Remote sensing application in agriculture

C H A P T E R

24 Remote sensing application in agriculture O U T L I N E 24.1 Introduction

872

24.2 Cropland information extracting 24.2.1 Cropland mapping 24.2.2 Monitoring cropland change 24.2.3 Agricultural irrigation

872 872 874 875

24.3 Crop yield prediction 880 24.3.1 Rice yield prediction by using NOAA-AVHRR NDVI and historical rice yield data 880 24.3.2 A production efficiency modele based method for satellite estimates of corn and soybean yields 881 24.4 Drought monitoring of crop 885 24.4.1 Analysis of agricultural drought using vegetation temperature condition index 887 24.4.2 Monitoring agricultural drought using multisensor remote sensing data 889

24.5 Crop residue monitoring 24.5.1 Crop residue cover 24.5.2 Crop residue burning

892 892 894

24.6 The impact from cropland 24.6.1 Irrigation impacts on land surface parameters 24.6.2 Impacts of cropland on surface temperature 24.6.3 Impact of crop residue burning

897

899 900

24.7 Response of crops to climate change 902 24.7.1 Effects of extreme heat on wheat growth 902 24.7.2 Effects of changes in humidity and temperature on crops 905 24.8 Summary

907

References

909

Further reading

914

as well as the simulation of carbon nitrogen cycle and the formulation of sustainable agricultural development policies. This chapter aims to describe how to use remote sensing data products for agricultural research, including extracting farmland information (24.2), detection of farmland change, estimation of grain yield (24.3), monitoring of agricultural disasters (drought, 24.4), monitoring crop residue (24.5), analysis of influence of farmland changes on surface parameters and environment (24.6), and study of the impact of climate change on farmland ecosystem (24.7).

Abstract Agriculture is the most important land use activity in the world. Agriculture not only affects the change of land cover but also has a profound impact on the sustainable development of social economy, food security, water and environment, ecosystem services, climate change, and carbon cycle. The area, location, status, and conversion information of farmland are important to understand how human activities affect the biosphere, hydrosphere, atmosphere, and lithosphere, Advanced Remote Sensing, Second Edition https://doi.org/10.1016/B978-0-12-815826-5.00024-6

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© 2020 Elsevier Inc. All rights reserved.

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24. Remote sensing application in agriculture

24.1 Introduction Agriculture is the predominant land use activity on the planet. Globally, cropland area was estimated at 265 Mha in 1700, 1471 Mha in 1990 (Goldewijk, 2001), and about 1.5e1.8 Bha at the end of the millennium (Ramankutty and Foley, 1999). It is estimated that around 46.5 million km2 of national vegetation had been converted to agricultural land (one-third for cropland and two-third for pasture) (Ramankutty et al., 2008). Agriculture is the largest component of anthropogenic water use. Irrigated agriculture consumes about 84% of the water used by humans globally (Shiklomanov, 2000). It is estimated that the global total crop water use was 6685 km3/yr during the time period 1998e2002; blue water use was 1180 km3/yr; green water use of irrigated crops was 919 km3/yr; and green water use of rainfed crops was 4586 km3/yr (Siebert and Doll, 2010). Blue water refers to water in lakes, reservoirs, rivers, ice caps, and groundwater, and green water refers to effective rainfall. Agriculture also alters the C and N cycles. Agriculture contributes 52% of global anthropogenic methane emissions and 84% of global nitrous oxide emissions (Smith et al., 2008). It is estimated that the global technical mitigation potential from agriculture by 2030 will be approximately 5500e6000 Mt CO2-eq./year (Smith et al., 2008), and the C sink capacity of global agricultural and degraded soil is 50% e66% of the historic carbon loss of 42e78 GtC (Lal, 2004). Therefore, the impacts of agriculture go far beyond changes in land cover; agriculture has implications for social economy, food security, water and environment sustainability, ecosystem services, climate change, and the carbon cycle (Foley et al., 2005; Khan and Hanjra, 2009; Lal, 2004, 2007; Paustian et al., 1997). Information on acreage, location, status, and transformation of cropland is crucial for an understanding of how

human activity impacts on the biosphere, hydrosphere, atmosphere, and pedosphere, for modeling the C and N cycles and for guiding policies for sustainable agricultural development. Various remote sensing data products have been widely used in the extraction of farmland spatial distribution (Zhu et al., 2014; SteeleDunne et al., 2017), crop type identification (Wardlow et al., 2007; Pena-Barragan et al., 2011; Sonobe et al., 2018), growth monitoring (Gitelson, 2004; Shafian et al., 2018), crop phenological monitoring (Sakamoto et al., 2005), yield estimation (Xin et al., 2013; Chlingaryan et al., 2018), crop disaster monitoring (Xin et al., 2013; Chlingaryan et al., 2018), crop response to climate change (Lobell et al., 2012; Brown et al., 2012), and others (Zheng et al., 2014; Begue et al., 2018).

24.2 Cropland information extracting 24.2.1 Cropland mapping Information about the distribution of cropland is important for land management and trade decisions, and it is also needed to estimate crop stress and productivity as well as other relative variables such as irrigation requirements and so on (Monfreda et al., 2008; Wu et al., 2010). Traditionally, crop areas are reported based on census data that cannot provide geographical distribution information. Besides, the process is tedious, time-consuming, and costly. Remote sensing has proven to be an effective tool to estimate crop distribution for a wide range of end users including government agencies, farmers, and modelers (Biradar et al., 2009; Frolking et al., 2002; Gumma et al., 2011; Karkee et al., 2009; Murthy et al., 2003; Ozdogan, 2010; Pena-Barragan et al., 2011; Wardlow et al., 2007). The technologies used for identifying cropland from satellite data have evolved from simple unsupervised approaches to various

24.2 Cropland information extracting

complex supervised classifications (e.g., maximum likelihood classification, support vector machine (SVM), decision tree, and wavelet transform classification, artificial neural networks classification, and so on), from pixelbased methods to subpixel (e.g., linear spectral unmixing, support vector regression, and decision tree regression) and object-oriented methods and from exploring spectral differences to examining crop phenology differences in time series data. The data used for cropland mapping depends on the purpose and extent of the study, as well as data availability and consumption. Local and regional studies work best with high resolution (e.g., IKONOS, Quickbird, TM, ETM, SPOT), national and continental studies work best with medium-resolution imagery (e.g., MODIS), and global studies are more likely to use lowresolution imagery. Optical, hyperspectral, and radar imageries have all proven to be useful for crop monitoring. It is commonly accepted that the classification results from higher resolution data are usually more accurate than those from lower resolution data, and the results from multitemporal images are more reliable than those from a single image. For example, Lobell and Asner (2004) mapped cropland using MODIS vegetation index (VI) data in two agricultural regions, i.e., the Yaqui Valley (YV) in Northwest Mexico and the Southern Great Plains (SGP) in the United States. The data used in their study included Landsat data and MODIS 16-day 250-m VI composite products (NDVI, EVI, and quality assessment flags). The method used to map cropland distribution in their study is known as probabilistic temporal unmixing. Fig. 24.1 shows the entire procedure. They first constructed time series of red and near-infrared (NIR) reflectance based on the MODIS NDVI composite products and chose image end-members from the reflectance time series with the help of Landsat classification. Instead of defining end-members with a single spectrum, they defined them as a set of spectra. In YV, the end-member set included 52

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FIGURE 24.1 Outline of probabilistic temporal unmixing (PTU) algorithm. The steps within the gray box, namely, the selection of an end-member from each set and the calculation of end-member fractions, are repeated many (50) times to derive distributions of end-member fractions that reflect the uncertainty associated with end-member variability (Lobell and Asner, 2004).

end-members of wheat, 28 end-members of maize, and 46 end-members of uncropped land. In SGP, the end-member set included 22 end-members of wheat, 24 end-members of pasture, and 42 end-members of summer crops. For each pixel, they repeatedly carried out spectral unmixing analysis using randomly selected end-members from end-member sets and produced several fraction maps. Finally, they built up a probability distribution of fractions of each end-member. Fig. 24.2 shows the mean and standard deviation images of wheat fractions derived using their method for two study areas. This study provides a reference for crop identification in areas where mixed pixel problems are prominent.

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24. Remote sensing application in agriculture

FIGURE 24.2 Mean (top) and standard deviation (bottom) images of wheat fractions from PTU, for YV (left side) and SGP (right side) (Lobell and Asner, 2004).

