (G X M) parameters to the satellite retrieval regression coeffcients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUECanopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coeffcients lead to large uncertainty in the G X Mparameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coeffcient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUECanopy. The weekly change in LAI is shown to be retrievable with a correlation coeffcient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models

Assessing Normalized Difference Vegetation Index (NDVI) data to estimate winter wheat yields and analyze winter wheat by homogeneous subregions at field scale in Kansas.

Doctor of PhilosophyDepartment of GeographyMarcellus M CaldasWheat (Triticum aestivum L.) is the 4th largest staple crop produced worldwide. While global demand has increased over the last 15 years, the rate of increase of global cereal production has slowed or stagnated. Accurate information about crop production is key for local-scale research, farmers, and decision-making evaluation due to the typically high spatial variability in agricultural production, especially in environmentally heterogeneous high-producing regions. The main goal of this dissertation was to investigate the potential of satellite imagery in predicting winter wheat yields and analyze winter wheat yields by homogeneous subregions at field scale in Kansas, the largest producer of winter wheat in the U.S. The first chapter examined the performance of different satellite sensors (from coarse to moderate resolution - MODIS, Landsat, and Sentinel) in predicting winter wheat yields. The following chapters analyze the winter wheat yield prediction using environmentally distinct subregions regarding weather and management practices and multisource data (NDVI, weather, and climate). Linear Regression and a robust machine learning model, (i.e., Random Forest) were applied to predict winter wheat yields. The results, using NDVI predictor variables, were not enough to explain field-scale winter wheat yield variability across much of Kansas, where Landsat USGS achieved the lowest prediction error among all sensors (RMSE = 0.95 Mg ha-1). The results proved to be more accurate when using Landsat NDVI variables to predict winter wheat yields in more homogeneous subregions (NC, SC, and West), with the best prediction in NC (RMSE = 0.76 Mg ha-1). NC, SC, and West Kansas achieved the best results when including weather and management variables along with NDVI (RMSE of 0.59 Mg ha-1 , 0.66 Mg ha-1, and 0.69 Mg ha-1in NC, SC, and West), and outperformed the prediction when using all fields-yields across Kansas ( RMSE=0.78 Mg ha-1). The prediction model showed that it is possible to predict yield in early crop developmental stages; however, after adding weather and management variables, NDVI predictor variables in the late stages of the growing season were the most important for winter wheat yield prediction. NDVI was more significant in predicting winter wheat yields in NC and West than in SC Kansas. NC showed management of fertilizers ( N, P, Cl) as good yield predictors and could be used along with NDVI to estimate yields. SC and West predictor variables relied more on variables related to environmental conditions or management practices related to environmental conditions, such as fungicide application, soil water storage, and sowing date. Overall, this research demonstrates that the applicability of empirical winter wheat yield modeling using NDVI predictor variables in Kansas is environmentally dependent. Lastly, winter wheat yield prediction using satellite imagery at the field scale could be benefited using this subregional scheme in Kansas

Earth Survey Applications Division: Research leading to the effective use of space technology in applications relating to the Earth's surface and interior

There are no author-identified significant results in this report

Earth resources: A continuing bibliography with indexes, issue 50

This bibliography lists 523 reports, articles and other documents introduced into the NASA scientific and technical information system between April 1 and June 30, 1986. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

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

Crop models and their use in assessing crop production and food security: A review

Agriculture is directly related to food security as it determines the global food supply. Research in agriculture to predict crop productivity and losses helps avoid high food demand with little supply and price spikes. Here, we review ten crop models and one intercomparison project used for simulating crop growth and productivity under various impacts from soil–crop–atmosphere interactions. The review outlines food security and production assessments using numerical models for maize, wheat, and rice production. A summary of reviewed studies shows the following: (1) model ensembles provide smaller modeling errors compared to single models, (2) single models show better results when coupled with other types of models, (3) the ten reviewed crop models had improvements over the years and can accurately predict crop growth and yield for most of the locations, management conditions, and genotypes tested, (4) APSIM and DSSAT are fast and reliable in assessing broader output variables, (5) AquaCrop is indicated to investigate water footprint, quality and use efficiency in rainfed and irrigated systems, (6) all models assess nitrogen dynamics and use efficiency efficiently, excluding AquaCrop and WOFOST, (7) JULES specifies in evaluating food security vulnerability, (8) ORYZA is the main crop model used to evaluate paddy rice production, (9) grain filling is usually assessed with APSIM, DAISY, and DSSAT, and (10) the ten crop models can be used as tools to evaluate food production, availability, and security