Global Crop Water Productivity and Savings through waterSMART (GCWP) Active
The waterSMART (Sustain and Manage America’s Resources for Tomorrow) project places technical information and tools in the hands of stakeholders that allow them to answer pertinent questions regarding water availability. Two goals of waterSMART are to 1) establish water availability and its use based on an understanding of the past and present water users and to 2) project water availability and use scenarios into the future taking into consideration climate variability and change. Given that worldwide about 90% of all human water use goes toward producing food through agriculture, the focus of this component of the waterSMART project will be to establish crop water productivity (“crop per drop”) of the world’s leading agricultural crops (e.g., wheat, rice, barley, corn, soybeans, cotton, potatoes, pulses, alfalfa) and to determine how much water can be saved by improving crop water productivity. Initially, our primary focus study area is the United States of America (USA). Subsequently, we plan to expand this globally once the methods and approaches mature. The approach and methods involve multi-sensor remote sensing data utilization using machi
Statement of Problem
waterSMART intends to place technical information and tools in the hands of stakeholders, allowing them to answer two primary questions about water availability:
- Does the nation have enough freshwater to meet both human and ecological needs?
- Will this water be present to meet needs in the future?
It is important to note the connectivity between the waterSMART and the National Water Census. WaterSMART is a Department of the Interior (DOI) initiative on water conservation. It includes activities in: Bureau of Reclamation, United States Geological Survey, and the Office of the Assistant Secretary for Water and Science. The National Water Census is an integral part of the U.S. Geological Survey’s Science Strategy to conduct an ongoing assessment of the nation’s water resources. The project envisions expanding to other countries of the world, especially to areas of the world where agriculture is a major activity and tied to water and food security of the country, region, and the world. This expansion will depend on opportunities and resource availability.
Since nearly 80-90% of all human water use globally is consumed by agricultural croplands to produce food, it is of great importance to understand, model, map, and monitor agricultural croplands and their water use. In California, 80% of all water used by humans is for irrigated croplands; about 250 different crops are grown on about 10 million acres, using 41,922 million cubic meters of water of the 53,019 million cubic meters diverted from surface waters or pumped from groundwater (California Department of Water Resources). There are three significant advances envisaged in this project:
- Mapping cropland fallows is very crucial to overcome the uncertainty that currently exists in crop water use assessments. The proportion of croplands left fallow will have a significant role in determining the quantum of water used. Hence there is a critical need in developing accurate map of cropland fallows.
- The need to explore new methods to produce cropland mapping (croplands as well as cropland fallows) by automated machine learning algorithms and cloud computing to bring in both speed and accuracy.
- Understanding, modeling, mapping, and monitoring agricultural crop water productivity (or “crop per drop”) (Figure 1). There is increasing pressure to reduce agricultural water use by producing more food from existing or even reduced:
(a) areas of croplands (more crop per unit area); and
(b) quantities of water (more crop per unit of water).
This increased food production per unit of water or increasing water productivity (or “more crop per drop”; kg/m3) is expected to lead to a “blue revolution” in agriculture making a major contribution to global food security in the twenty-first century. In this project we will study the current and the past water consuming patterns of the crops by modeling and mapping crop water productivity in California’s Central Valley and other places in the United States. This will allow us to quantify historical and current water use by crops in study areas. Water savings in the study areas can then be determined by understanding and establishing:
1. Percentage of cropland areas under medium or low crop water productivity.
2. Quantum of water used by each crop for each level of water productivity.
Subsequently, the water that could be saved will be modeled for different scenarios, including:
1. Increasing crop water productivity,
2. Growing less water consuming crops (e.g., wheat in place of rice) in some areas.
3. Growing second season crops with low water consuming, short season crops that still provide rich nutrition and economic value (e.g., lentil, chickpea, fava bean, in place of certain proportion of rice fields).
Such approaches can potentially save massive quantities of water that could be used to replenish existing surface and ground water reservoirs and/or help create new “water banks”. We will study these separately for rainfed and irrigated croplands. Growing fewer water consuming crops and/or increasing water productivity in rainfed croplands will help recharge groundwater and/or fill existing or new small ponds or reservoirs throughout the cropland areas. These are “new green water banks” (water saved from rainfed croplands). It may also help retain water in the reservoirs which can then be used for alternative uses. These are termed “new blue water banks” (water saved from irrigated croplands).
Novel Objectives
Given the above context, there are several novel objectives that will be achieved by this waterSMART project for 2001-2025 time-period using multisensory remote sensing high resolution imagery (30 m or better), machine learning, and cloud computing. These objectives are:
- Establish automated cropland algorithm's (ACA's) for Conterminous United States (CONUS).