24.2.2 Monitoring cropland change Besides cropland extent, information on cropland changes and their drivers is very important for assessing food and water security and guiding policies on sustainability (Liu et al., 2005a; Yan et al., 2009). Traditionally, the information on crop area change is derived by comparing the census data for many years. However, this method cannot provide geographical distributions. As a result, remote sensing plays a more and more important role in monitoring the transition of cropland (Amissah-Arthur et al., 2000; Doygun, 2009; Zhao et al., 2004; Zomeni et al., 2008). Methods for monitoring cropland change include various change detection technologies

that are commonly applied in LCLUC detection and have been summarized in Section 23.2.2 of this chapter. The most popular and straightforward change detection method is a postclassification comparison method for cropland. In Section 2.2, we have shown a case study in which a postclassification comparison method was used to detect urbanization. Here, we show a case study in which the changed areas are directly distinguished from the unchanged areas by multistage classification. For example, Kuemmerle et al. (2008) monitored change in Carpathian cropland by classification of multitemporal composites. The data used in their study included field measurement data, 16 Quickbird images available from Google Earth, TM and ETMþ images (path/row 186/

24.2 Cropland information extracting

26) for October 2, 1986; July 27, 1988; June 10, 2000; and August 2, 2000. Field measurement was conducted in the summer of 2004, spring of 2005, and spring of 2006, and a total of 481 ground truth points were collected by field measurement. Their method included the following steps. (1) They preprocessed Landsat images by geometrical and atmospheric rectifications and then masked out forests, water bodies, and built-up areas from Landsat images from 1988. They further masked out areas with altitudes higher than 1000 m from all four Landsat images and then stacked the four masked images into one multitemporal dataset. (2) They digitized 1171 plots using Quickbird images with the help of field measurement data. The 1171 plots plus 481 field measurements were used as ground truth data for training and validation purposes. (3) They divided all ground truth data into three classes, namely, unchanged area, fallow land, and reforested land, and used 1079 ground truth points to train the classifier of SVM and classify the multitemporal dataset. (4) They used the remaining 573 ground truth samples to validate the accuracy of classification. Fig. 24.3 shows their farmland abandonment map. The overall accuracy of their farmland abandonment map is 90.9% and the kappa is 0.82. During the period from 1988 to 2000, abandoned farmland in the border triangle of Poland, Slovakia, and Ukraine covered 1285 km2, 12.5% of which was converted to forests.

24.2.3 Agricultural irrigation Irrigated agriculture consumes about 84% of the water used by humans globally (Shiklomanov, 2000). It affects hydrological processes (Rosenberg, 2000; Shibuo et al., 2007), local climate (Adegoke et al., 2007; Boucher et al., 2004; Lobell et al., 2006b; Sen Roy et al., 2007; Stohlgren et al., 1998; Tilman et al., 2001;

875

Trenberth, 2004), and environmental variables such as soil salinity (Metternicht and Zinck, 2003) and soil quality depletion (Asadi et al., 2008; Dobermann and Oberthiir, 1997; Liu et al., 2005b). As a result, accurate information on the extent of irrigation is needed in many areas of research, such as water exchange between the land surface and the atmosphere (Boucher et al., 2004; Gordon et al., 2005; Ozdogan et al., 2006), climate change, irrigation water requirements (D€ oll and Siebert, 2002), water resources management (V€ or€ osmarty et al., 2005), hydrological modeling, and agricultural planning. Several efforts have been made to map irrigated areas globally. One is the US Geological Survey (USGS) Global Land Cover Map (Loveland et al., 2000) produced on the basis of 1-km Advanced Very HigheResolution Radiometer (AVHRR) observations between April 1992 and September 1993. It includes four irrigated land classes, namely, irrigated grassland, rice paddies and fields, hot irrigated cropland, and cool irrigated cropland. The USGS map provides a general classification scheme, so the irrigated classes used as subsets of the general classification scheme are less accurate. In addition, the FAO and the University of Frankfurt (FAO/UF) have published several versions of a global map of irrigated areas (FAO map) (D€ oll and Siebert, 1999; Siebert and D€ oll, 2001; Siebert et al., 2005b). The latest version is a global dataset of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000) (Portmann et al., 2010) (Fig. 24.4). This dataset describes the monthly growing areas of 26 irrigated and rainfed crops including wheat, rice, maize, barley, rye, millet, sorghum, soybeans, sunflower, potatoes, cassava, sugar cane, sugar beet, oil palm, rape seed/canola, groundnuts/ peanuts, pulses, citrus, date palm, grapes/vines, cocoa, and coffee, as well as related crop calendars for 402 spatial units. The spatial resolution of this map is 50  50 .

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24. Remote sensing application in agriculture

FIGURE 24.3

Farmland abandonment from 1986 to 2000 in the study area (Kuemmerle et al., 2008).

The International Water Management Institute (IWMI) also produced a global map of irrigated areas (IWMI map) (Thenkabail et al., 2006, 2008, 2009). The IWMI map has 10-km grid resolution and was produced using 20 years of AVHRR data and other additional data, including SPOT-VEGETATION, Japanese Earth Resources Satellite, and Landsat GeoCover 2000 data. The area statistics are reported as

annualized irrigated area and total area available for irrigation. IWMI’s method offers two advantages; it considers irrigation type and irrigation intensity information and uses subpixel decomposition techniques to derive the irrigated fraction within a pixel (Thenkabail et al., 2007). In addition to global-scale data, irrigated area studies at other scales have been reported (Beltran and Belmonte, 2001; Biggs et al., 2006;

24.2 Cropland information extracting

877

FIGURE 24.4 Global distribution of (top) rainfed harvested area (AHR) and (middle) irrigated harvested area (AHI) in percent of grid cell area and (bottom) AHI in percent of total harvested area (AHT), for 1998e2002 (Portmann et al., 2010).

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24. Remote sensing application in agriculture

FIGURE 24.5 Flowchart of the major steps in the proposed mapping algorithm. Each dashed box with a number refers to the processing step in the proposed irrigation mapping procedure (Ozdogan and Gutman, 2008).

Boken et al., 2004; Dheeravath et al., 2010; El-Magd et al., 2003; Ozdogan and Gutman, 2008; Thenkabail et al., 2005; Wriedt et al., 2009; Zhu et al., 2014). For example, Ozdogan and Gutman (2008) produced a national irrigation map of the United States using two tree-

based models (decision trees and regression trees). Decision trees were used to distinguish irrigated pixels from nonirrigated pixels, while regression trees were used to estimate irrigation fraction within each irrigated pixel identified by decision trees.

24.2 Cropland information extracting

Fig. 24.5 shows their whole procedures. They first calculated the radiative dryness index (D) using mean annual net radiation, annual precipitation, and the latent heat of vaporization and then used it further to calculate a water availability parameter (W). They found a linear relationship between W and the fractional irrigated area, from which they calculated the irrigation potential and referred to it as the effective irrigation potential. Second, they excluded the cloud, snow, and nonagricultural pixels from the MODIS time series data and calculated NDVI and GRI using MODIS reflectance data. GRI equals NIR band reflectance divided by green band reflectance. Third, they built two training sets from Landsat images. One set consisted of irrigated/nonirrigated polygons that were used to train the decision tree classifier (C4.5) and classify the input dataset into two categories, namely, irrigated and nonirrigated areas. Input datasets included GRI, NDVI, and the

879

effective irrigation potential. Another dataset consisted of the irrigation fraction in each 500-m pixel that was used to train the regression tree. To get the irrigation fraction, they classified each Landsat image into irrigated and nonirrigated classes and aggregated these maps up to 500 m. Finally, they used the regression tree method to retrieve the irrigation fraction in each irrigated pixel identified in the former step. Fig. 24.6 shows their irrigation map. They collected validation samples from the highresolution Landsat images and calculated the error matrix. Accuracy assessment suggested that the map was highly accurate in the Western United States but less accurate in the Eastern United States. They also compared their map to a global irrigation map made by the FAO, and their map shows more detail. The irrigated area estimated from their map is also highly correlated with the irrigated agriculture statistical datasets.

FIGURE 24.6 Spatial distribution of irrigation in the United States c.2001 mapped form MODIS and ancillary data using the proposed procedure. The thick vertical line separates the east and west portions of the country individually selected for accuracy assessment (Ozdogan and Gutman, 2008).

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24. Remote sensing application in agriculture

24.3 Crop yield prediction Crop yield estimation methods based on remote sensing can be generally divided into three categories: statistical analysis of remote sensing (Shanahan et al., 2001; Panda et al., 2010; Liu and Kogan, 2002; Dempewolf et al., 2014), production efficiency model (Bastiaanssen and Ali, 2003; Lobell et al., 2003; Tao et al., 2005; Liu et al., 2010; Peng et al., 2014; Yuan et al., 2016), and crop growth model (Mo et al., 2005; Fang et al., 2008; Moriondo et al., 2007; Padilla et al., 2012; Wang et al., 2013, 2014). The first method develops a relationship model between remote sensing bands (band combinations or various remote sensing indices, etc.) and crop yield (or yield components). The second method suggests crop yields under nonstressed conditions correlate linearly with the amount of absorbed photosynthetically active radiation. This method first estimates crop aboveground dry matter using remote sensing data and then converts this into crop yield. The third method applies satellite data to calibrate physiologically based crop models and then uses crop models to simulate physical crop growth processes and finally estimate yield.

