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
GIScience and Remote Sensing
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Abstract
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 Imaging
Spectroradiometer normalized difference vegetation index data to develop an algorithm
that can routinely map cropland status (planted or fallowed) with over 75% user’s and
producer’s accuracies. The Fallow-land Algorithm based on Neighborhood and
Temporal Anomalies (FANTA) compares the current greenness of a cultivated pixel to
its historical greenness and to the greenness of all cultivated pixels within a defined
spatial neighborhood, and is therefore transportable across space and through time. This
article introduces FANTA and applies it to California from 2001 to 2015 as a case study
for use in data-poor places and for use in historical modeling. Timely and accurate
knowledge of the extent of fallowing can provide decision makers with insights and
knowledge to mitigate the impacts of drought and provide a scientific basis for effective
management response. This study is part of the WaterSMART (Sustain and Manage
America’s Resources for Tomorrow) project, an interdisciplinary and collaborative
research effort focused on improving water conservation and optimizing water use.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | 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 |
| Series title | GIScience and Remote Sensing |
| DOI | 10.1080/15481603.2017.1290913 |
| Volume | 54 |
| Issue | 2 |
| Year Published | 2017 |
| Language | English |
| Publisher | Taylor & Francis |
| Contributing office(s) | Western Geographic Science Center |
| Description | 25 p. |
| First page | 258 |
| Last page | 282 |
| Country | United States |
| State | California |
| Other Geospatial | Central Valley |
| Google Analytic Metrics | Metrics page |