Chapter 2
Remote Sensing for Agriculture
Feng Gao
Abstract Remote sensing provides an in-direct approach to monitor agricultural
landscapes efficiently and consistently. It plays a critical role in current agricultural
management. In the past, remote sensing application has been limited to crop type
mapping due to the high cost or lack of remote sensing observations. In recent years,
high temporal and spatial resolution satellite observations have become available.
Many medium resolutions (10–100 m) satellite images are freely accessible to the
public. The near-surface observations and unmanned aerial vehicles are common.
The suite of remote sensing platforms has provided the capability of application for
field-scale agricultural management. This chapter introduces basic remote sensing
concepts and applications in agriculture. Remote sensing characteristics, such as
spatial, temporal, and spectral resolutions are discussed. The major agriculturalrelated and freely available remote sensing data sources (e.g., Landsat, MODIS,
VIIRS, and Sentinel-2) and the data products are presented. Remote sensing applications in crop type classification, crop phenology mapping, crop yield estimation,
crop water use monitoring, and soil moisture retrieving are introduced. The current
progress and potential future directions for agriculture remote sensing are discussed.
Keywords Remote sensing · Crop type · Crop phenology · Crop water use · Yield
estimation · Crop water use · Soil moisture · Landsat · Sentinel-2 · MODIS
This chapter discusses the theoretical foundation of remote sensing in agriculture, characteristics of
the major agriculture-related remote sensing satellites, the remote sensing data sources and
accesses, and major categories of remote sensing applications in agriculture.
F. Gao (*)
USDA, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville,
MD, USA
e-mail: Feng.Gao@USDA.GOV
© Springer Nature Switzerland AG 2021
L. Di, B. Üstündağ (eds.), Agro-geoinformatics, Springer Remote Sensing/
Photogrammetry, https://doi.org/10.1007/978-3-030-66387-2_2
7
Remote Sensing for Agriculture
Feng Gao
Abstract Remote sensing provides an in-direct approach to monitor agricultural
landscapes efficiently and consistently. It plays a critical role in current agricultural
management. In the past, remote sensing application has been limited to crop type
mapping due to the high cost or lack of remote sensing observations. In recent years,
high temporal and spatial resolution satellite observations have become available.
Many medium resolutions (10–100 m) satellite images are freely accessible to the
public. The near-surface observations and unmanned aerial vehicles are common.
The suite of remote sensing platforms has provided the capability of application for
field-scale agricultural management. This chapter introduces basic remote sensing
concepts and applications in agriculture. Remote sensing characteristics, such as
spatial, temporal, and spectral resolutions are discussed. The major agriculturalrelated and freely available remote sensing data sources (e.g., Landsat, MODIS,
VIIRS, and Sentinel-2) and the data products are presented. Remote sensing applications in crop type classification, crop phenology mapping, crop yield estimation,
crop water use monitoring, and soil moisture retrieving are introduced. The current
progress and potential future directions for agriculture remote sensing are discussed.
Keywords Remote sensing · Crop type · Crop phenology · Crop water use · Yield
estimation · Crop water use · Soil moisture · Landsat · Sentinel-2 · MODIS
This chapter discusses the theoretical foundation of remote sensing in agriculture, characteristics of
the major agriculture-related remote sensing satellites, the remote sensing data sources and
accesses, and major categories of remote sensing applications in agriculture.
F. Gao (*)
USDA, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville,
MD, USA
e-mail: Feng.Gao@USDA.GOV
© Springer Nature Switzerland AG 2021
L. Di, B. Üstündağ (eds.), Agro-geoinformatics, Springer Remote Sensing/
Photogrammetry, https://doi.org/10.1007/978-3-030-66387-2_2
7