24.3.1 Rice yield prediction by using NOAA-AVHRR NDVI and historical rice yield data Since the 1980’s, NDVI time series has been used for crop yield forecast. The most popular method is to build the empirical relationship between NDVI and crop yield (Mkhabela et al., 2005; Huang et al., 2014; Mashaba et al., 2017). However, different studies use different NDVI variables, such as original NDVI, average or accumulated NDVI over the growth period, average or accumulated NDVI over key growing stages, etc. Although NDVI time series can reflect interannual crop yield variation, crop yield changes caused by technical development

or management improvement cannot be detected by NDVI. Therefore, Huang et al. (2013) used long-term historical yield data and a time series of AVHRR NDVI composite imagery to estimate paddy rice yield of Heilongjiang, Sichuan, Jiangxi, Hunan, and Guangxi provinces in China. Their basic concept was to first decompose historical rice yield into trend and remotely sensed yields, where trend yield refers to the component regulated by agricultural technology and remotely sensed yield refers to the component regulated by natural environmental conditions, such as temperature, precipitation, pets, disease, etc. Independent models were built to estimate the trend and remotely sensed yields, and then final crop estimation was calculated from the sum of trend and remotely sensed yield. The data used in their study mainly included time series 15 day AVHRR maximum NDVI composite imagery with 8 km spatial resolution from July 1981 to December 2006 and historical rice yield data from the China Statistical Year Book 1979e2009. The main processing steps were as follows: (1) Five-year moving average and linear regression models were employed to fit historical rice yield data for each province, and they found the result detrended by linear regression performed better than that detrended by a 5-year moving average for Heilongjiang, Henan, Jiangxi, and Sichuan, and performed worse for Guangxi. Therefore, they used trend yield estimates from the linear regression method for Heilongjiang, Henan, Jiangxi, and Sichuan and from the 5-year moving average for Guangxi for further analysis. (2) Remotely sensed yield was calculated using the difference between historical yield data and the trend yield estimated in step 1. (3) Remotely sensed yield was used as the dependent variable and NDVI as independent variables to build remotely sensed yield prediction models through

24.3 Crop yield prediction

stepwise regression, selecting remotely sensed yield prediction models with the highest correlation coefficients for each province. Table 24.1 shows NDVI variables used in their study and Table 24.2 shows the best model chosen for each province. (4) Trend yield estimates from step 1 and remotely sensed yield estimates from step 3 were summed to obtain the final yield estimation. Fig. 24.7 compares observed and predicted rice yields for Heilongjiang, Hunan, Jiangxi, Sichuan, and Guangxi provinces form 1982 to 2004. Table 24.3 shows validation results for predicted rice yields form 2005 to 06. Validation results indicate that relative error between predicted and observed rice yield is w5.85%. Thus, their proposed method would be suitable for rice yield prediction at provincial level. The authors also argued that their method has potential to predict crop yield for other counties and other crop types.

24.3.2 A production efficiency modelebased method for satellite estimates of corn and soybean yields Photosynthesis is the main material source of crop yield formation, but only a part of sunlight can be utilized by leaf photosynthesis. Therefore, the concept of vegetation productivity has been proposed to describe the ability of plants to absorb CO2 in the atmosphere through photosynthesis, converting light to chemical energy and hence accumulating organic dry matter. Vegetation productivity can be divided into gross primary (GPP) and net primary (NPP) productivity. GPP refers to the total amount of organic carbon fixed by photosynthesis by green plants for unit time and unit area, whereas NPP is the residual GPP from organic matter consumed by plant autotrophic respiration. GPP and NPP are highly correlated with crop

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biomass; hence, they are often used to study crop yield estimation. Considering the difference of radiation use efficiency (RUE) of various crops and the mixed pixel problem, Xin et al. (2013) improved the crop GPP calculation method and used it to estimate maize and soybean yields in Midwestern United States. Their data included 8-day 1 km leaf area index (LAI) and fraction of photosynthetically active radiation product (MOD15A2) and vegetation productivity product (MOD17A2) over the period of 2009e11. Statistical data describing corn and soybean yields and harvested areas in each US county were provided by the Quick Stats database from the National Agricultural Statistics Service (NASS) of the US Department of Agriculture. Crop type classification maps with 30 m spatial resolution were sourced from the NASS cropland data layer (CDL) program and irrigation maps with 250 m resolution from the USGS early warning program. The main processing included the following steps: (1) Relatively pure corn and soybean MODIS pixels were identified using CDL. (2) Crop type-specific RUE values for corn and soybeans were used to estimate yields rather than the biome-wide value in the MOD17 algorithm. Corn and soybean RUE used in their study were 3.35 g/MJ PAR and 1.44 g / MJ PAR, respectively. (3) The GPP for each MODIS pixel was divided into contributions from crops and from other vegetation types, and Formula 24.1e24.6 were used to calculate the contribution from crops in a given MODIS pixel. (4) Crop GPP was converted to crop yield by Formula 24.7. (5) Corn and soybean yield estimation accuracy was validated for 12 states in Midwest United States (North Dakota, South Dakota, Nebraska, Kansas, Minnesota, Iowa, Missouri, Michigan, Wisconsin, Illinois, Indiana, and Ohio).

882 TABLE 24.1

24. Remote sensing application in agriculture

NVDI variables and their calculation formulas (Huang et al., 2013).

Number

NDVIs

Description of formulas

1

NDVImaxb1

The first biweekly NDVI before NDVImax

2

NDVImaxb2

The second biweekly NDVI before NDVImax

3

NDVImaxb3

The third biweekly NDVI before NDVImax

4

NDVImaxb4

The fourth biweekly NDVI before NDVImax

5

NDVImax

The maximum NDVI during the growth period

6

NDVImaxa1

The first biweekly NDVI after NDVImax

7

NDVImaxa2

The second biweekly NDVI after NDVImax

8

NDVImaxb4b3

ðNDVImaxb4 þ NDVImaxb3 Þ=2

9

NDVImaxb4b2

ðNDVImaxb4 þ NDVImaxb3 þ NDVImaxb2 Þ=3

10

NDVImaxb4b1

ðNDVImaxb4 þ NDVImaxb3 þ NDVImaxb2 þ NDVImaxb1 Þ=4

11

NDVImaxb4max

12

NDVImaxb4a1

ðNDVImaxb4 þ NDVImaxb3 þ NDVImaxb2 þ NDVImaxb1 þ NDVImax Þ=5 ðNDVImaxb4 þ NDVImaxb3 þ NDVImaxb2

13

NDVImaxb4a2

14

NDVImaxb3b2

ðNDVImaxb3 þ NDVImaxb2 Þ=2

15

NDVImaxb3b1

ðNDVImaxb3 þ NDVImaxb2 þ NDVImaxb1 Þ=3

16

NDVImaxb3max

ðNDVImaxb3 þ NDVImaxb2 þ NDVImaxb1 þ NDVImax Þ=4

17

NDVImaxb3a1

18

NDVImaxb3a2

ðNDVImaxb3 þ NDVImaxb2 þ NDVImaxb1 þ NDVImax þ NDVImaxa1 Þ=5 ðNDVImaxb3 þ NDVImaxb2 þ NDVImaxb1

19

NDVImaxb2b1

þ NDVImax þ NDVImaxa1 þ NDVImaxa2 Þ=6 ðNDVImaxb2 þ NDVImaxb1 Þ=2

20

NDVImaxb2max

ðNDVImaxb2 þ NDVImaxb1 þ NDVImax Þ=3

21

NDVImaxb2a1

ðNDVImaxb2 þ NDVImaxb1 þ NDVImax þ NDVImaxa1 Þ=4

22

NDVImaxb2a2

ðNDVImaxb2 þ NDVImaxb1 þ NDVImax þ NDVImaxa1 þ NDVImaxa2 Þ=5

23

NDVImaxb1max

ðNDVImaxb1 þ NDVImax Þ=2

24

NDVImaxb1a1

ðNDVImaxb1 þ NDVImax þ NDVImaxa1 Þ=3

25

NDVImaxb1a2

ðNDVImaxb1 þ NDVImax þ NDVImaxa1 þ NDVImaxa2 Þ=4

26

NDVImaxa1

ðNDVImax þ NDVImaxa1 Þ=2

27

NDVImaxa2

ðNDVImax þ NDVImaxa1 þ NDVImaxa2 Þ=3

28

NDVImaxa1a2

ðNDVImaxa1 þ NDVImaxa2 Þ=2

þ NDVImaxb1 þ NDVImax þ NDVImaxa1 Þ=6 ðNDVImaxb4 þ NDVImaxb3 þ NDVImaxb2 þ NDVImaxb1 þ NDVImax þ NDVImaxa1 þ NDVImaxa2 Þ=7

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24.3 Crop yield prediction

TABLE 24.2

Results of the stepwise regression models for remotely sensed rice yield using AVHRR-derived NDVI measures as independent variables (Huang et al., 2013).

Study areas

Model

R

Heilongjiang

YRS ¼ 849:158 þ 0:137NDVImaxa1

0.42a

4.508

361.99

Hunan

YRS ¼ 1240:690 þ 0:229mNDVImaxb1a2

b

19.342

114.57

Jiangxi

YRS ¼ 1553:145 þ 0:261mNDVImaxb1max

b

5.689

166.38

Sichuan

YRS ¼ 1495:515 þ 0:403mNDVImaxb4b3

b

24.238

207.07

Guangxi

YRS ¼ 1832:285 þ 1:138mNDVImaxb4b3

b

25.103

87.70

F-test value

0.69 0.46 0.73 0.92

RMSE

þ 0:214NDVImaxa2  1:315mNDVImaxb4b2 þ 0:307maxb2b1 a b

Significant at 0.05 level. Significant at 0.01 level, n ¼ 23.