- Develop automated cropland fallow algorithm (ACFA) for CONUS.
- Develop cropland versus cropland fallow products for the CONUS using Landsat, Sentinel, and MODIS satellite sensor data, cloud computing, and machine learning. This will be a unique contribution hitherto not produced by others;
- Conduct crop water productivity (“crop per drop”) research by taking major world crops (e.g., wheat, rice, corn, soybeans, cotton) in irrigated and rainfed cropland areas of the US using multi-sensor remote sensing, cloud computing, and machine learning.
These models and products by this research team are intended to compliment the CONUS products from USDA and other USGS groups. These studies will be conducted considering the factors such as the global irrigated and rainfed croplands (Figure 2), and Koppen-Geiger climate classification (Figure 3) and taking major world crops such as wheat, rice, barley, corn, soybeans, cotton, potatoes, sugarcane, pulses, alfalfa, etc. into consideration.
Outputs, Web Access, and Dissemination
We will perform a comprehensive assessment of the state-of-art of crop water productivity (CWP; “crop per drop”) research worldwide using remote sensing and non-remote sensing methods and approaches based on existing research and meta-analysis. A peer-reviewed journal article will be published based on this work. Then, the study will develop and publish three unique models for CONUS:
- Automated cropland fallow algorithm (ACFA)
- Automated cropland algorithm (ACA)
- Crop water productivity models (CWPM’s)
Nominal 30 m products for 2001-2025 will then be generated for CONUS using ACFA, ACA, and CWPM’s. Next, the study will “pin-point” cropland areas with low and high CWP. These maps and models will provide precise locations from where we can save water and by how much. Spatial representations will pinpoint “hotspots” across the regions where water availability will be severely limited in the future. Scenarios will then be developed that determine how much water (and where) can be saved via improved CWP, and/or the planting of water saving, short duration crops. The scenario outcomes would identify where "new water" would be generated and/or the opportunities for replenishment of existing water resources- both surface and ground water. These scenario analyses will be conducted separately for rainfed and irrigated crops to establish “green water” savings (from rainfed croplands), and “blue water” savings (from rainfed croplands). We will nominate several new or existing reservoirs for this new water as well as establish where and how much of this will be below ground. The research will analyze potential reductions in applied water, which allow farmers and water agencies to remove less water from streams, improving stream quality and ecosystem health, while reducing pumping, delivery, and treatment costs. We will show, through improved CWP in irrigated croplands, various quanta of “new water” that becomes available for alternative uses like urban, industrial, riparian restoration, and re-forestation. Once CWP maps are produced at different resolutions for the representative areas and extrapolated to larger areas using the best models, we will build spatial models for each of the 3 WP study river basins in GEE cloud that will simulate “new water” saved through various scenarios such as improved WP and re-allocation of crops (e.g., growing wheat instead of rice). Users will be able to view and query, compare scenario maps, and generate customized maps from the website. All data and products will be made available through USGS global croplands data portal (e.g., www.croplands.org; LP DAAC). Finally, scenario analysis will allow us to compare existing water use based on existing normal water productivity of various existing crops. This will allow us to suggest alternative pathways.
Impact and Outcomes
This project is targeted to demonstrate: (a) scientific advances in understanding, modeling, and mapping cropland fallows and crop water productivity (“crop per drop”) through advanced remote sensing data; (b) methodological advances in crop-by-crop water use/ET modeling and automated machine learning algorithms for cropland fallows, crop water use, and crop water productivity in cloud computing; (c) societal benefits made by clear demonstration of opportunities for water savings by modeling, mapping, and pin-pointing areas of low and high water productivity and thus contributing to food security. It should help in “understanding variability of agricultural water use and characterizing short and long-term imbalances between agricultural water supplies and agricultural water requirements.”
Below are other science projects associated with this project.
Global Food-and-Water Security-support Analysis Data (GFSAD)
Focus Area Studies
Global Hyperspectral Imaging Spectral-library of Agricultural-Crops & Vegetation (GHISA)
WaterSMART: Improving Tools for Assessing and Forecasting Ecological Responses to Hydrologic Alteration
Below are multimedia items associated with this project.
Below are publications associated with this project.