GPPtotal;DOYn ¼ GPPcrop;DOYn þ GPPother;DOYn (24.1) GPPother;DOYn ¼ MRother;DOYn =ð1  CUEÞ (24.2) MRother;DOYn ¼ Leaf MRother þ Froot MRother (24.3) Leaf MRother ¼ LAIother =SLA  leaf mr base  Q10 mr½ðTavg20:0=10:0Þ (24.4) Froot MRother ¼ LAIother =SLA  froot leaf ratio  froot mr base  Q10 mr½ðTavg20:0=10:0Þ 
LAIother zmax LAItotal.DOY n¼89:N0 Yield ¼

nX ¼ N1 n>N0

1  1  MC

GPPcrop;DOYn 

(24.5) (24.6)

HI ð1 þ RSÞ (24.7)

where GPPtotal;DOYn refers to the GPP value for a specific day of year (DOY)n. GPPcrop;DOYn and GPPother;DOYn are the contribution from crops (i.e., corn or soybeans) and the contribution

from other vegetation types for the same time period, respectively. MRother;DOYn is the maintenance respiration of other vegetation (Kg C/day). It includes the maintenance respiration of the leaves of other vegetation (Leaf MRother ) and the maintenance respiration of fine roots of other vegetation (Froot MRother ).CUE is the carbon use efficiency.LAIother (m2$leaf$m2 ground area) is the estimated LAI of vegetation other than crops. SLA (projected leaf area m2/kg$leaf C) is the specific leaf area for a given pixel. LAItotal:DOY n¼89:N0 is the LAI value between April (DOY 89) and before mid-May (DOY N0). leaf mr base and froot mr base (kg$C$kg/C/ day$20 C) are the maintenance respiration of leaves and fine roots per unit mass at 20 C, respectively. froot leaf ratio is the ratio of the fine root to leaf mass. Q10 mr is an exponent shape parameter that controls respiration as a function of temperature.Tavg is the average daily temperature. Yield is estimated crop yield. HI is the harvest index. RS is the root to shoot ratio. MC is the grain moisture content. In their study, LAI was obtained from MOD15d. Tavg was estimated by using meteorological data from the NASA Data Assimilation Office. HI of corn and soybean are 0.53 and 0.42, respectively. RS of corn and soybean are 0.18 and 0.15, respectively. MC of corn and soybean are 0.11 and 0.10, respectively.

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24. Remote sensing application in agriculture

FIGURE 24.7 Observed versus predicted yields of rice (kg/ha) for the provinces of Heilongjiang (HLJ), Hunan (HN), Jiangxi (JX), Sichuan (SC), and Guangxi (GX) over the period 1982e2004 (Huang et al., 2013).

Other biome-specific coefficients are obtained from a Biome Parameter Look-Up Table from a general ecosystem model (Running et al., 2000). Figs. 24.8 and 24.9 compare crop yields estimated from MODIS data and reported by

NASS for rainfed counties and states in Midwestern United States, respectively, and Table 24.4 compares estimated crop yield errors from the authors’ approach with those from the original MOD17 GPP. Rainfed corn and soybean

24.4 Drought monitoring of crop

Observed and predicted rice yields (independent test) (Huang et al., 2013).

Provinces

Year

Observed Predicted Relative (kg/ha) (kg/ha) error (%)

Heilongjiang

2005

6795.7

6780.7

0.22

2006

6261.3

6897.8

10.17

2005

6050.3

6337.5

4.75

2006

6141.3

6441.2

4.88

Jiangxi

2005

5328.2

5545.9

4.09

2006

5475.1

5634.9

2.92

Sichuan

2005

7213.0

8018.4

11.17

2006

6420.7

7680.3

19.62

2005

4953.0

5028.98

1.53

2006

5088.0

5053.44

0.68

Hunan

Guangxi

yields estimated using the authors’ method was highly correlated with the NASS survey data. Crop yield estimate accuracy by the authors’ method was higher than that estimated using the original MOD17 GPP product.

(A)

Yield estimated from MODIS (MT/ha)

12

Drought is a phenomenon of water deficiency caused by water imbalance. Droughts are generally divided into four categories: meteorological droughts, agricultural droughts, hydrological droughts, and economic and social droughts. Agricultural ecosystems are most directly and severely affected by droughts. Drought indices based on meteorological stations are highly accurate and can be extended to the surface by interpolation. However, in areas lacking sufficient weather stations, they are impossible to accurately monitor crop drought conditions. Compared with meteorological stations, remote sensing provides large-scale real-time monitoring. Therefore, drought indices based on remote sensing data are widely used for crop drought monitoring. Drought indices derived from remote sensing data began to emerge in the 1990s, and common indices include NDVI, vegetation condition index (VCI), vegetation water supply index, and temperature vegetation dryness index. However, it is difficult for a single index to fully capture

(B) Rainfed corn

10 R2 = 0.772 RMSE = 0.89 8 ME = –0.18 6

4 2

0 0

24.4 Drought monitoring of crop

2 4 6 8 10 12 Yield reported by NASS (MT/ha)

4 Yield estimated from MODIS (MT/ha)

TABLE 24.3

885

Rainfed soybean

R2 = 0.659 3 RMSE = 0.38 ME = –0.02 2

1

0 0

1 2 3 4 Yield reported by NASS (MT/ha)

FIGURE 24.8 Comparisons between crop yields estimated from MODIS data and reported by the NASS for rainfed counties in the Midwestern United States. The black line is the 1:1 line. (A) Rainfed corn and (B) rainfed soybean (Xin et al., 2013).

886

24. Remote sensing application in agriculture

(A)

10

Corn North Dakota

R2 = 0.513 RMSE = –1.40 8 ME = –0.23 Nebraska 6

4 Kansas 2

0 0

Soybean

4 Yield estimated from MODIS (MT/ha)

Yield estimated from MODIS (MT/ha)

12

(B)

3

R2 = 0.583 RMSE = –0.38 ME = 0.07 Nebrasks North Dakota

2

1

0 0

2 4 6 8 10 12 Yield reported by NASS (MT/ha)

Wisconsin

Kansas

1 2 3 4 Yield reported by NASS (MT/ha)

FIGURE 24.9 Comparisons between crop yields estimated from MODIS data and reported by the NASS for states in the Midwestern United States. The black line is the 1:1 line. (A) Corn and (B) soybean (Xin et al., 2013).

agricultural drought information. Domestic and international studies have proposed integrating various remote sensing data to construct comprehensive drought indices and improve the accuracy of drought monitoring. For example, Liu and Kogan (2002) proposed a vegetation health index (VHI) by linearly combining TCI and VCI, and Rhee et al. (2010) proposed a drought monitoring index suitable for both dry and humid areas by linearly weighting land surface temperature (LST), NDVI, and tropical rainfall measuring mission (TRMM) precipitation. Zhang and Jia (2013) used microwave data to monitor droughts and constructed weights by TABLE 24.4

enumeration to construct a series of drought indices suitable for different regions and timescales. Hao and Aghakouchak (2014) studied several drought monitoring indices and proposed a multivariate drought monitoring index by integrating precipitation and soil moisture (SM) information and verified their proposed index for drought monitoring. This section introduces two crop drought monitoring cases based on remote sensing data. The first case uses the study of Patel et al. (2012) as an example to introduce using single remote sensing data (e.g., MODIS) to monitor agricultural drought. The second case uses the

Statistics between crop yields estimated from our approach using MODIS data and reported by the NASS for rainfed counties for each year from 2009 to 2011. Corn

Year

R

2009

Soybean

RMSE (MT/ha)

ME (MT/ha)

R

0.55 (0.15)

1.21 (5.52)

0.60 (5.39)

2010

0.54 (0.22)

1.17 (4.65)

2011

0.77 (0.46)

0.89 (4.56)

2

2

RMSE (MT/ha)

ME (MT/ha)

0.50 (0.35)

0.38 (0.86)

0.07 (0.77)

0.14 (4.38)

0.73 (0.53)

0.30 (0.97)

0.09 (0.89)

0.18 (4.28)

0.66 (0.53)

0.38 (1.06)

0.02 (0.95)

For comparison, values in parentheses are statistics using the standard MOD17 products (Xin et al., 2013).

887

24.4 Drought monitoring of crop

study of Rhee et al. (2010) as an example to introduce a method to monitor agricultural drought using multisensor remote sensing data.

24.4.1 Analysis of agricultural drought using vegetation temperature condition index Patel et al. (2012) analyzed agricultural drought conditions in Gujarat, India, 2000e04, based on the vegetation temperature condition index (VTCI) calculated from the MODIS data. The data used in their study included 8-day NDVI data and surface temperature data from MODIS during cloudless periods 2000e04 along with maximum and minimum temperature and rainfall data from 20 weather stations. The main processing included the following steps: (1) NDVI and MODIS surface temperature data during cloudless periods (day 241e297) over 2000e04 was used to construct NDVI-Ts spaces, and then wet and dry edges were extracted. VTCI was calculated by Formula 24.8e24.10 based on NDVI-Ts space.

(3) Historical production was detrended (Larson et al., 2004), and abnormal outputs were calculated,   Yai  1  100 DYai ¼ Yti

(24.11)

where Yai is detrended yield anomaly for the ith year, and Yai and Yti are actual and time trende based yield of ith year. (4) The relationship between VTCI and CMI and abnormal outputs was explored (Table 24.5), and VTCI drought threshold was divided according to the CMI, where 0.45e1.0, 0.45e0.38, 0.38e0.31, and 0.31e0.0 refer to normal, light, mid, and severe drought, respectively. (5) They used 0.45 VTCI threshold ¼ 0.45, where VTCI < 0.45 was classified as arid and VTCI > 0.45 was classified as areas without drought. Accumulated drought days in the study period were counted and then compared with actual crop yields to verify

LSTNDVIimax  LSTNDVIi VTCI ¼ LSTNDVIimax  LSTNDVIimin

(24.8)

Correlation coefficient of VTCI with yield and detrended yield anomalies (Patel et al., 2012).

LSTNDVIimax ¼ a þ bNDVIi

(24.9)

Food grains

0

0

LSTNDVIimin ¼ a þ b NDVIi

TABLE 24.5

(24.10)

where the LSTNDVIimax and LSTNDVIimin are maximum and minimum LSTs of pixels, which have the same NDVI value in a study region on each DOY or period of image, respectively. LSTNDVIi denotes actual LST of one pixel whose NDVI value is NDVIi. Coefficients a, b, a0 , and b0 can be estimated from an area large enough where SM at surface layer should span from wilting point to field capacity at pixel level. (2) Crop moisture index (CMI) was estimated using the 20 meteorological sites (Palmer, 1968).

2000

Oilseeds 2002

2000

2002

DOY Yield Anomaly Yield Anomaly Anomaly Anomaly 0.414 0.036

0.46 0.342

0.033

0.206

249

0.589 0.233

0.54 0.451

0.079

0.275

257

0.542 0.273

0.49 0.330

0.087

0.221

265

0.451 0.150

0.38 0.189

0.044

0.237

273

0.559 0.186

0.42 0.192

0.075

0.315

281

0.436 0.138

0.39 0.212

0.043

0.282

289

0.509 0.150

0.30 0.166

0.025

0.319

297

0.464 0.214

0.39 0.241

0.114

0.331

888

24. Remote sensing application in agriculture

FIGURE 24.10 Spatial pattern of VTCI on DOY 249 in year (A) 2000, (B) 2002, and (C) 2004 (Patel et al., 2012).

drought duration monitoring performance using VTCI images. Fig. 24.10 shows VTCI spatial distribution in drought (2000 and 2002) and drought-free (2004) years. VTCI was generally lower in 2000 and 2002 than 2004, VTCI was mostly >0.5 in 2004, and droughts were mainly distributed in midwestern and northern regions. Areas with higher VTCI indicate better irrigation, and the southern regions had higher VTCI because cover type was forest. Fig. 24.11 shows 2002 drought classification in the study area. Most of the study area

experienced severe drought in 2002, mainly distributed in the central and northern regions, with only a proportion of the study area experiencing moderate and light drought. Drought situation can be more directly described by the classification of drought level. Fig. 24.12 shows accumulated drought days during the study period. The drought situation was the most serious in 2002, followed by 2000. Crop yield monitoring results show good agreement with actual crop yield. Thus, VTCI can be used to distinguish drought and drought-free years and further analyze drought levels and drought duration.

FIGURE 24.11 Spatial pattern of drought severity in 2002 (Patel et al., 2012).

889

24.4 Drought monitoring of crop

2004

2002

2000

Drought duration (8-day periods) Non-Agri./ No drought

1

2

3

4

5

6

7

8

FIGURE 24.12 Spatial pattern of drought duration obtained from VTCI for drought (2000 and 2002) and wet (2004) years (Patel et al., 2012).

24.4.2 Monitoring agricultural drought using multisensor remote sensing data

(http://www.nass.usda.gov) 1981e2000. The main processing included the following steps:

Rhee et al. (2010) used LST, VI, and precipitation data from the TRMM satellite to construct a remote sensing scaled drought condition index (SDCI) and compared it with previous drought indices: Z index, PDSI, and standardized precipitation index (SPI). SDCI drought monitoring accuracy was superior to the previous indices, and monitoring results were consistent with the US drought monitor map. Data used in their study included monthly total precipitation and monthly mean temperature 1971e2009 obtained from the Applied Climate Information System for weather stations, MODIS 8-day LST (MOD11A2, collection v005), monthly VI (NDVI, MOD13A3, collection v005), 8-day surface reflectance data (MOD09A1, collection v005), and TRMM monthly rainfall 2000e09. They also used the National Drought Monitor (USDM) provided by the United States Drought and Disaster Reduction Center and historical crop yield statistics obtained from NASS

(1) MODIS surface reflectance data were employed to calculate normalized multiband drought index (NMDI) and normalized difference water index (NDWI). Then, the normalized difference drought index (NDDI) was calculated by using NDVI and NDWI, and VHI (Unganai and Kogan, 1998) was calculated using NDVI and LST. (2) Correlations were calculated for VI, LST, and precipitation data with 3 month SPI, and then three groups of weights were defined in terms of these correlations to construct the composite index (CI), as shown in Table 24.6. (3) Correlation coefficients were analyzed between the CI and common previous drought indices (Z index, PDSI, 3 and 6 month SPI) at meteorological sites, selecting the optimal drought index individually for the different situations, which was called SDCI. (4) SDCI monitoring performance for agricultural drought conditions was

890 TABLE 24.6

24. Remote sensing application in agriculture

Formulas for remote sensing variables. r represents the spectral reflectance.

Remote sensing variable

Formula

NDVI(500 m)

ðrband2  rband1 Þ=ðrband2 þ rband1 Þ

NMDI NDWI

ðrband2  ðrband6  rband7 ÞÞ=ðrband2 þ ðrband6  rband7 ÞÞ .   rband2 þ rband5ðor 6 or 7Þ rband2  rband5ðor 6 or 7Þ

NDDI

ðNDVI  NDWIÞ=ðNDVI þ NDWIÞ

Scaled LST

ðLSTmax  LSTÞ=ðLSTmax  LSTmin Þ

Scaled NDVI(¼VCI)

ðNDVI  NDVImin Þ=ðNDVImax  NDVImin Þ

Scaled NMDI

ðNMDImax  NMDIÞ=ðNMDImax  NMDImin Þ for the arid region ðNMDI  NMDImin Þ=ðNMDImax  NMDImin Þ for the humid region

Scaled NDWI

ðNDWI  NDWImin Þ=ðNDWImax  NDWImin Þ

Scaled NDDI

ðNDDImax  NDDIÞ=ðNDDImax  NDDImin Þ

Scaled TRMM

ðTRMM  TRMMmin Þ=ðTRMMmax  TRMMmin Þ

VHI

ð1 =2Þscaled LST þ ð1 =2Þscaled NDVI

CI1

ð1 =3Þscaled LST þ ð1 =3Þscaled TRMM þ ð1 =3Þscaled VI

CI2

ð1 =4Þscaled LST þ ð2 =4Þscaled TRMM þ ð1 =4Þscaled VI

CI3

ð2 =5Þscaled LST þ ð2 =5Þscaled TRMM þ ð1 =5Þscaled VI

The vegetation component shown as VI is one of NDVI, NMDI, NDWI, and NDDI, and the scaled TRMM uses one of the 1-, 3-, 6-, 9-, and 12month timescales (Rhee et al., 2010).

assessed by comparing year to year SDCI changes with those for VHI and Z-index. (5) The May 2000eMay 2009 SDCI map was compared with USDM maps of the arid zone in May, and the relationship between SDCI and crop yields analyzed to further verify SDCI for crop drought monitoring. The main study conclusions were as follows: (1) Correlation coefficients between the comprehensive drought index and the in situ drought indices show that LST, TRMM, and NDVI provided the best combination of CO2 weights in arid regions, whereas LST, TRMM, and NDDI6 worked best with CO2 weights in wet regions. (2) Comparing yearly changes of SDCI, VHI, and Z-index (Fig. 24.13) show that SDCI was

consistent with Z-index. The VHI trend was slightly different from those of SDCI and Z-index because it did not use direct precipitation information. (3) Comparing SDCI and USDM (Fig. 24.14) shows that for arid regions, droughts evident in 2000, 2002, 2006, 2007, and 2009 USDI maps were successfully monitored using SDCI, where extreme drought conditions in Carolina in 2008 (humid region) were not caught by SDCI. Overall, SDCI and USDM provided comparable monitoring. (4) Correlations between yearly crop yield and SDCI for each month from May to September show that for arid areas, only May SDCI and yearly cotton yield were significantly correlated. June and July SDCI had significant correlation with yearly corn

24.5 Crop residue monitoring

891

FIGURE 24.13 Year-to-year changes of VHI, SDCI (TRMM1 used), and Z-Index averaged over (A) the arid region and (B) the humid region (Rhee et al., 2010).

yield, and September SDCI had significant correlation with yearly soybean yield. TRMM precipitation timescale was shown to affect SDCI applicability. The authors concluded that integrating VI, surface temperature, and precipitation data

into a comprehensive drought index could better monitor agricultural drought, and monitoring accuracy would continue to improve increased use of remote sensing data.

892

24. Remote sensing application in agriculture

FIGURE 24.14 Year-to-year changes of SDCI (TRMM1 used) and the USDM maps in the humid region for September from 2000 to 2009. (A), (C), (E), (G), (I), (K), (M),(O), (Q), (S) refers to the SDCI of september from 2000 to 2009. (A), (C), (E), (G), (I), (K), (M), (O), (Q), (S) refers to the SDCI of september from 2000 to 2009.