A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades
Fallow-land Algorithm based on Neighborhood and TemporalAnomalies (FANTA) to map planted versus fallowed croplands usingMODIS data to assist in drought studies leading to water and foodsecurity assessments
Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation)
Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation
Developing in situ non-destructive estimates of crop biomass to address issues of scale in remote sensing
Biomass modeling of four water intensiveleading world crops using hyperspectral narrowbands in support of HyspIRI Mission
Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm
An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data
Below are partners associated with this project.
- Overview
The waterSMART (Sustain and Manage America’s Resources for Tomorrow) project places technical information and tools in the hands of stakeholders that allow them to answer pertinent questions regarding water availability. Two goals of waterSMART are to 1) establish water availability and its use based on an understanding of the past and present water users and to 2) project water availability and use scenarios into the future taking into consideration climate variability and change. Given that worldwide about 90% of all human water use goes toward producing food through agriculture, the focus of this component of the waterSMART project will be to establish crop water productivity (“crop per drop”) of the world’s leading agricultural crops (e.g., wheat, rice, barley, corn, soybeans, cotton, potatoes, pulses, alfalfa) and to determine how much water can be saved by improving crop water productivity. Initially, our primary focus study area is the United States of America (USA). Subsequently, we plan to expand this globally once the methods and approaches mature. The approach and methods involve multi-sensor remote sensing data utilization using machi
Statement of Problem
waterSMART intends to place technical information and tools in the hands of stakeholders, allowing them to answer two primary questions about water availability:
- Does the nation have enough freshwater to meet both human and ecological needs?
- Will this water be present to meet needs in the future?
It is important to note the connectivity between the waterSMART and the National Water Census. WaterSMART is a Department of the Interior (DOI) initiative on water conservation. It includes activities in: Bureau of Reclamation, United States Geological Survey, and the Office of the Assistant Secretary for Water and Science. The National Water Census is an integral part of the U.S. Geological Survey’s Science Strategy to conduct an ongoing assessment of the nation’s water resources. The project envisions expanding to other countries of the world, especially to areas of the world where agriculture is a major activity and tied to water and food security of the country, region, and the world. This expansion will depend on opportunities and resource availability.
Since nearly 80-90% of all human water use globally is consumed by agricultural croplands to produce food, it is of great importance to understand, model, map, and monitor agricultural croplands and their water use. In California, 80% of all water used by humans is for irrigated croplands; about 250 different crops are grown on about 10 million acres, using 41,922 million cubic meters of water of the 53,019 million cubic meters diverted from surface waters or pumped from groundwater (California Department of Water Resources). There are three significant advances envisaged in this project:
- Mapping cropland fallows is very crucial to overcome the uncertainty that currently exists in crop water use assessments. The proportion of croplands left fallow will have a significant role in determining the quantum of water used. Hence there is a critical need in developing accurate map of cropland fallows.
- The need to explore new methods to produce cropland mapping (croplands as well as cropland fallows) by automated machine learning algorithms and cloud computing to bring in both speed and accuracy.
- Understanding, modeling, mapping, and monitoring agricultural crop water productivity (or “crop per drop”) (Figure 1). There is increasing pressure to reduce agricultural water use by producing more food from existing or even reduced:
(a) areas of croplands (more crop per unit area); and
(b) quantities of water (more crop per unit of water).
This increased food production per unit of water or increasing water productivity (or “more crop per drop”; kg/m3) is expected to lead to a “blue revolution” in agriculture making a major contribution to global food security in the twenty-first century. In this project we will study the current and the past water consuming patterns of the crops by modeling and mapping crop water productivity in California’s Central Valley and other places in the United States. This will allow us to quantify historical and current water use by crops in study areas. Water savings in the study areas can then be determined by understanding and establishing:
1. Percentage of cropland areas under medium or low crop water productivity.
2. Quantum of water used by each crop for each level of water productivity.
Subsequently, the water that could be saved will be modeled for different scenarios, including:
1. Increasing crop water productivity,
2. Growing less water consuming crops (e.g., wheat in place of rice) in some areas.
3. Growing second season crops with low water consuming, short season crops that still provide rich nutrition and economic value (e.g., lentil, chickpea, fava bean, in place of certain proportion of rice fields).
Such approaches can potentially save massive quantities of water that could be used to replenish existing surface and ground water reservoirs and/or help create new “water banks”. We will study these separately for rainfed and irrigated croplands. Growing fewer water consuming crops and/or increasing water productivity in rainfed croplands will help recharge groundwater and/or fill existing or new small ponds or reservoirs throughout the cropland areas. These are “new green water banks” (water saved from rainfed croplands). It may also help retain water in the reservoirs which can then be used for alternative uses. These are termed “new blue water banks” (water saved from irrigated croplands).