24.5 Crop residue monitoring 24.5.1 Crop residue cover Crop residues are materials left on cultivated land after the crop has been harvested. Retention of crop residues after harvesting is considered to be an effective antierosion measure. Crop residues can improve soil structure, increase organic matter content in the soil, reduce evaporation, and help fix CO2 in the soil. Good residue management practices on agricultural lands have

many positive impacts on soil quality. Besides, crop residues can be used in biofuel production. Information on residue cover guides polices for promoting beneficial management practices and helps the estimation of soil carbon. Traditional methods of residue cover measurement, such as line-point transects or photographic techniques, are inefficient in large-scale investigations, and their accuracy is impacted by operator bias and sampling representatives. A satellite-based approach is an efficient and

24.5 Crop residue monitoring

less costly way to measure residue cover (Daughtry et al., 1996; Sullivan et al., 2004). Currently, both optical and microwave remote sensing have been used to estimate crop residues; however, both of these face some challenges. The biggest challenge to using optical remote sensing to detect crop residues is the ability to distinguish crop residues from bare soil. The spectra of crop residues and soils are often similar and differ only in amplitude for a certain wavelength. Moreover, the spectral reflectance of soils is affected by many factors, such as organic matter, moisture, texture, particle size distribution, iron oxide content, and surface roughness. The spectral reflectance of crop residues is affected by degree of decomposition, water content, and harvesting time. Therefore, there is no uniform relationship between crop residue spectra and soil spectra. In microwave images, the difference between soil and crop residues lies in different backscatter (McNairn and Brisco, 2004). However, radar backscatter is influenced by surface roughness, soil status, SM, and crop residue distributions (McNairn et al., 2002). So far, some indices have been developed for detecting crop residues, such as the brightness index, normalized difference index, normalized difference tillage index, normalized difference senescence index, normalized difference residue index, soil adjusted corn residue index, modified soil adjusted corn residue index, crop residue index multiband, cellulose absorption index (CAI), lignin cellulose absorption index, and shortwave infrared normalized difference residue index (Bannari et al., 2006; Biard and Baret, 1997). All of the above indices try to intensify the spectral difference between soil and crop residues and usually use visible (400e700 nm), NIR (700e1200 nm), and shortwave infrared (1200e2500 nm) bands (Fig. 24.15). Among these, CAI was reported to have the highest accuracy, followed by LCP (Serbin et al., 2009). This is because crop residues are primarily

893

composed of cellulose, hemicelluloses, and lignin and the absorption characteristics of crop residues are highly correlated to the absorption characteristics of cellulose and lignin. The CAI can capture the cellulose absorption at 2101 nm.   R2031 þ R2211 CAI ¼ 100  R2101 (24.12) 2 where R is the reflectance, and the subscripts 2031, 2101, and 2211 denote 11-nm-wide bands centered at 2031, 2101, and 2211 nm, respectively. Besides spectral indices, classification methods (such as linear spectral mixture analysis, LSMA) have also been used to measure crop residue by remote sensing in recent years (Bannari et al., 2006; Zhang et al., 2011a). For example, Pacheco and McNairn (2010) mapped crop residue on SPOT and Landsat images. They extracted end-members of three different residues (corn, soybean, and grain) and two soil types (clay and loam) directly from the image based on the ground data and then used the LSMA model in the PCI Geomatica software to derive the crop residue fractions. They compared the percentage residue cover estimated by SMA with that observed by ground measurements via the coefficient of determination (R2) and the root mean square error (RMSE). Fig. 24.16 shows one of their results. R2 for the estimated and measured crop residue was between 0.58 and 0.78, and the RMSEs were between 17.29% and 20.74%. Using radar images to estimate crop residues is usually conducted by building a linear relationship between crop residue coverage and backscattering coefficient (Zhang et al., 2011a). However, the simple linear relationship cannot accurately represent the complex relationship between crop residue and radar backscatter and consequently cannot produce ideal results. For example, McNairn et al. (2001) studied different crop type, residue moisture content, and residue amount using radar backscatter

894

24. Remote sensing application in agriculture

FIGURE 24.15 Visible, near-infrared (NIR), and shortwave-infrared reflectance spectra for two soils, two crop residues, and live corn canopy, with ranges of Landsat Thematic Mapper (TM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and cellulose absorption index (CAI) bands. Wheat residues were acquired from a strip-tilled field in June, w9 mo after harvest. Corn residues were acquired from a field of standing corn residue stubble in May, w8 mo after harvest. Corn canopy was at the silking stage (R1) (Serbin et al., 2009).

and found that radar backscatter increased with increasing amount and moisture content of residue. McNairn et al. (2002) examined the sensitivity of linear polarizations and polarimetric parameters of polarimetric synthetic aperture radars to crop residues and found that the polarimetric parameters vary depending on the type and amount of residue cover.

24.5.2 Crop residue burning Crop residue burning is a common type of land management in cultivated land. It can help control pests and weeds and remove extra residues before planting. It also emits substantial amounts of particulate matter and other pollutants into the atmosphere. The burned areas

24.5 Crop residue monitoring

895

FIGURE 24.16 Percent crop residue cover map over the Casselman/St. Isidore study site derived from spectral mixture analysis on a SPOT image acquired on November 9, 2007 (Pacheco and McNairn, 2010).

also affect the hydrological and ecological environments. Satellite remote sensing provides the only means of monitoring vegetation burning at regional to global scales and has been used to monitor fire globally for more than two decades. Currently, the NASA (MODIS) on the Terra and Aqua satellites has been used to systematically generate a suite of global MODIS fire products including the active fire product and burned area product (http://modis-fire.umd.edu/index.html). The algorithms for MODIS fire products have been continuously improved and can be found in references (Giglio et al., 2003; Roy et al., 2005, 2008).

The MODIS active fire product is derived by a contextual algorithm, in which thresholds are applied to the observed middle-infrared and thermal infrared brightness temperature and then false fire pixels are deleted by comparing the brightness temperature of the detected pixel with those of neighboring pixels (Giglio et al., 2003). Fig. 24.17 shows an example of the density of MODIS 1-km active fire detections per 10 km grid in the contiguous United States (CONUS). The MODIS burned area product is developed using a detection method in which the predicted bidirectional reflectance (BRF) in a pixel is estimated by a bidirectional reflectance model and compared with observed BRF. If the difference

896

24. Remote sensing application in agriculture

2001

Density of 1 km MODIS Fire Points per 10 km Grid

2002

0 - 300 detections 300 - 2,000 detections 2,000 - 4,000 detections 4,000 - 8,000 detections 8,000 - 16,000 detections

2003

2004

FIGURE 24.17 Density of MODIS 1 km Active Fire Detections (MOD14) per 10 km grid in the contiguous United States (2001e2004) (McCarty et al., 2007).

between predicted and observed BRF exceeds a certain threshold in a pixel, the pixel will be labeled as burned (Roy et al., 2005). The MODIS burned area product is distributed as a monthly gridded 500-m product in Hierarchical Data Format and sinusoidal equal area projection. The MODIS fire product focuses not only on agricultural land cover but is also used to monitor agricultural burning together with other data (McCarty et al., 2007, 2008, 2009; Zhang et al., 2011b). For example, McCarty et al. (2009) monitored crop residue burning in the CONUS using multiple MODIS products including the 500-m MODIS 8-day surface reflectance product (MOD09A1), the 1-km MODIS active fire product (TERRA/AQUA, MOD14/ MYD14), and the MODIS 1-km Land Cover

Dataset (MOD12) during growing seasons from 2003 to 2007. Fig. 24.18 shows their workflow for monitoring crop residue burning. Their method included two parts. The first part was to detect burned pixels using the Normalized Burn Ratio (dNBR). However, dNBR method is more suitable for burned areas bigger than 20 ha. For burned areas smaller than 20 ha, the 1-km MODIS active fire product was used; this is the second part of the workflow. Then, they combined the burning maps from these two parts to generate the final burning map. The dNBR method included the following steps. (1) They generated a dNBR map for each tile of the MODIS surface reflectance product covering the study area. The method for calculating the

24.6 The impact from cropland

897

FIGURE 24.18 Workflow for calculating the crop residue burned area for the CONUS (McCarty et al., 2009).

dNBR can be found in Lopez Garcia and Caselles (1991). (2) Corresponding to each MODIS tile, they built a crop mask using the MODIS 1-km Land Cover Dataset, used the crop mask to get rid of the noncrop area from the dNBR map, and finally produced a cropland dNBR map. (3) For each cropland dNBR map, they set a different burn threshold based on ground measurements, and pixels in the dNBR map with values greater than the burn threshold were recognized as burned pixels. The ground measurements included a total of 296 GPS data points that can well represent the difference in cropping systems, soil properties, irrigation activities, and residue burning frequencies of CONUS. According to their estimation, 1,239,000 ha of croplands are burned each year in the CONUS. Fig. 24.19 shows the annual burned area in CONUS and two magnified burned areas in California and Louisiana.

24.6 The impact from cropland 24.6.1 Irrigation impacts on land surface parameters Like land cover and land use change, land management also can have a big impact on the climate system, but it is not yet of too much concern. Irrigation is one of most important land management practices by which people try to grow crops in dry areas or increase food production. It is reported that agricultural irrigation accounts for 84% of global water use by the world’s population (Shiklomanov, 2000) and has grown rapidly over the past 200 years. The irrigated area was estimated to be about 8 Mha around 1800, 47 Mha around 1900 (Shiklomanov, 2000), and 274 Mha around 2000 (Siebert et al., 2005a). Considering the tremendous increase of

898

24. Remote sensing application in agriculture

FIGURE 24.19 Seasonal cropland burned area for subregions of California and Louisiana for years 2003e07; for mapping purposes, active fire detections were not calibrated into area for display purposes and remain as original point shapefiles but are symbolized as squares; seasons defined as winter: JanuaryeMarch; spring: AprileJune; summer: JulyeSeptember; and Fall: OctobereDecember (McCarty et al., 2009).

irrigated area and the potential impact of irrigation on the climate system, it may have contributed to the formation of current climate system and may impact our future climate system. So far, there have been some reports about the impact of irrigation on near-surface air temperature (Bonfils and Lobell, 2007; Kueppers et al., 2007; Lobell and Bonfils, 2008; Mahmood et al., 2006), energy fluxes (Devries, 1959; Douglas et al., 2006), groundwater (Kendy et al., 2004), water vapor (Boucher et al., 2004), and precipitation (Barnston and Schickedanz, 1984; Lee et al., 2009; Lohar and Pal, 1995; Moore and Rojstaczer, 2001; Segal et al., 1998) based on climate observations and modeling studies. Observational

studies usually make comparisons between pre- and postirrigation temperature trends in irrigated areas (Adegoke et al., 2003; Mahmood et al., 2004) or between irrigated and nonirrigated areas (Christy et al., 2006; Segal et al., 1998). Modeling studies usually compare the outputs from different models (regional or global, coupled or uncoupled) with and without irrigation, for example, fixing a high value of SM (Kanamaru and Kanamitsu, 2008; Lobell et al., 2006a), imposing a fixed amount of evapotranspiration (ET) from irrigated areas (Boucher et al., 2004; Segal et al., 1998), and designing an irrigation model based on the balance between water demand and supply (De Rosnay et al.,

24.6 The impact from cropland

2003; Haddeland et al., 2006) throughout the growing season. Both observational and modeling studies are facing some challenges (Bonfils and Lobell, 2007; Lobell and Bonfils, 2008). For example, meteorological observations essentially provide point measurements, which usually do not represent area means. It is difficult to clearly distinguish the impact of irrigation on climate from the impact of other factors as the characteristics of irrigated sites such as land cover type, altitude, latitude, and longitude, distance from urban/ocean areas, and black carbon concentration may vary considerably. The results from modeling rely heavily on input parameters associated with four key aspects of irrigation, namely, where to irrigate, when to irrigate, how much to irrigate, and how to irrigate (e.g., rain, spray, drip, and rate), causing over- or underestimation. Remote sensing observation is a promising tool and could provide land parameter information on a large scale, including SM, albedo, LST, vegetation cover, and so on. It could be a valid method for determining the impact of irrigation on the local surface climatedespecially in those regions where direct observations are limited or obscured by other factors, such as urbanization in China. It can also be integrated into models to better represent reality. However, the studies based on remote sensing observations are rare so far. Zhu et al. (2011) used satellite observations to analyze the impact of irrigation on land surface biogeophysical parameters in Jilin Province, China. The parameters used in their study included albedo, LST, NDVI, SM, and ET. The surface albedo dataset was from the (MODIS) albedo product (MCD43C3: Albedo 16-Day L3 Global 0.05Deg CMG). The LST dataset was from the MODIS/Aqua LST/Emissivity Monthly L3 Global 0.05Deg CMG (MYD11C3) products. The NDVI dataset was from the MODIS Vegetation Indices Monthly L3 Global 0.05Deg CMG (MOD13C2) product. SM data

899

were from the Advanced Microwave Scanning Radiometer-Earth Observing System L3 Surface Soil Moisture products provided by the National Snow and Ice Data Center (Njoku, 2005). ET was calculated using a statistical equation (Wang and Liang, 2008). To evaluate land surface parameters under different irrigation intensities, they compare land surface parameters between cultivated areas featuring a high percentage of irrigated land and those with a low irrigation percentage. Fig. 24.20 shows the comparison results. They found that highly irrigated areas always corresponded to a lower albedo and LST and higher SM, NDVI, and ET over the study period of 2000e08. Their study proved that satellite observations are sufficiently valid to determine the impact of irrigation on land surface parameters and provide another viable method for understanding the impact of irrigation on local climate, especially in those regions where direct observations are limited or obscured by other factors, such as urbanization in China.

24.6.2 Impacts of cropland on surface temperature The role of intensive agriculture in modifying surface climate has been documented by various studies based on both modeling and observational methods. LST is generally defined as the skin temperature of the ground. LST is a key parameter in the physics of land surface processes, combining surfaceeatmosphere interactions and energy fluxes between the atmosphere and the ground. Ge (2010) used MODIS LST to study the impacts of intensive agriculture on surface temperature. His study area was the winter wheat belt in the North American Great Plains. The data used in this study included MODIS land cover products (MOD12C1) with the International GeosphereBiosphere Programme (IGBP) classification scheme from 2001 (http://modis-land.gsfc. nasa.gov/landcover.htm) and monthly Terra

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FIGURE 24.20 Comparison of land surface parameters between highly and lowly irrigated areas. The pixels with an irrigation percentage smaller than 10 were classified as “reference” areas (RA). The pixels with an irrigation percentage greater than 30 and 50 were denoted as “target1” and “target2” areas, respectively. NIR-BB, SW-BB, and VIS-BB are black-sky near-infrared, shortwave, and visible band albedo, respectively. ET, evapotranspiration; LST, land surface temperature; SM, soil moisture. From Zhu, X., Liang, S., Pan, Y., Zhang, X., 2011. Agricultural irrigation impacts on land surface characteristics detected from satellite data products in Jilin Province, China. IEEE J. Select. Topics Appl. Earth Obser. Remote Sens. 4, 721e729, ©2011, IEEE.

and Aqua LST data (version 5) with 0.05 spatial resolution from August 2002 to July 2008 (https://wist.echo.nasa.gov/wist-bin/api/ims. cgi?mode¼MAINSRCH&JS¼1). His method involved two main steps. First, he identified the winter wheat and grassland pixels in his study area by using the MODIS land cover product (Figs. 24.21) and then he compared the daytime, nighttime, and diurnal LSTs of winter wheat with those of grassland from August 2002 to July 2008. Figs. 24.22 and 24.23 are parts of his results. Fig. 24.22 shows LST difference between wheat and grassland observed by MODIS Aqua. Fig. 24.23 is averaged LST diurnal difference between winter wheat and grassland. In term of his results, the author concluded that the wheat field has a cool anomaly in the

growing season and warm anomaly when bare soil is exposed and that the temperature over the wheat field is more uniform than that of surrounding areas. His study indicated the potential advantages of using satellite observations in landeclimate interaction research.

24.6.3 Impact of crop residue burning Burning crop residue can be an important source of particulate and trace gas emissions that affect both air quality and public health (Badarinath et al., 2006; Zhang et al., 2011b). In recent years, satellite-based approaches have been employed to study the impact of residue burning on near surface atmosphere and land surface characteristics (Badarinath et al., 2009;

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24.6 The impact from cropland

Serbin et al., 2009). For example, McCarty et al. (2009) studied the impact of burning crop residue using multiple satellite products in the Indo-Gangetic Plains (IGP), which is one of the world’s largest and most intensively cultivated areas. The data used in their study included MODIS (AOD) data (available at http://g0dup05u.ecs. nasa.gov/Giovanni), Indian remote sensing satellite (IRS)-P4 ocean color monitor and CO data from the troposphere instrument (available at http://eosweb.larc.nasa.gov/PRODOCS/ mopitt/table_mopitt. html), and aerosol index (AI) from the ozone monitoring instrument flown on the EOS Aura spacecraft and MODIS fire products (available at http://maps.geog. umd.edu/activefire_html/). These data were acquired in November 2007, which is a typical crop residue burning period in the IGP. Besides, a major Indian festival known as Diwali was celebrated on November 9, 2007.

FIGURE 24.21 Distribution of winter wheat and grassland in the study area (Ge, 2010).

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FIGURE 24.22 LST difference between wheat and grassland (LSTwheatLSTgrassland) from August 2002 to July 2008 observed by MODIS Aqua (MYD) (Ge, 2010).

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To estimate impacts of crop residue burning on AOD, AI, and CO in IGP, they compared the AOD, AI, and CO observed during the Diwali period with those observed after Diwali. Fig. 24.24 shows a comparison example. They also conducted back trajectory analysis via the HYSPLIT model. Their results indicated that crop residue burning and fireworks led to an increase of 30% in AOD at 550 nm and influenced the AI and CO over the Arabian Sea.

24.7 Response of crops to climate change Climate is the basic environment for human survival, and climate change has brought many adverse effects on the global environment and human life. Agriculture is very sensitive to climate change, and many previous studies have shown changes to agricultural planting

areas (Newman, 1980), planting systems, crop growth periods (Wang et al., 2004), crop photosynthesis efficiency, water use efficiency, crop yield (Parry and Swaminathan 1992; Terjung et al., 1989), etc., due to climate changes. This section introduces two case studies of crop response to climate change based on remote sensing.