Novel Objectives
Given the above context, there are several novel objectives that will be achieved by this waterSMART project for 2001-2025 time-period using multisensory remote sensing high resolution imagery (30 m or better), machine learning, and cloud computing. These objectives are:
- Establish automated cropland algorithm's (ACA's) for Conterminous United States (CONUS).
- Develop automated cropland fallow algorithm (ACFA) for CONUS.
- Develop cropland versus cropland fallow products for the CONUS using Landsat, Sentinel, and MODIS satellite sensor data, cloud computing, and machine learning. This will be a unique contribution hitherto not produced by others;
- Conduct crop water productivity (“crop per drop”) research by taking major world crops (e.g., wheat, rice, corn, soybeans, cotton) in irrigated and rainfed cropland areas of the US using multi-sensor remote sensing, cloud computing, and machine learning.
These models and products by this research team are intended to compliment the CONUS products from USDA and other USGS groups. These studies will be conducted considering the factors such as the global irrigated and rainfed croplands (Figure 2), and Koppen-Geiger climate classification (Figure 3) and taking major world crops such as wheat, rice, barley, corn, soybeans, cotton, potatoes, sugarcane, pulses, alfalfa, etc. into consideration.
Outputs, Web Access, and Dissemination
We will perform a comprehensive assessment of the state-of-art of crop water productivity (CWP; “crop per drop”) research worldwide using remote sensing and non-remote sensing methods and approaches based on existing research and meta-analysis. A peer-reviewed journal article will be published based on this work. Then, the study will develop and publish three unique models for CONUS:
- Automated cropland fallow algorithm (ACFA)
- Automated cropland algorithm (ACA)
- Crop water productivity models (CWPM’s)
Nominal 30 m products for 2001-2025 will then be generated for CONUS using ACFA, ACA, and CWPM’s. Next, the study will “pin-point” cropland areas with low and high CWP. These maps and models will provide precise locations from where we can save water and by how much. Spatial representations will pinpoint “hotspots” across the regions where water availability will be severely limited in the future. Scenarios will then be developed that determine how much water (and where) can be saved via improved CWP, and/or the planting of water saving, short duration crops. The scenario outcomes would identify where "new water" would be generated and/or the opportunities for replenishment of existing water resources- both surface and ground water. These scenario analyses will be conducted separately for rainfed and irrigated crops to establish “green water” savings (from rainfed croplands), and “blue water” savings (from rainfed croplands). We will nominate several new or existing reservoirs for this new water as well as establish where and how much of this will be below ground. The research will analyze potential reductions in applied water, which allow farmers and water agencies to remove less water from streams, improving stream quality and ecosystem health, while reducing pumping, delivery, and treatment costs. We will show, through improved CWP in irrigated croplands, various quanta of “new water” that becomes available for alternative uses like urban, industrial, riparian restoration, and re-forestation. Once CWP maps are produced at different resolutions for the representative areas and extrapolated to larger areas using the best models, we will build spatial models for each of the 3 WP study river basins in GEE cloud that will simulate “new water” saved through various scenarios such as improved WP and re-allocation of crops (e.g., growing wheat instead of rice). Users will be able to view and query, compare scenario maps, and generate customized maps from the website. All data and products will be made available through USGS global croplands data portal (e.g., www.croplands.org; LP DAAC). Finally, scenario analysis will allow us to compare existing water use based on existing normal water productivity of various existing crops. This will allow us to suggest alternative pathways.
Impact and Outcomes
This project is targeted to demonstrate: (a) scientific advances in understanding, modeling, and mapping cropland fallows and crop water productivity (“crop per drop”) through advanced remote sensing data; (b) methodological advances in crop-by-crop water use/ET modeling and automated machine learning algorithms for cropland fallows, crop water use, and crop water productivity in cloud computing; (c) societal benefits made by clear demonstration of opportunities for water savings by modeling, mapping, and pin-pointing areas of low and high water productivity and thus contributing to food security. It should help in “understanding variability of agricultural water use and characterizing short and long-term imbalances between agricultural water supplies and agricultural water requirements.”
- Science
Below are other science projects associated with this project.
Global Food-and-Water Security-support Analysis Data (GFSAD)
The GFSAD is a NASA funded project (2023-2028) to provide highest-resolution global cropland data and their water use that contributes towards global food-and-water security in the twenty-first century. The GFSAD products are derived through multi-sensor remote sensing data (e.g., Landsat-series, Sentinel-series, MODIS, AVHRR), secondary data, and field-plot data and aims at documenting cropland...Focus Area Studies
Focus Area Studies were stakeholder-driven assessments of water availability in river basins with known or potential conflict. They contributed toward ongoing assessments of water availability in large watersheds, provided opportunities to test and improve approaches to water availability assessment, and informed the National Water Census with local information. These studies focused on key water...Global Hyperspectral Imaging Spectral-library of Agricultural-Crops & Vegetation (GHISA)
This webpage showcases the key research advances made in hyperspectral remote sensing of agricultural crops and vegetation over the last 50 years. There are three focus areas:WaterSMART: Improving Tools for Assessing and Forecasting Ecological Responses to Hydrologic Alteration
WaterSMART (Sustain and Manage America’s Resources for Tomorrow) is a program of the Department of the Interior that focuses on improving water conservation and helping water-resource managers make sound decisions about water use. - Multimedia
Below are multimedia items associated with this project.
Meta-analysis of Global Crop Water Productivity of Irrigated CroplandsPredicting and Preventing Crisis in Irrigated Water Use (CALFED) - Publications
Below are publications associated with this project.
A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades
The overarching goal of this study was to perform a comprehensive meta-analysis of irrigated agricultural Crop Water Productivity (CWP) of the world’s three leading crops: wheat, corn, and rice based on three decades of remote sensing and non-remote sensing-based studies. Overall, CWP data from 148 crop growing study sites (60 wheat, 43 corn, and 45 rice) spread across the world were gathered fromAuthorsDaniel J. Foley, Prasad Thenkabail, Itiya Aneece, Pardhasaradhi Teluguntla, Adam OliphantFallow-land Algorithm based on Neighborhood and TemporalAnomalies (FANTA) to map planted versus fallowed croplands usingMODIS data to assist in drought studies leading to water and foodsecurity assessments
An important metric to monitor for optimizing water use in agricultural areas is the amount of cropland left fallowed, or unplanted. Fallowed croplands are difficult to model because they have many expressions; for example, they can be managed and remain free of vegetation or be abandoned and become weedy if the climate for that season permits. We used 250 m, 8-day composite Moderate Resolution ImAuthorsCynthia Wallace, Prasad S. Thenkabail, Jesus R. Rodriguez, Melinda K. BrownHyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation)
Evapotranspiration (ET) is an important component of micro- and macro-scale climatic processes. In agriculture, estimates of ET are frequently used to monitor droughts, schedule irrigation, and assess crop water productivity over large areas. Currently, in situ measurements of ET are difficult to scale up for regional applications, so remote sensing technology has been increasingly used to estimatAuthorsMichael T. Marshall, Prasad S. Thenkabail, Trent Biggs, Kirk PostAdvantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation
Crop biomass is increasingly being measured with surface reflectance data derived from multispectral broadband (MSBB) and hyperspectral narrowband (HNB) space-borne remotely sensed data to increase the accuracy and efficiency of crop yield models used in a wide array of agricultural applications. However, few studies compare the ability of MSBBs versus HNBs to capture crop biomass variability. TheAuthorsMichael T. Marshall, Prasad S. ThenkabailDeveloping in situ non-destructive estimates of crop biomass to address issues of scale in remote sensing
Ground-based estimates of aboveground wet (fresh) biomass (AWB) are an important input for crop growth models. In this study, we developed empirical equations of AWB for rice, maize, cotton, and alfalfa, by combining several in situ non-spectral and spectral predictors. The non-spectral predictors included: crop height (H), fraction of absorbed photosynthetically active radiation (FAPAR), leaf areAuthorsMichael T. Marshall, Prasad S. ThenkabailBiomass modeling of four water intensiveleading world crops using hyperspectral narrowbands in support of HyspIRI Mission
New satellite missions are expected to record high spectral resolution information globally and consistently for the first time, so it is important to identify modeling techniques that take advantage of these new data. In this paper, we estimate biomass for four major crops using ground-based hyperspectral narrowbands. The spectra and their derivatives are evaluated using three modeling techniquesAuthorsMichael T. Marshall, Prasad S. ThenkabailSeasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm
Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal faAuthorsZhuoting Wu, Prasad S. Thenkabail, Rick Mueller, Audra Zakzeski, Forrest Melton, Lee Johnson, Carolyn Rosevelt, John Dwyer, Jeanine Jones, James P. VerdinAn Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data
The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research,AuthorsPrasad S. Thenkabail, Zhuoting Wu - Partners
Below are partners associated with this project.