24.7.1 Effects of extreme heat on wheat growth Wheat is one of the world’s major food crops and has the most extensive planting area globally. Depending on the different planting periods, it can be divided into winter and spring wheat. Ground temperature increases through the wheat growing season, reaching maximum during the grain filling stage. Temperature

24.7 Response of crops to climate change

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FIGURE 24.24 MODIS derived aerosol optical depth at 550 nm during (A) November 1e10, 2007, (B) November 11e20, 2007, and (C) November 21e30, 2007 (Badarinath et al., 2009).

during this latter period can affect grain filling speed, leaf senescence speed, and wheat yield (Wardlaw and Wrigley, 1994; Wardlaw and Moncur, 1995; Alkhatib and Paulsen, 1999). Lobell et al. (2012) used satellite data in Northern India to monitor wheat senescence rates at temperatures >34 C. The main experimental data and steps were as follows:

(1) VI products based on MODIS satellite data were employed to extract wheat green-up and senescence dates in IGP in India and estimate the green season length (GSL). (2) Long-term average monthly maximum and minimum temperatures from high resolution climatology maps from the WorldClim database (http://worldclim.org) and Global

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Summary of the Day data from the National Climate Data Center (http://www.ncdc. noaa.gov/cgi-bin/res40.pl?page¼gsod. html) were used to estimate daily minimum and maximum temperatures over 1 km grid cells and then calculate 2 days (DDs): one called GDD, where base temperature was 0 C and maximum 30 C and the other called EDD, where base temperature was 34 C. (3) Rainfall could be related to extreme heat, mitigating moisture stress and therefore delaying senescence. Therefore, gridded rainfall data was collected from NASA (http://power.larc.nasa.gov/). (4) Depending on the green-up period, they divided the study area into three groups and established regression equations for GDD, EDD, rainfall, and GSL for each group GSL ¼ b0 þ bG GDD þ bE EDD þ bR RAIN (24.13) (5) The established regression equations and two crop models (CERES-Wheat and APSIM) were used to simulate extreme heat effects on the crop yield and explore the impact of

climate change on crop growing season. To ensure that the selected sites covered the possible extreme heat range, sites were divided into three groups depending on planting date and selected corresponding to 5th, 50th, and 90th percentiles of EDD in each group for analysis. GDD and EDD were calculated after temperature rises (assumed to be 1, 2, 3, and 4 C), and GSL was estimated from the regression Eq. (24.13). Under conditions without nitrogen and water stress, CERES-Wheat and APSIM models were used to simulate temperature increase effects on growth season length and yield, respectively. (6) Regression model and crop model simulation estimates were compared. All three simulation models indicated that shortening of GSL in the study area was related to sowing date (Fig. 24.25A). The shortening length of the growing season simulated by CERES-Wheat and APSIM was smaller than that estimated from the MODIS regression model, especially for late seeding wheat. For example, when the temperature rises by 2 C and the sow date is November 25, the shortening

FIGURE 24.25 Comparison of simulation results based on MODIS data and crop-based models. (A) Shortening length of wheat growing season at different sowing dates and (B) loss of wheat yield at different sowing dates (Lobell et al., 2012).

24.7 Response of crops to climate change

lengths predicted by MODIS Regression, CERES-Wheat, and APSIM were 9, 6, and 3 d, respectively. They estimated MODIS yield loss using the CERES simulation relationship between GSL shortening and yield loss. Comparing CERES and APSIM, larger yield losses were predicted from MODIS regression (Fig. 24.25B). Both CERES and APSIM models may have underestimated yield loss caused by temperature increase; however, the outcomes verified that crop phenological information extracted based on satellite data can be used to evaluate model performance.

24.7.2 Effects of changes in humidity and temperature on crops Climate is the key factor for agricultural production, as soil temperature and moisture are the basis for crop growth. Climate changes, such as temperature increase, precipitation changes, and light condition changes, directly affect food yields. For example, temperature changes in temperate and tropical regions have significant effects on agricultural production (Piao et al., 2007). Water resource shortages lead to reduced crop yields from rainfed agriculture (Jeong et al., 2010). Brown et al. (2012) investigated crop phenology responses to climate change globally from 1981 to 2006. The data used in this study included AVHRR NDVI datasets from the NASA Global Inventory Monitoring and Modeling Systems (GIMMS) July 1981e December 2006 with 8 km resolution; 3-hourly gridded meteorological data with a 1 resolution from the Global Land Data Assimilation System, global crop distribution map with 500  500 (w10  10 km) latitude-longitude grid produced by Monfreda et al. (2008); rainfed cereal production (country level) 1982e2006 from United Nation Food and Agriculture Organization, and US wheat yields 1982e2006

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from NASS. The main processing included the following steps: (1) Data preprocessing: The GIMMS data were regridded from native Albers equal area 8 km resolution to 0.08 resolution. Average daily temperature was calculated by aggregating 3 h temperatures, subtracting base temperature (5 C) from average daily temperature to obtain GDD. Average daily temperature was not used for calculating GDD if it was <5 C. Finally, the global crop distribution map was regridded from 500 to 0.08 resolution and the rainfed crop distribution map extracted. (2) Model building and phenology prediction: The method proposed by White et al. (2009) was used to estimate growing season start and end dates and GSL based on NDVI data. A quadratic regression model (24.14) was fitted for growing season NDVI and used to estimate NDVI peak height and position. See Brown and de Beurs (2008) for further details regarding the quadratic regression model. NDVI ¼ a þ bx þ gx2

(24.14)

where x is either accumulated growing degree days (AGDD) or accumulated relative humidity (Arhum); a is NDVI at SOS, and b and g determine GSL. Peak position is estimated by model parameters. Peak NDVI is determined based on the peak position. They defined two 18-month crop growing periods: October to March (cycle 1) and April to September (cycle 2). AGDD and Arhum were calculated by summing daily mean GDD and relative humidity over the study periods. Two models were built for each cycle using AGDD and Arhum, respectively, i.e., four models in total, called AGDD-Cycle1, AGDDCycle2, Arthum-Cycle1, and Arthum-Cycle2. (3) Statistical analysis. Based on the phenology prediction above the last step, interannual

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variation and temporal trends were analyzed using phenology estimates for SOS, peak period, and GSL, as well as relationships between crop phenology variation and agricultural production 1982e2006. Peak period refers to the period from SOS to date when peak NDVI occurs. Fig. 24.26A shows SOS trends 1982e2006, where positive indicates later and negative indicates earlier SOS. West African Sahel, Southeast Asia, and North America have been

(A)

experiencing later SOS, and Europe generally shows earlier SOS. Fig. 24.26B shows GSL trends 1982e2006, where positive indicates longer and negative indicates shorter GSL. Approximately 27% of cereal crop areas have experienced longer GSL 1982, with average lengthening 2.3 days. Fig. 24.27 shows peak growing period trends, where positive indicates later and negative indicates earlier peak timing. More areas have experienced later peak timing estimated from Arhum than estimated from AGDD. Arhum results

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FIGURE 24.26 Significant trends of (A) start of season and (B) length of growing season over the 26 years in cropping regions, given by the regression coefficient of the parameter versus time. Significance is measured by P value of less than 0.1 (Brown et al., 2012).

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24.8 Summary

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FIGURE 24.27 Significant trends of peak period for (A) relative humidity model and (B) accumulated growing degree days for 26 years in cropping regions, given by the regression coefficient of the parameter versus time. Significance is measured by P value of less than 0.1 (Brown et al., 2012).

show that Eastern Europe and Eastern United States have significantly later peak timing, whereas dry regions of south Asia, India, and arid southwest in North and South America have significantly earlier peak timing. AGDD results show peak timing has a large positive trend across Asia. Fig. 24.28 shows the correlations between agricultural production and peak growing season positions estimated from AGDD-Cycle1, AGDD-Cycle2, Arthum-Cycle1, and ArthumCycle2. More than 25% of the pixels showed significant correlation between agricultural production and peak growing season position variance in 75 countries. Correlations between

agricultural production and peak NDVI estimated from AGDD-Cycle1 and Arthum-Cycle1 are stronger than for AGDD-Cycle2 and Arthum-Cycle2.

24.8 Summary This chapter focuses on remote sensing data and products in the field information extraction, crop yield estimation, drought monitoring, land use management of farmland on the impact of surface parameters and climate change on crop growth. However, remote sensing research in agriculture is in-depth and extensive, and

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FIGURE 24.28 (A) Overview map showing which model results in phenological metrics that best correlates with production statistics. For each cropland pixel in each country/state or region, the model type that reveals the most correlated pixels with production is shown. Countries that were part of the former Soviet Union and countries that were part of former Yugoslavia are omitted. Countries that do not reveal a significant model are omitted as well. (B) Same as above but only the countries for which at least 25% of the pixels show a significant behavior are show (Brown et al., 2012).

gradually toward application, such as precision agriculture, agricultural insurance monitoring and evaluation, agricultural engineering monitoring, agricultural policy effect evaluation, etc. On the other hand, the emerging new sensors (hyperspectral remote sensing data, fluorescence remote sensing, polarization remote sensing, and UAV remote sensing) provide more abundant data resources for agricultural research and application. The newly proposed and developed technologies (artificial intelligence, deep

learning, and large data) provide a technical approach for agricultural remote sensing information extraction and information inversion. The new application demand is bound to promote the further development of remote sensing technology and products, and the further development of agricultural remote sensing technology will also promote the intelligence and automation of modern agricultural development.

References

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Further reading Hazaymeh, K., Hassan, Q.K., 2016. Remote sensing of agricultural drought monitoring: a state of art review. Aims Environ. Sci. 3, 604e630. Khanal, S., Fulton, J., Shearer, S., 2017. An overview of current and potential applications of thermal remote sensing in precision agriculture. Comput. Electron. Agric. 139, 22e32. Liu, W.T., Kogan, F., 2002. Monitoring Brazilian soybean production using NOAA/AVHRR based vegetation condition indices. Int. J. Remote Sens. 23 (6), 1161e1179. Mahlein, A.K., Oerke, E.C., Steiner, U., Dehne, H.W., 2012. Recent advances in sensing plant diseases for precision crop protection. Eur. J. Plant Pathol. 133, 197e209.